Germane Cognitive Load and Programming Self-Efficacy as Mediators of Teacher Effects on Student Engagement in Vocational Programming Education

Germane Cognitive Load and Programming Self-Efficacy as Mediators of Teacher Effects on Student Engagement in Vocational Programming Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Germane Cognitive Load and Programming Self-Efficacy as Mediators of Teacher Effects on Student Engagement in Vocational Programming Education Xinning Wu, Yi Wang, Haifeng Zheng, Jingchao Liu, Yingxi Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8966481/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This study integrates social support theory, cognitive load theory, and social cognitive theory to address a persistent pedagogical challenge: bridging the gap between programming’s high abstraction and logical rigor and vocational students’ often weaker foundational skills, which typically leads to low engagement. Using structural equation modeling and multi-group analysis of survey data from 310 secondary vocational programming students, our findings revealed that perceived teacher support significantly and positively predicted programming learning engagement ( β = 0.105, p = 0.005); computer programming self-efficacy served as a significant single mediator ( β = 0.085); germane load and self-efficacy sequentially mediated the effect of teacher support ( β = 0.042); gender moderation effects were significant, with direct and serial mediation effects pronounced in the male group, whereas for female students, the influence operated indirectly through reduced extraneous load, with germane load demonstrating a significant negative effect ( β = −0.135, p = 0.026). Taken together, these findings clarify the core mechanism of “environmental support — cognitive processing — motivational beliefs — learning behaviors”. The study provides an evidence-based framework for designing gender-differentiated instructional strategies, advancing both pedagogical effectiveness and equity in vocational STEM education. Perceived teacher support Programming learning engagement Cognitive load Computer programming self-efficacy Serial mediation Secondary vocational education Figures Figure 1 Figure 2 1 Introduction The transformation of programming from a specialized skill to a foundational element of digital literacy reflects the profound impact of digital technologies on global socioeconomic structures. The cultivation of creative thinking, logical reasoning, and skills relevant to future employment is imperative. On this issue, the international community has reached a definitive consensus. According to the EU’s Digital Education Action Plan (2021–2027), computational thinking and programming are considered essential skills for all students. UNESCO’s Youth Coding Initiative exemplifies a proactive approach to the global dissemination of programming and artificial intelligence skills. Education systems worldwide are thus pressured to adapt, with vocational education facing acute demands due to its direct alignment with labor market needs. However, the core challenge in teaching programming lies in its high level of abstraction, rigorous logic, and strong practicality, which can easily lead to issues such as low learning motivation and insufficient engagement among novices (Bennedsen & Caspersen, 2019 ; Margulieux et al., 2020 ). This challenge is especially pronounced in secondary vocational education, which emphasizes practical skill development and direct alignment with industry demands. Secondary vocational students typically manifest uneven prior knowledge, fragmented academic foundations, and weak abstract reasoning capacities, generating heightened dependency on external teaching support. This vulnerability makes them particularly susceptible to experiencing excessive extraneous load from suboptimal instructional design, thereby intensifying learning barriers. In this context, the systematic exploration of effective teaching support mechanisms has emerged as a critical issue for enhancing the quality of secondary vocational programming education, with an emphasis on understanding the underlying logic by which perceived teacher support enhances student learning engagement through cognitive regulation and psychological construction pathways. Existing research has yet to adequately address this issue, due to several limitations: First, research has primarily focused on examining pairwise relationships between variables such as perceived teacher support and learning outcomes. However, there is a paucity of research that integrates key variables like perceived teacher support, cognitive load, and self-efficacy into a unified framework to reveal their synergistic mechanisms. Second, there is insufficient empirical evidence to support the complete sequential psychological pathway that external teacher support shapes self-efficacy through cognitive load regulation, thereby driving learning engagement. Third, extant research samples are predominantly drawn from traditional higher education settings, thereby overlooking the unique learning characteristics and potentially distinct psychological mechanisms of vocational education students. Fourth, as gender structures evolve within the context of vocational education, the role of gender as a significant moderating variable in programming learning processes has yet to be thoroughly examined. To address these gaps, the present study synthesizes social support theory (Malecki & Demaray, 2003 ), cognitive load theory (Sweller, 1988 ), and social cognitive theory (Bandura, 1997 ) within an integrated conceptual framework. We construct and empirically validate a serial mediation model to systematically investigate the underlying mechanisms linking perceived teacher support (PTS) to programming learning engagement (PLE) among secondary vocational students, with particular emphasis on testing the sequential mediating effects of extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE), alongside the moderating role of gender. This study employs structural equation modeling and multi-group analysis on survey data from 310 secondary vocational programming learners to investigate the direct and indirect roles of perceived teacher support in learning engagement, test a serial mediation model via extraneous load, germane load, and programming self-efficacy, and ultimately uncover the differential moderating role of gender across these pathways. The present study integrates a variety of theoretical perspectives to construct a chained mediation model, empirically revealing the complete sequential mechanism of “environmental support — cognitive processing — motivational beliefs — learning behaviors” in vocational education programming contexts. It overcomes the limitations of isolated variable research and deepens the integrated application of social support theory and cognitive load theory in vocational education contexts. Concurrently, this study focuses on secondary vocational students, a relatively neglected group in existing research. By clarifying the distinctiveness of their learning psychological mechanisms, it enhances the explanatory power and applicability of the theory within the vocational education context. Furthermore, this study provides educators with targeted intervention strategies, including the optimization of targeted instructional support to reduce EL, optimize GL, and strengthen CPSE by clarifying the gender-modulating effects and differentiated pathways of action. These strategies enhance the quality and efficiency of programming instruction while promoting educational equity. 2 Literature review 2.1 Perceived teacher support Teacher support is defined as the supportive behaviors exhibited by teachers that students receive during learning or daily life (Skinner & Belmont, 1993 ). Consistent with social support theory principles, behaviors conducive to well-being that individuals perceive from their social networks have been shown to produce universal benefits and positively influence individuals’ mental health and development (Berkman & Syme, 1979 ). The evolution of Self-Determination Theory has expanded its definition, leading to its widespread recognition as a critical environmental resource for fulfilling students’ three fundamental psychological needs: autonomy, competence, and relatedness. By fulfilling these basic psychological needs (Miller et al., 1988 ), supportive contexts enhance intrinsic motivation, facilitate extrinsic motivation internalization, and sustain deep engagement. In the field of research on teacher support, scholars have developed distinct research frameworks based on varying theoretical perspectives. From the functionalist perspective of social support theory, Malecki & Demaray ( 2003 ) parsed teacher support into four constituent dimensions: emotional, informational, appraisal, and instrumental support. This functionalist typology transcends specific educational contexts, deriving from universal interpersonal support functions to systematically catalog concrete forms of instructional assistance, thereby emphasizing a behavior-centric classification of supportive behaviors. Grounded in Self-Determination Theory’s psychological needs orientation, Brewster & Bowen ( 2004 ) delineated an alternative three-dimensional framework encompassing emotional, behavioral, and autonomy support. This needs-based conceptualization directly maps external instructional behaviors to students’ intrinsic psychological needs, providing a coherent theoretical framework for explaining the mechanisms through which teacher support influences motivation, learning engagement, and psychological well-being. Alternatively, Patrick et al. ( 2007 ) conceptualized teacher support as a holistic perceptual variable, examining it alongside other socio-contextual classroom dimensions. This integrative approach foregrounds two core dimensions: emotional and academic support. It primarily assesses whether students perceive instructors as dependable sources of support. Given the methodological constraints of directly assessing behaviors comprising both explicit and implicit dimensions, perceived teacher support (PTS), operationalized as students’ subjective evaluations of instructional assistance, has become the predominant measurement approach and research focus. Contemporary scholarship predominantly examines relationships between PTS and critical educational outcomes, including learning engagement, academic motivation, academic adjustment, and emotional development. Existing research consistently confirms moderate to strong positive correlations between PTS and multiple dimensions of learning engagement (Roorda et al., 2017 ; Liu et al., 2018 ; Yang et al., 2021 ; Tao et al., 2022 ), demonstrating robust cross-contextual generalizability across educational levels, disciplines, and cultural settings. In physical education contexts, instructor-provided autonomy, competence, and relatedness support significantly enhance students’ behavioral and emotional engagement (Guo et al., 2023 ). PTS exerts both direct effects on learning engagement and indirect influences mediated through achievement emotions (Strati et al., 2017 ; Wu & Kang, 2023 ). At vocational and higher education levels, the predictive power of teacher support for learning engagement becomes particularly pronounced when students confront academic challenges (Xu et al., 2023 ; Zhou & Wu, 2023 ). Notably, the facilitative effects of teacher support on engagement rarely operate through single direct pathways; rather, they propagate through multiple indirect routes involving learning motivation, positive emotions, and self-efficacy. This multifaceted mediation pattern underscores the necessity of examining the underlying mechanisms, thereby providing critical directions for subsequent research. 2.2 Programming learning engagement Learning engagement denotes the intensity of students’ active involvement in academic tasks or learning activities (Fredricks et al., 2004 ). Scholarly consensus recognizes a tripartite structure encompassing emotional, cognitive, and behavioral dimensions. Specifically, emotional engagement encompasses students’ affective reactions during learning processes. Cognitive engagement refers to the mental effort and intellectual activities learners invest in learning. Behavioral engagement manifests as the quantity and quality of students’ active participation (Hiver et al., 2024 ). Building upon this classical framework, Reeve & Tseng ( 2011 ) augmented the model by introducing agentic engagement as a fourth core dimension. This construct is defined as “students’ constructive contributions toward the instructional process they receive,” emphasizing learners’ transcendence beyond passive participation or reactive responding to actively influence, enrich, and regulate their own learning processes and instructional environments through personal preferences, interests, and needs. Manifestations include proactively asking instructors questions, expressing learning preferences, offering suggestions for curricular improvement, and independently seeking supplementary learning resources. Research on programming learning engagement typically adapts these general theoretical frameworks to the distinctive demands of programming education, focusing on addressing pedagogical challenges arising from knowledge abstraction, procedural complexity, and high practical demands while exploring pathways to enhance student engagement through innovative instructional models, pedagogical strategies, and technological tools. Within programming learning contexts, each dimension assumes distinct disciplinary characteristics. Agentic engagement manifests through proactive inquiry, articulation of learning preferences, contribution to curricular improvements, and autonomous pursuit of personalized learning pathways. This dimension centers on the learner’s transition from passive task reception to active co-designer of their programming learning experiences. Behavioral engagement is evidenced by sustained concentration during coding, persistent practice, tenacity when confronting complex problems, and active participation in collaborative activities, highlighting the time investment, effort allocation, perseverance, and adherence to task requirements that learners exhibit. Emotional engagement encompasses interest in and curiosity about programming concepts, enjoyment during exploration, and appreciation of programming’s utility, fundamentally reflecting learners’ subjective affective experiences. Cognitive engagement is characterized by deep elaboration and integration of programming knowledge, strategic approaches to coding practice, and the depth of cognitive processing and strategic deployment during problem-solving, examining whether learners construct knowledge through active mental elaboration. 2.3 Research questions Existing research has identified associations among students’ perceived teacher support (PTS), cognitive load (CL), self-efficacy, and learning engagement (Yu, 2019 ; Hu et al., 2024 ). However, whether extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE) act as sequential mediators in the relationship between PTS and programming learning engagement (PLE) remains insufficiently examined. Furthermore, the potential moderating role of gender in this process within vocational education is unclear. To address these gaps and provide targeted insights, this study is guided by the following research questions: RQ1: Does perceived teacher support (PTS) have a direct effect on programming learning engagement (PLE) among secondary vocational students? RQ2: Is the effect of PTS on PLE sequentially mediated by extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE)? RQ3: Does student gender moderate the direct and/or mediated pathways in the proposed model? 2.4 Hypothesis model 2.4.1 PTS, CL, and CPSE in programming learning Perceived teacher support (PTS) is defined as students’ subjective evaluations of teachers’ emotional care, cognitive guidance, and autonomy encouragement within learning environments. In the context of programming education, which is characterized by numerous challenges, these functions become particularly critical and multifaceted. On the one hand, the abstract nature and logical rigor of programming knowledge can generate frustration among learners. Almdahem ( 2023 ) indicated that heightened perceptions of instructional support are associated with reduced experienced difficulties during learning. Emotional support directly alleviates learning anxiety, while clear cognitive guidance mitigates task-induced cognitive load. On the other hand, PTS significantly and positively predicts students’ attitudes toward programming and their flow experiences during learning (Kong et al., 2025 ), rendering learners more inclined to perceive programming as an interesting and worthwhile challenge. Furthermore, Zheng et al. ( 2025 ) found that introducing intelligent agents with personalized emotional feedback and support capabilities (an extension of teacher support) into elementary programming classrooms substantially enhances student concentration and participation, corroborating the significance of emotional and cognitive support for sustaining learning behaviors. Hence, we hypothesize: H1 Perceived teacher support exerts a significant positive effect on students’ programming learning engagement. According to cognitive load theory, cognitive demands during learning can be categorized into three distinct types: intrinsic load (IL), extraneous load (EL), and germane load (GL). IL is derived from the interaction between two factors: element interactivity and learners’ prior knowledge levels. The former constitutes an immutable task attribute, while the latter is a stable individual difference. Neither of these factors is directly manipulable by teachers. Consequently, the present study excludes IL from consideration. Excessive EL has been shown to deplete limited working memory capacity, trigger frustration, and divert attention resources, thereby significantly undermining learning engagement (Xiang, 2023 ). Teachers can effectively reduce EL arising from poor instructional design by providing clear task instructions, well-structured materials, and timely technical support, thereby enabling students to focus cognitive resources on core learning activities (Li, 2019 ). Concurrently, moderate GL has been shown to correlate with deep learning and higher-order thinking development, thereby promoting cognitive engagement (Ayvaz-Tuncel & Demir, 2024 ). Teachers assist students in managing intrinsic task difficulty and direct cognitive effort toward knowledge construction, thereby promoting deep engagement rather than surface-level coping through instructional scaffolding such as decomposing complex tasks, providing worked examples, and designing heuristic questions (Li, 2019 ). Furthermore, when confronted with elevated cognitive demands, teachers’ emotional and cognitive support will enhance students’ confidence and efficacy beliefs in meeting challenges, fostering persistence rather than abandonment (Dong et al., 2020 ). Thus, we propose: H2 Perceived teacher support exerts a significant negative effect on extraneous load. H3 Perceived teacher support exerts a significant positive effect on germane load. H4 Extraneous load exerts a significant negative effect on programming learning engagement. H5 Germane load exerts a significant positive effect on programming learning engagement. The more pivotal function of perceived teacher support (PTS) lies in its indirect influence on deep learning outcomes through shaping students’ intrinsic psychological mechanisms. When students perceive trust and support from their teachers, they are more inclined to develop positive beliefs in their own capabilities. Students with elevated self-efficacy tend to demonstrate greater persistence, more positive emotional states, and more sophisticated cognitive strategy use when confronted with learning challenges. This suggests a willingness to invest more time and effort in the learning process, while also deriving enjoyment from learning (Zhou & Wu, 2023 ). Multiple studies across educational contexts have corroborated that PTS significantly and positively predicts academic self-efficacy (Yu, 2019 ; Jia et al., 2020 ; Hu et al., 2024 ). This teacher-support-activated self-efficacy has been shown to directly translate into more sustained and profound learning behaviors and emotional engagement. When conceptualizing self-efficacy within the specific domain of computer programming self-efficacy (CPSE), the relationship among teacher support, self-efficacy, and learning engagement has been empirically validated in information technology and programming learning contexts. Liu et al. ( 2023 ) investigated the impact of teacher support on students’ self-efficacy in high school information technology courses. Their findings indicated that teacher support significantly positively influenced students’ self-efficacy ( β = 0.719). Computer self-efficacy was also found to have a substantial impact on learning engagement in information technology courses ( β = 0.709). Within the total effect of “teacher support → learning engagement,” the mediating effect of computer programming self-efficacy accounts for 65.72% of the variance. These findings indicate that although teacher support directly facilitates learning engagement, its core function operates through enhancing students’ programming confidence to indirectly catalyze sustained, deep learning behaviors. In the context of programming education, teacher support functions as a critical external resource, initiating virtuous cycles. Computer programming self-efficacy serves as the central psychological mediating mechanism, and learning engagement represents the ultimate outcome most directly influenced by students’ programming confidence. Accordingly, we hypothesize: H6 Perceived teacher support exerts a significant positive effect on computer programming self-efficacy. H7 Computer programming self-efficacy exerts a significant positive effect on programming learning engagement. When task-induced cognitive load, particularly extraneous load, chronically exceeds individual processing capacities, learners readily experience frustration and resource depletion. These negative experiences subsequently undermine self-efficacy beliefs, creating a vicious cycle (Tingting et al., 2024 ). Conversely, when learners invest germane load and successfully construct knowledge schemata and master relevant skills, the resulting “mastery experiences” constitute the most potent source of self-efficacy enhancement (Hartelt & Martens, 2024 ). Such positive experiences serve to reinforce the notion that individuals possess the capacity to achieve their goals. Therefore, we propose: H8 Extraneous load exerts a significant negative effect on computer programming self-efficacy. H9 Germane load exerts a significant positive effect on computer programming self-efficacy. 2.4.2 The mediating effects of CL and CPSE Teachers can directly regulate students’ cognitive load levels by providing effective instructional support and optimizing instructional design (Wang & Cao, 2013 ). Specifically, the objectives of teacher support typically encompass reducing extraneous load (EL) while deliberately increasing germane load (GL). Existing research has confirmed that EL exerts direct negative effects on learning engagement, whereas GL demonstrates significant positive correlations with learning outcomes. For instance, Xiang ( 2023 ) found in a study of adaptive learning environments that EL significantly and negatively influenced junior high school students’ learning engagement. Similarly, Wu ( 2011 ) demonstrated in multimedia learning research that enhanced GL substantially improved learners’ learning effectiveness. Consequently, we hypothesize: H10 Extraneous load mediates the relationship between perceived teacher support and programming learning engagement. H11 Germane load mediates the relationship between perceived teacher support and programming learning engagement. Empirical studies have confirmed the mediating role of self-efficacy in the relationship between teacher support and learning engagement (Wang et al., 2017 ; Feng et al., 2023 ; He et al., 2024 ; Alrashidi & Alshammari, 2025 ). Concurrently, social cognitive theory posits that environmental factors shape behavioral manifestations through influencing individual cognitive factors. Both specific empirical investigations and overarching theoretical frameworks provide robust support for the mediating function of self-efficacy. Hence, we hypothesize: H12 Computer programming self-efficacy mediates the relationship between perceived teacher support and programming learning engagement. Teacher support, particularly autonomy support and clear structured guidance, effectively regulates students’ extraneous load through instructional scaffolding and minimizing irrelevant information interference, thereby optimizing cognitive resource allocation (Evans et al., 2024 ). Zhang ( 2024 ) explicitly examined the mediating role of cognitive load between self-efficacy and English learning motivation, confirming the significant influence of cognitive load on self-efficacy. Additionally, extensive research demonstrates that self-efficacy constitutes a critical antecedent of learning engagement. Students with elevated self-efficacy demonstrate greater willingness to exert effort, persist longer, and engage more deeply across cognitive, affective, and behavioral dimensions (Martin & Rimm-Kaufman, 2015 ; Kim et al., 2018 ; Liu et al., 2018 ). Accordingly, we propose: H13 Extraneous load (negative) and computer programming self-efficacy serially mediate the relationship between perceived teacher support and programming learning engagement. H14 Germane load and computer programming self-efficacy serially mediate the relationship between perceived teacher support and programming learning engagement. 2.5 Research model Based on the aforementioned hypotheses, the structural model of the present study is illustrated in Fig. 1 . 3 Methods 3.1 Participants This study employed cluster random sampling, recruiting students from a secondary vocational school in City Y, Guangdong Province, China. A total of 367 questionnaires were distributed. After implementing rigorous screening procedures that excluded invalid responses with completion times under three minutes or containing eight consecutive identical answers, 310 valid questionnaires were retained, yielding an effective response rate of 84.47%. The demographic characteristics of the sample were as follows: 150 male participants (48.39%) and 160 female participants (51.61%); 93 students with urban household registration (30.00%) and 217 students with rural household registration (70.00%). 3.2 Instrument development 3.2.1 Perceived teacher support scale The measurement of perceived teacher support was assessed using the Teacher Support Scale revised by Patrick et al. ( 2007 ). This eight-item instrument comprises two dimensions: teacher emotional support (TES) and teacher academic support (TAS). Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating stronger perceived teacher support. In this study, Cronbach’s α coefficients for the total scale and the two subscales were 0.880, 0.792, and 0.827, respectively, indicating satisfactory internal consistency reliability. 3.2.2 Cognitive load scale The measurement of cognitive load was measured using the Cognitive Load Scale revised by Leppink et al. ( 2013 ). This nine-item instrument comprises three distinct dimensions: intrinsic, extraneous, and germane load, with three items assigned to each dimension. Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating elevated levels of the corresponding cognitive load type. In this study, Cronbach’s α coefficients for the intrinsic, extraneous, and germane load subscales were 0.900, 0.825, and 0.875, respectively, meeting acceptable reliability standards. Notably, while intrinsic load (IL) was not incorporated into the statistical analyses of variable relationships, its inclusion in the questionnaire design and administration was imperative to ensure participants’ accurate comprehension of the measurement construct. This comprehensive approach enabled respondents to clearly distinguish among and appropriately complete items related to intrinsic, extraneous, and germane load dimensions. Concurrently, measuring IL provided auxiliary insights into students’ cognitive burden when engaging with programming knowledge and tasks, thereby providing reference points for subsequent instructional optimization. 3.2.3 Computer programming self-efficacy scale The measurement of computer programming self-efficacy was assessed using the Computer Programming Self-Efficacy Scale revised by Tsai et al. ( 2019 ). This twelve-item instrument comprises four dimensions: logical thinking (LT), algorithm (AL), control (CO), and debug (DE). Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating stronger computer programming self-efficacy. In this study, Cronbach’s α coefficients for the total scale and the four subscales were 0.903, 0.844, 0.879, 0.860, and 0.810, respectively, meeting acceptable psychometric standards. 3.2.4 Programming learning engagement scale The measurement of programming learning engagement was measured using the Student Engagement Scale revised by Reeve & Tseng ( 2011 ). This twelve-item instrument comprises four dimensions: agentic engagement (AE), behavioral engagement (BE), emotional engagement (EE), and cognitive engagement (CE). Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater programming learning engagement. In this study, Cronbach’s α coefficients for the total scale and the four subscales were 0.892, 0.853, 0.813, 0.775, and 0.807, respectively, meeting acceptable standards for empirical research. 3.3 Data analysis The questionnaire survey was administered with informed consent from both school teachers and students, conducted collectively by class. During survey administration, students were required to complete it independently within a 20-minute time limit, with the option to withdraw at any point. Questionnaires were collected immediately upon completion. Following the collection of data, the statistical software SPSS 26.0 and AMOS 24.0 were employed for the analysis. 4 Results 4.1 Descriptive statistics Descriptive statistics for the primary variables revealed secondary vocational students’ experiential patterns within perceived teacher-supported programming learning environments. The mean for perceived teacher support (PTS) was 4.08 ( SD = 0.60), indicating moderately high levels of perceived instructional support. Intrinsic load (IL) yielded a mean of 3.58 ( SD = 0.80), suggesting students experienced moderate yet manageable cognitive burden when comprehending programming knowledge and task requirements. Extraneous load (EL) averaged 2.73 ( SD = 0.75), indicating relatively low unnecessary cognitive demands imposed by learning materials or instructional design, thereby reflecting clear and comprehensible content organization and presentation in current programming instruction. Germane load (GL) registered a mean of 3.22 ( SD = 0.73), indicating students invested moderate cognitive effort in integrating novel programming knowledge with existing cognitive structures, facilitating deep processing and knowledge transfer. Computer programming self-efficacy (CPSE) averaged 3.53 ( SD = 0.60), reflecting moderate confidence among secondary vocational students in their capability to complete programming tasks, alongside foundational motivation and psychological readiness for programming learning. Programming learning engagement (PLE) demonstrated a mean of 3.53 ( SD = 0.55), underscoring students’ heightened concentration and participation willingness, with demonstrated willingness to invest time and effort in learning activities. Pearson correlation coefficients were computed to assess relationships among key variables (see Table 1 ). PTS demonstrated a significant negative correlation with EL ( r = -0.238, p < 0.01), and significant positive correlations with GL ( r = 0.341, p < 0.01), CPSE ( r = 0.467, p < 0.01), and PLE ( r = 0.487, p < 0.01). EL exhibited significant negative correlations with CPSE ( r = -0.287, p < 0.01) and PLE ( r = -0.230, p < 0.01). GL showed significant positive correlations with CPSE ( r = 0.635, p < 0.01) and PLE ( r = 0.576, p < 0.01). CPSE and PLE were significantly and positively correlated ( r = 0.649, p < 0.01). These correlation patterns aligned with theoretical expectations, establishing a foundation for subsequent mediation and moderation analyses. Table 1 Descriptive statistics and correlation analysis of each variable (n = 310) Variable M ± SD 1 2 3 4 5 6 1.PTS 4.084 ± 0.599 1 2.IL 3.58 ± 0.80 -0.032 1 3.EL 2.732 ± 0.747 -0.238** 0.551** 1 4.GL 3.225 ± 0.727 0.341** -0.436** -0.328** 1 5.CPSE 3.531 ± 0.595 0.467** -0.330** -0.287** 0.635** 1 6.PLE 3.526 ± 0.550 0.487** -0.203** -0.230** 0.576** 0.649** 1 Note: ** indicates statistical significance at the 0.01 level (two-tailed). 4.2 Common method bias test This study employed Harman’s single-factor test to assess common method bias. Exploratory factor analysis of all items yielded eight factors with eigenvalues exceeding 1. The largest factor accounted for 31.665% of the variance, below the 50% threshold (Podsakoff et al., 2003 ), indicating that common method bias was not a significant concern in this data. 4.3 Confirmatory factor analysis Confirmatory factor analysis was conducted to assess the discriminant validity of variables, including perceived teacher support (PTS), extraneous load (EL), germane load (GL), computer programming self-efficacy (CPSE), and programming learning engagement (PLE) (see Table 2 ). The results indicated that the baseline model (five-factor model) demonstrated optimal fit, with all fit indices falling within acceptable ranges, suggesting satisfactory discriminant validity among the measurement instruments employed in this study. In contrast, the four-factor, three-factor, two-factor, and one-factor models exhibited substantially inferior data fit compared to the baseline model, further corroborating that common method bias was not a significant threat in the present research. Table 2 Confirmatory factor analysis results Fit Indices χ 2 df χ 2 /df CFI TLI RMSEA SRMR Baseline Model (Five-factor): PTS, EL, GL, CPSE, PLE 264.240 91 2.904 0.937 0.917 0.078 0.054 Four-factor Model: PTS, EL + GL, CPSE, PLE 641.259 95 6.750 0.801 0.749 0.136 0.094 Three-factor Model: PTS, EL + GL+CPSE, PLE 792.390 98 8.086 0.747 0.691 0.151 0.104 Two-factor Model: PTS, EL + GL+CPSE + PLE 989.578 100 9.896 0.677 0.612 0.170 0.113 One-factor Model: PTS + EL+GL+CPSE + PLE 1165.637 104 11.208 0.614 0.555 0.182 0.118 4.4 Reliability and validity test The structural integrity of constructs in the research model was rigorously evaluated through reliability and validity testing, with the results presented in Table 3 . The internal consistency of the subdimensions/items for each construct was assessed using Cronbach’s alpha (α) and Composite Reliability (CR). For the construct of perceived teacher support (PTS), α and CR were determined to be 0.880 and 0.834, respectively. For the construct of extraneous load (EL), α and CR were determined to be 0.825 and 0.840, respectively. For the construct of germane load (GL), α and CR were determined to be 0.875 and 0.876, respectively. For the construct of computer programming self-efficacy (CPSE), α and CR were determined to be 0.903 and 0.802, respectively. For the construct of programming learning engagement (PLE), α and CR were determined to be 0.892 and 0.819, respectively. All Cronbach’s α coefficients and CR values exceeded the acceptable threshold of 0.7, indicating satisfactory internal consistency among subdimensions/items for each construct and confirming that measurement reliability met empirical research standards. Convergent validity was assessed through Standardized Loading (Std.), Average Variance Extracted (AVE), and the associated Z and p -values. First, the standardized loadings for all subdimensions/items on their respective constructs ranged from 0.586 to 0.934, exceeding the 0.5 benchmark, with all loadings significant at p < 0.001. This indicates that each subdimension/item effectively represented the core meaning of its corresponding construct. Second, the AVE values for PTS (0.717), EL (0.643), GL (0.705), CPSE (0.507), and PLE (0.535) all surpassed the 0.5 minimum threshold, demonstrating strong convergent validity among subdimensions/items for each construct. Summarily, the results of the reliability and validity tests provided robust support for the appropriateness of the measurement model. The high values of Cronbach’s α and CR for all constructs confirmed satisfactory consistency among subdimensions/items when measuring target constructs. The attainment of acceptable standardized loadings and AVE values further validated the representativeness of subdimensions/items for their respective constructs. These findings indicate that the measurement instruments employed in this study demonstrated satisfactory validity and reliability when assessing PTS, EL, GL, CPSE, and PLE within programming learning contexts. Table 3 Reliability and validity test results Construct Subdimensions /Items Unstd. S.E. Z p Std. Cronbach’s α CR AVE PTS TES 1.000 0.784 0.880 0.834 0.717 TAS 1.188 0.106 11.165 *** 0.905 EL EL1 1.000 0.838 0.825 0.840 0.643 EL2 1.065 0.067 15.999 *** 0.934 EL3 0.645 0.056 11.429 *** 0.595 GL GL1 1.00 0.707 0.875 0.876 0.705 GL2 1.396 0.094 14.909 *** 0.905 GL3 1.440 0.098 14.765 *** 0.893 CPSE LT 1.000 0.670 0.903 0.802 0.507 AL 1.318 0.104 12.707 *** 0.832 CO 0.918 0.096 9.535 *** 0.613 DE 1.051 0.094 11.208 *** 0.716 PLE AE 1.000 0.586 0.892 0.819 0.535 BE 1.130 0.110 10.260 *** 0.807 EE 1.014 0.107 9.457 *** 0.705 CE 1.082 0.104 10.367 *** 0.806 Note: *** indicates statistical significance at the 0.001 level (two-tailed). 4.5 Model fit test This study employed various key fit indices to evaluate the correspondence between the theoretical model and observed data, with results summarized in Table 4 . Model fit constitutes the core criterion for determining whether a theoretical framework effectively represents sample data, directly influencing the credibility of subsequent path analysis results. The Chi-square statistic ( χ 2 ), a conventional metric for evaluating model fit, yielded a value of 114.126. Upon normalization by degrees of freedom ( df ), the resultant χ 2 /df ratio was 2.717, falling below the recommended threshold of 3. This finding suggests that the model exhibited minimal deviation from the observed data, thereby indicating adequate fit. The additional fit indices obtained met the established criteria. RMSEA was 0.075, and SRMR was 0.052, both falling below the 0.08 critical value. CFI, IFI, and TLI were 0.957, 0.958, and 0.933, respectively, all exceeding the 0.90 acceptable benchmark. The convergence of these fit indices indicates that the proposed structural model demonstrated robust goodness-of-fit, with the theoretical framework and hypothesized relationships among variables receiving strong empirical support. This model effectively captures the mechanisms through which perceived teacher support operates within programming learning contexts and its influence pathways on learning engagement. It provides a reliable foundation for subsequent mediation and moderation analyses and interpretation. Table 4 Model fit test results Fit indices χ 2 df χ 2 /df RMSEA SRMR CFI IFI TLI Value 114.126 42 2.717 0.075 0.052 0.957 0.958 0.933 Threshold - - <3 <0.08 0.90 >0.90 >0.90 4.6 Path hypothesis test This study employed Structural Equation Modeling (SEM) to examine the variable pathways specified in theoretical hypotheses, with detailed results presented in Table 5 and Fig. 2 . This analysis clarified direct effect pathways among variables, providing empirical evidence for hypothesis verification. As shown in Table 5 , hypotheses H1, H2, H3, H6, H7, and H9 received empirical support, with all path relationships among variables reaching statistical significance. Specifically, perceived teacher support (PTS) demonstrated significant effects on programming learning engagement (PLE), extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE); germane load (GL) exerted a significant positive effect on computer programming self-efficacy (CPSE); and computer programming self-efficacy (CPSE) demonstrated a significant positive effect on programming learning engagement (PLE). These results confirm that perceived teacher support plays a facilitative role in promoting germane load and computer programming self-efficacy formation, while concurrently suppressing the generation of extraneous load. Table 5 Path hypothesis test results Hypothesis Path Relationship Unstandardized Coefficient (Unstd.) Standard Error (S.E.) C.R. p Standardized Coefficient (Std.) Hypothesis Outcome H1 PTS→PLE 0.105 0.037 2.812 0.005 0.219 Supported H2 PTS→EL -0.337 0.080 -4.219 *** -0.262 Supported H3 PTS→GL 0.352 0.073 4.831 *** 0.285 Supported H4 EL→PLE -0.009 0.019 -0.465 0.642 -0.024 Rejected H5 GL→PLE 0.013 0.035 0.366 0.715 0.033 Rejected H6 PTS→CPSE 0.263 0.047 5.599 *** 0.316 Supported H7 CPSE→PLE 0.321 0.085 3.783 *** 0.562 Supported H8 EL→CPSE -0.003 0.029 -0.103 0.918 -0.005 Rejected H9 GL→CPSE 0.453 0.043 10.520 *** 0.671 Supported Note: *** indicates statistical significance at the 0.001 level (two-tailed). 4.7 Mediation effect test To clarify the indirect mechanisms through which extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE) operate between perceived teacher support (PTS) and programming learning engagement (PLE), this study employed the percentile bootstrap method and bias-corrected percentile bootstrap method proposed by Taylor et al. ( 2008 ), with bootstrap samples set at 5,000 and a 95% confidence interval. Following Preacher & Hayes’s ( 2008 ) criteria, mediation effects were deemed significant if the upper and lower bounds of the confidence interval did not contain zero. The results of the mediation effect test, which encompass direct and total effects, are detailed in Table 6 . These results systematically reveal the pathways and internal logic through which factors influencing perceived teacher support operate within secondary vocational students’ programming learning contexts. As indicated by the examination results, two significant indirect pathways were identified. First, the relationship between PTS and PLE was significantly mediated by CPSE. Second, GL and CPSE constituted a significant serial mediation pathway between PTS and PLE. These findings validated the sequential transmission effects of GL and CPSE, demonstrating that PTS can elevate PLE by enhancing students’ GL, subsequently strengthening their CPSE. This study underscores the imperative of simultaneously attending to the cultivation of GL and CPSE in programming instructional practice, with integrated interventions targeting both constructs holding substantial value for promoting student learning engagement. Table 6 Mediation effects test results Path relationship Point estimate Product of coefficient Bootstrapping Bia-Corrected 95% SE p Lower Upper Indirect Effects Ind1: PTS→EL→PLE 0.003 0.007 0.602 -0.010 0.019 Ind2: PTS→CPSE→PLE 0.085 0.035 0.000 0.036 0.176 Ind3: PTS→EL→CPSE→PLE 0.000 0.004 0.895 -0.007 0.010 Ind4: PTS→GL→PLE 0.004 0.017 0.636 -0.026 0.044 Ind5: PTS→GL→CPSE→PLE 0.042 0.016 0.000 0.019 0.084 Direct Effects PTS→PLE 0.105 0.058 0.030 0.011 0.247 Total Effects PTS→PLE 0.248 0.073 0.000 0.123 0.413 4.8 Multigroup analysis: gender The objective of this study was to examine whether path relationships among variables in the theoretical model exhibited gender differences. A multi-group analysis was conducted with gender as the grouping variable. The male group (n = 150) and the female group (n = 160) were compared. The results of this study are detailed in Table 7 , Table 8 , and Table 9 . As shown in Table 7 , discrepancies in fit indices between the two models indicated gender heterogeneity in the mechanisms through which perceived teacher support influences learning engagement within programming learning contexts. Subsequent path coefficient comparisons (see Table 8 and Table 9 ) revealed that the male group’s path relationships exhibited congruence with full-sample results, whereas the female group demonstrated three significant divergences. First, H1 (PTS→PLE) yielded a positive path coefficient in the female group that failed to reach statistical significance. Second, H5 (GL→PLE) manifested as a significant negative effect in the female group, contradicting both full-sample and male group results. Third, H10 (PTS→EL→PLE) was significant exclusively within the female group. Table 7 Multigroup analysis results Group χ2 df χ2/df RMSEA SRMR CFI IFI TLI Male (n = 150) 125.565 42 2.990 0.116 0.0624 0.899 0.902 0.842 Female (n = 160) 139.749 42 3.327 0.121 0.0696 0.896 0.898 0.836 Table 8 Multigroup analysis: path hypothesis test results Hypothesis Path Relationship Male Female Estimate S.E. C.R. P Hypothesis Outcome Estimate S.E. C.R. P Hypothesis Outcome H1 PTS→PLE 0.154 0.062 2.949 0.012 Supported 0.030 0.043 0.691 0.489 Rejected H2 PTS→EL -0.225 0.111 -2.027 0.043 Supported -0.442 0.110 -4.025 *** Supported H3 PTS→GL 0.367 0.102 3.616 *** Supported 0.302 0.095 3.167 0.002 Supported H4 EL→PLE 0.025 0.030 0.818 0.414 Rejected -0.049 0.280 -1.750 0.080 Rejected H5 GL→PLE 0.106 0.055 1.946 0.052 Rejected -0.135 0.061 -2.229 0.026 Supported H6 PTS→CPSE 0.240 0.067 3.562 *** Supported 0.240 0.060 3.962 *** Supported H7 CPSE→PLE 0.398 0.135 2.949 0.012 Supported 0.473 0.151 3.134 0.002 Supported H8 EL→CPSE 0.048 0.041 1.169 0.242 Rejected -0.065 0.041 -1.611 0.107 Rejected H9 GL→CPSE 0.391 0.064 6.111 *** Supported 0.451 0.057 7.939 *** Supported Note: *** indicates statistical significance at the 0.001 level (two-tailed). Table 9 Multigroup analysis: mediation effects test results Male Female Path relationship Point estimate Product of coefficient Bootstrapping Point estimate Product of coefficient Bootstrapping Bia-Corrected 95% Bia-Corrected 95% SE p Lower Upper SE p Lower Upper Indirect Effects Ind1: PTS→EL→PLE -0.006 0.009 0.177 -0.040 0.004 0.022 0.019 0.039 0.001 0.087 Ind2: PTS→CPSE→PLE 0.096 0.053 0.005 0.020 0.245 0.113 0.086 0.005 0.021 0.379 Ind3: PTS→EL→CPSE→PLE -0.004 0.006 0.143 -0.030 0.001 0.014 0.016 0.041 0.000 0.067 Ind4: PTS→GL→PLE 0.039 0.035 0.636 0.000 0.158 -0.033 0.043 0.050 -0.186 0.000 Ind5: PTS→GL→CPSE→PLE 0.035 0.020 0.005 0.005 0.088 0.033 0.019 0.010 0.006 0.092 Direct Effects PTS→PLE 0.154 0.099 0.024 0.016 0.438 0.030 0.085 0.604 -0.133 0.188 Total Effects PTS→PLE 0.336 0.149 0.000 0.090 0.687 0.202 0.083 0.001 0.047 0.377 5 Discussion Grounded in an integrative framework synthesizing social support theory, cognitive load theory, and social cognitive theory, this study systematically investigated the mechanisms through which perceived teacher support (PTS) influences programming learning engagement (PLE). It focused particularly on validating the serial mediating roles of extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE), alongside gender moderation effects. These findings revealed multi-level psychological mechanisms. PTS not only directly facilitated programming learning engagement ( β = 0.105, p = 0.005), but also exerted indirect effects through the single mediation pathway of CPSE ( β = 0.085) and the serial mediation pathway of GL and CPSE ( β = 0.042). Concurrently, PTS significantly reduced EL ( β = −0.337, p < 0.001) and enhanced GL ( β = 0.352, p < 0.001), with the latter subsequently influencing PLE via elevated CPSE ( β = 0.453, p < 0.001). A notable finding was the identification of gender moderation effects. In the female group, the direct effect of PTS on PLE was non-significant. However, GL demonstrated a negative effect on PLE ( β = −0.135, p = 0.026), and EL exhibited a significant mediating effect between PTS and PLE. Conversely, in the male group, the direct effect of PTS on PLE remained significant. Additionally, the serial mediation effect of GL and CPSE proved more pronounced. These findings provide multi-level interpretations of the internal mechanisms through which teacher support translates into learning behaviors within secondary vocational programming contexts. First, the direct effect aligns with the core tenets of social support theory. As students’ subjective cognition and internalization of supportive experiences in programming learning environments, PTS effectively addresses students’ psychological needs during highly demanding programming learning. This alleviates frustration and anxiety while transforming into sustained and profound motivation for learning participation. This finding is consistent with the results reported by Xu et al. ( 2023 ) in the field of vocational education research, which support the stable positive association between perceived teacher support and learning engagement. Second, the mediation mechanism validates the sequential logic of “cognitive regulation — psychological construction — behavioral engagement.” The significant single mediation effect of CPSE conforms to Bandura’s ( 1997 ) core proposition in social cognitive theory that external environmental support must be transformed through individuals’ intrinsic psychological beliefs to drive behavioral change. Given secondary vocational students’ characteristics of weak programming foundations and heterogeneous prior knowledge, teachers’ positive feedback and targeted guidance constitute critical pathways for accumulating successful learning experiences (Karsten et al., 2012 ), thereby strengthening self-efficacy beliefs regarding programming task completion and ultimately promoting enhanced learning engagement. The serial mediation effect of GL and CPSE further reveals that teacher support facilitates the construction and integration of programming knowledge schemata by guiding students to invest moderate GL. Such deep cognitive participation substantially elevates self-efficacy and subsequently drives sustained increases in learning engagement. This aligns profoundly with the theoretical assertion that GL promotes deep processing of knowledge (Sweller, 1988 ). Third, the differentiated effects of cognitive load reflect the actual state of secondary vocational programming education. The non-significant mediating effect of EL may be attributed to current programming instruction having effectively controlled EL through structured design (e.g., clear task instructions, visualized instructional materials), as evidenced by the mean EL of 2.73 in this study. With direct interference with learning engagement reduced to minimal levels, the mediating effect failed to manifest. Conversely, GL exerted no direct effect on PLE but operated exclusively through CPSE, indicating that the core value of GL lies in promoting knowledge construction and self-efficacy enhancement rather than directly driving learning participation behaviors. This concurs with Quintero-Manes & Vieira ( 2025 ) regarding pathways of cognitive load effects in programming learning. Fourth, gender-differentiated moderation effects mirror cognitive characteristics and learning style variations among secondary vocational students. Males have been shown to exhibit superior performance in logical thinking and challenge acceptance (Meehan, 1984 ; Cassar & Rigdon, 2021 ). This suggests that males may more readily derive accomplishment through GL guided by teacher support and subsequently strengthen self-efficacy. Therefore, the serial mediation effect is more pronounced in males. Within the female group, the significant mediating effect of EL alongside the non-significant direct effect of PTS on PLE may relate to the high abstraction and strong logical demands characteristic of programming learning. When PTS fails to effectively reduce EL, superfluous cognitive consumption occupies limited working memory resources (Sweller, 1988 ), weakening direct facilitative effects on PLE and necessitating the mediating pathway of EL reduction to create conditions for females to focus on core programming knowledge. Additionally, the negative effect of GL on PLE within the female group may stem from females facing relatively greater cognitive challenges in programming learning, with excessive investment in GL readily inducing cognitive depletion (Phan & Ngu, 2021 ) and consequently diminishing learning participation willingness. It underscores the necessity of targeted research on gender differences against the backdrop of demographic shifts in vocational education. The major contribution of this study lies in constructing and validating, through multi-theoretical integration and empirical examination, a comprehensive mechanism of how perceived teacher support influences learning engagement within secondary vocational programming contexts. On the one hand, this study transcends prior limitations of predominantly examining bivariate variable relationships by incorporating perceived teacher support, cognitive load, and self-efficacy within a unified analytical framework. Its integration clarifies the sequential transmission pathway of “environmental support — cognitive processing — motivational beliefs — learning behaviors.” On the other hand, this investigation clarifies the learning psychological mechanisms characteristic of their “high support dependency, low foundational starting point” profile by centering on secondary vocational students as a distinctive population. It enhances the explanatory power and contextual applicability of relevant theories within vocational education settings. Concurrently, this study provides empirical support for attending to individual differences in programming education, particularly addressing instructional adaptation needs amid demographic shifts in vocational education by specifying gender-differentiated moderation effects. 5.1 Implications The theoretical and practical implications of this study operate at multiple levels. Theoretically, the findings support the development of cognitive-motivational integration models by incorporating environmental support variables into the cognitive load theoretical framework. This integration provides novel theoretical perspectives for the learning sciences. Future research may further examine this model’s adaptability in emerging technology courses such as data science and artificial intelligence, while exploring novel manifestations of teacher support within digitally intelligent instructional environments, such as intelligent pedagogical agents with capabilities encompassing knowledge-based responses, emotional support, and personalized guidance (Wu et al., 2025 ). Moreover, the identification of gender differences contributes to a more comprehensive understanding of individual differences in programming education, thereby establishing a theoretical foundation for subsequent investigations that focus on differential variables, such as gender and learning foundation. Practically, this study provides precise instructional improvement strategies for secondary vocational programming education. First, curriculum design should adhere to the “cognitive scaffolding” principle. Initial tasks should employ visual programming tools and code templates to minimize EL; as students’ skills develop, progressively adopt open-ended projects and interdisciplinary application problems to increase GL. Second, the cultivation of CPSE should be prioritized at the instructional core. The accumulation of students’ successful experiences is facilitated through task decomposition, staged feedback, and modeling exemplars, particularly through the design of “small-step, rapid-progress” pathways to success tailored to secondary vocational students’ weak foundational characteristics. Third, the implementation of gender-sensitive teacher support strategies is imperative. Female students should be provided with cognitive tasks that incorporate a gradual increase in difficulty, thereby enhancing their ability to regulate EL and prevent cognitive depletion from excessive cognitive investment. Conversely, male students should be given more exploratory tasks to stimulate active cognitive participation. It is essential to ensure that the effects of cognitive load regulation and perceived teacher support are synergistic. Fourth, the enhancement of programming instructional support systems is crucial. It is imperative to strengthen teacher training in support provision and cognitive load regulation capabilities, develop gender-sensitive instructional resources and task systems, and establish virtuous cycles of “support — cognition — motivation — behavior” to promote sustained enhancement of student learning engagement. 5.2 Limitations This study has several limitations. First, the cross-sectional design imposes limitations on causal inference. While the theoretical framework supports the existence of directional relationships among variables, experimental or longitudinal designs would provide more conclusive evidence. Future research may employ instructional intervention experiments to measure dynamic changes in student psychological variables before and after modifications in teacher support strategies. Second, the sample was restricted to a school in a specific region of China, which may have affected the generalizability of the results. The learning experiences of students enrolled in vocational schools are influenced by a variety of factors, including cultural contexts, educational policies, and industry demands. Future research should aim to expand the scope of sampling and conduct cross-regional and cross-cultural comparative studies to examine the robustness of the models. Third, this study did not adequately control for potential confounding variables such as prior programming experience and family background. These factors may moderate the effects of teacher support. Future research should construct more comprehensive predictive models incorporating these individual difference variables. Fourth, this study focused on short-term learning process variables without tracking long-term learning outcomes. Future research may establish complete evidence chains of “psychological mechanisms — learning processes — long-term outcomes,” such as tracking students’ subsequent application of programming skills in specialized courses, thereby enhancing practical value. Abbreviations EU European Union UNESCO United Nations Educational, Scientific and Cultural Organization PTS Perceived Teacher Support PLE Programming Learning Engagement CL Cognitive Load IL Intrinsic Load EL Extraneous Load GL Germane Load CPSE Computer Programming Self-Efficacy TES Teacher Emotional Support TAS Teacher Academic Support LT Logical Thinking AL Algorithm CO Control DE Debug AE Agentic Engagement BE Behavioral Engagement EE Emotional Engagement CE Cognitive Engagement SEM Structural Equation Modeling Declarations Ethics approval and consent to participate The study adhered to the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Hunan University of Science and Technology. Prior to data collection, all participants were fully informed of the purpose and procedures of the study. Completion of the questionnaire was deemed as informed consent to participate. Consent for publication Not applicable. Availability of data and materials The data are available upon request from the corresponding author. Competing interests The authors declare no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions Conceptualization, X.W., methodology, J.L., software, Y.W., validation, H.Z., data curation, Y.W. and Y.X.W., writing—original draft preparation, X.W. and Y.W., writing—review and editing, X.W. and Y.W., visualization, Y.W. and Y.X.W., supervision, X.W., J.L., and H.Z. All authors have read and agreed to the published version of the manuscript. Acknowledgements The authors are grateful to the individuals who participated in this study. References Almdahem, A. (2023). Teaching programming in the computer science strand of the 2014 National Curriculum for computing at key stage 4: Challenges, difficulties and prospects [Doctoral dissertation]. 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The influence of presentation format and knowledge type on multimedia learning – based on the research of cognitive load theory [Master’s thesis]. Hangzhou Normal University. https://kns.cnki.net/kcms2/article/abstract?v=Mz9udXFtQxl_FOjdniqJ4Rqyw4OQV4mgNpxmIXWskqaCh3tX7D-G4RwgbBNi5MVs-eAKZkNiN61caVLIzI85QZjxSyJ-WKRAadVYDBrHMHtRcLO2yc9VlxRre4gDkwLS2DCE10YurornoHwdR-wdmzTRZPghjZkyrGXZqNQmiUM9n3hGrXG19A==&uniplatform=NZKPT&language=CHS Wu, X. N., Wang, Y., & Zhang, L. (2025). Artificial Intelligence Empowering the Teaching of “An Introduction to Community for the Chinese Nation”: Research on Dialogue-Driven Practice Pathways. J.NORTHWEST MINZU UNIVERSITY(Philosophy and Social Sciences) , (5), 75–85. https://doi.org/10.14084/j.cnki.cn62-1185/c.20251020.006 Wu, Y., & Kang, X. (2023). Relationship between perceived teacher support and learning engagement in EFL context: The mediating role of achievement emotions. International Journal of Education and Humanities , 3(1), 85-98. https://doi.org/10.58557/(ijeh).v3i1.141 Xiang, S. (2023). A study of factors influencing junior high school students’ engagement in an adaptive learning environment [Master’s thesis]. Southwest University. https://doi.org/10.27684/d.cnki.gxndx.2023.002124. Xu, X., Wu, Z., & Wei, D. (2023). The relationship between perceived teacher support and student engagement among higher vocational students: A moderated mediation model. Frontiers in Psychology , 14 , 1116932. https://doi.org/10.3389/fpsyg.2023.1116932 Yang, Y., Li, G., Su, Z., & Yuan, Y. (2021). Teacher’s Emotional Support and Math Performance: The Chain Mediating Effect of Academic Self-Efficacy and Math Behavioral Engagement. Frontiers in Psychology , 12 , 651608. https://doi.org/10.3389/fpsyg.2021.651608 Yu, Z. X. (2019). Research on Relationship between Teachers’ Support and Students’ Learning Engagement in Higher Vocational Colleges—Based on Analysis of Mediating Effect of Academic Self-efficacy. Vocational and Technical Education , 40 (17), 65–70. Zhang, H. (2024). Cognitive load as a mediator in self-efficacy and English learning motivation among vocational college students. PLOS ONE , 19 (11), e0314088. https://doi.org/10.1371/journal.pone.0314088 Zheng, X. L., Lyu, Z. Y., Geng, X. H., Wang, F., Lai, W. H., & Zhao, A. P. (2025). Design, Architecture and Effectiveness of a Classroom Emotional Agent. Modern Distance Education Research , 37 (4), 14–22. https://doi.org/10.3969%2Fj.issn.1009-5195.2025.04.002 Zhou, S., & Wu, W. (2023). A study on the relationship between higher vocational students’ perceived teacher support and learning engagement: The chain mediation of academic self-efficacy and professional commitment. Nurture , 17(4), 595–606. https://doi.org/10.55951/nurture.v17i4.435 Additional Declarations No competing interests reported. Supplementary Files Appendix1Measurementitems.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 30 Mar, 2026 Editor invited by journal 03 Mar, 2026 Editor assigned by journal 01 Mar, 2026 Submission checks completed at journal 01 Mar, 2026 First submitted to journal 25 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8966481","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615305061,"identity":"72ee55e4-0ce1-4728-b3fe-4b427eb67448","order_by":0,"name":"Xinning Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACNmaGhAMJFTZyBlABxgZCWvjYGR4++HAmzRis5QAxWuT4GR8bzmw5nLiBaC1szMxp0rwNaenbpXuMP39gsJHdcID52QP8WtiAWnbY5O6cc8ZM4gBDmvGGA2zmBvi18AC1nEnL3XAjxwzoMKALD/CwSeDXwv9NmrftcLrBjRzjDwcY/hOjhSHZcGbb4QSgFgOgww4QpSURFMiGG26klUmcMUg2nnmYzQyvFvn+A+ColDe4kbz5Q0WFnWzf8eZneLWgAVBQMZOgfhSMglEwCkYBdgAAcmxMYh8ufIcAAAAASUVORK5CYII=","orcid":"","institution":"Hunan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Xinning","middleName":"","lastName":"Wu","suffix":""},{"id":615305063,"identity":"09f1630c-a5ef-4efa-9632-bd6b391f6624","order_by":1,"name":"Yi Wang","email":"","orcid":"","institution":"Hunan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Wang","suffix":""},{"id":615305065,"identity":"6850eb44-daf9-4967-b71c-34662e02ebcb","order_by":2,"name":"Haifeng Zheng","email":"","orcid":"","institution":"Luoding Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Zheng","suffix":""},{"id":615305067,"identity":"7fdf46f2-3a4a-4551-8489-a753c44f7b56","order_by":3,"name":"Jingchao Liu","email":"","orcid":"","institution":"Hunan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jingchao","middleName":"","lastName":"Liu","suffix":""},{"id":615305068,"identity":"8fbd7e06-ede5-4fe2-b78d-b3e147a8de20","order_by":4,"name":"Yingxi Wang","email":"","orcid":"","institution":"Hunan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yingxi","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-02-25 10:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8966481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8966481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105955435,"identity":"812b8ac8-f29d-4a0b-afbe-1521719a61ad","added_by":"auto","created_at":"2026-04-01 19:56:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40439,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesis model of the study\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e This figure presents the hypothesized structural model illustrating the relationships among perceived teacher support (PTS), extraneous load (EL), germane load (GL), computer programming self-efficacy (CPSE), and programming learning engagement (PLE), with the corresponding hypothesis labels (H1–H14).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8966481/v1/7b3e7dffe040b2702391908f.jpg"},{"id":106094253,"identity":"baf9020f-0b4e-4f1b-b6b6-06fb2872ee40","added_by":"auto","created_at":"2026-04-03 11:41:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33570,"visible":true,"origin":"","legend":"\u003cp\u003ePath test results\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eThis figure presents the standardized path coefficients of the structural model, with solid lines indicating significant paths (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, denoted by ***) and dashed lines indicating non-significant paths.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8966481/v1/af4012e15b254d97be573a39.jpg"},{"id":106096348,"identity":"5b64ae2a-23b6-4cea-8859-943c0edef3a5","added_by":"auto","created_at":"2026-04-03 11:54:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1811547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8966481/v1/a77daae9-8ac5-48c2-824f-65367b376b0e.pdf"},{"id":105955436,"identity":"658d651c-5a61-47b5-a9e2-807d54a3360d","added_by":"auto","created_at":"2026-04-01 19:56:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23225,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1Measurementitems.docx","url":"https://assets-eu.researchsquare.com/files/rs-8966481/v1/d65182adabc6ce3c681daa5a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Germane Cognitive Load and Programming Self-Efficacy as Mediators of Teacher Effects on Student Engagement in Vocational Programming Education","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe transformation of programming from a specialized skill to a foundational element of digital literacy reflects the profound impact of digital technologies on global socioeconomic structures. The cultivation of creative thinking, logical reasoning, and skills relevant to future employment is imperative. On this issue, the international community has reached a definitive consensus. According to the EU\u0026rsquo;s Digital Education Action Plan (2021\u0026ndash;2027), computational thinking and programming are considered essential skills for all students. UNESCO\u0026rsquo;s Youth Coding Initiative exemplifies a proactive approach to the global dissemination of programming and artificial intelligence skills. Education systems worldwide are thus pressured to adapt, with vocational education facing acute demands due to its direct alignment with labor market needs.\u003c/p\u003e \u003cp\u003eHowever, the core challenge in teaching programming lies in its high level of abstraction, rigorous logic, and strong practicality, which can easily lead to issues such as low learning motivation and insufficient engagement among novices (Bennedsen \u0026amp; Caspersen, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Margulieux et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This challenge is especially pronounced in secondary vocational education, which emphasizes practical skill development and direct alignment with industry demands. Secondary vocational students typically manifest uneven prior knowledge, fragmented academic foundations, and weak abstract reasoning capacities, generating heightened dependency on external teaching support. This vulnerability makes them particularly susceptible to experiencing excessive extraneous load from suboptimal instructional design, thereby intensifying learning barriers. In this context, the systematic exploration of effective teaching support mechanisms has emerged as a critical issue for enhancing the quality of secondary vocational programming education, with an emphasis on understanding the underlying logic by which perceived teacher support enhances student learning engagement through cognitive regulation and psychological construction pathways. Existing research has yet to adequately address this issue, due to several limitations: First, research has primarily focused on examining pairwise relationships between variables such as perceived teacher support and learning outcomes. However, there is a paucity of research that integrates key variables like perceived teacher support, cognitive load, and self-efficacy into a unified framework to reveal their synergistic mechanisms. Second, there is insufficient empirical evidence to support the complete sequential psychological pathway that external teacher support shapes self-efficacy through cognitive load regulation, thereby driving learning engagement. Third, extant research samples are predominantly drawn from traditional higher education settings, thereby overlooking the unique learning characteristics and potentially distinct psychological mechanisms of vocational education students. Fourth, as gender structures evolve within the context of vocational education, the role of gender as a significant moderating variable in programming learning processes has yet to be thoroughly examined.\u003c/p\u003e \u003cp\u003eTo address these gaps, the present study synthesizes social support theory (Malecki \u0026amp; Demaray, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), cognitive load theory (Sweller, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), and social cognitive theory (Bandura, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) within an integrated conceptual framework. We construct and empirically validate a serial mediation model to systematically investigate the underlying mechanisms linking perceived teacher support (PTS) to programming learning engagement (PLE) among secondary vocational students, with particular emphasis on testing the sequential mediating effects of extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE), alongside the moderating role of gender. This study employs structural equation modeling and multi-group analysis on survey data from 310 secondary vocational programming learners to investigate the direct and indirect roles of perceived teacher support in learning engagement, test a serial mediation model via extraneous load, germane load, and programming self-efficacy, and ultimately uncover the differential moderating role of gender across these pathways.\u003c/p\u003e \u003cp\u003eThe present study integrates a variety of theoretical perspectives to construct a chained mediation model, empirically revealing the complete sequential mechanism of \u0026ldquo;environmental support \u0026mdash; cognitive processing \u0026mdash; motivational beliefs \u0026mdash; learning behaviors\u0026rdquo; in vocational education programming contexts. It overcomes the limitations of isolated variable research and deepens the integrated application of social support theory and cognitive load theory in vocational education contexts. Concurrently, this study focuses on secondary vocational students, a relatively neglected group in existing research. By clarifying the distinctiveness of their learning psychological mechanisms, it enhances the explanatory power and applicability of the theory within the vocational education context. Furthermore, this study provides educators with targeted intervention strategies, including the optimization of targeted instructional support to reduce EL, optimize GL, and strengthen CPSE by clarifying the gender-modulating effects and differentiated pathways of action. These strategies enhance the quality and efficiency of programming instruction while promoting educational equity.\u003c/p\u003e"},{"header":"2 Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Perceived teacher support\u003c/h2\u003e \u003cp\u003eTeacher support is defined as the supportive behaviors exhibited by teachers that students receive during learning or daily life (Skinner \u0026amp; Belmont, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Consistent with social support theory principles, behaviors conducive to well-being that individuals perceive from their social networks have been shown to produce universal benefits and positively influence individuals\u0026rsquo; mental health and development (Berkman \u0026amp; Syme, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). The evolution of Self-Determination Theory has expanded its definition, leading to its widespread recognition as a critical environmental resource for fulfilling students\u0026rsquo; three fundamental psychological needs: autonomy, competence, and relatedness. By fulfilling these basic psychological needs (Miller et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), supportive contexts enhance intrinsic motivation, facilitate extrinsic motivation internalization, and sustain deep engagement.\u003c/p\u003e \u003cp\u003eIn the field of research on teacher support, scholars have developed distinct research frameworks based on varying theoretical perspectives. From the functionalist perspective of social support theory, Malecki \u0026amp; Demaray (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) parsed teacher support into four constituent dimensions: emotional, informational, appraisal, and instrumental support. This functionalist typology transcends specific educational contexts, deriving from universal interpersonal support functions to systematically catalog concrete forms of instructional assistance, thereby emphasizing a behavior-centric classification of supportive behaviors. Grounded in Self-Determination Theory\u0026rsquo;s psychological needs orientation, Brewster \u0026amp; Bowen (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) delineated an alternative three-dimensional framework encompassing emotional, behavioral, and autonomy support. This needs-based conceptualization directly maps external instructional behaviors to students\u0026rsquo; intrinsic psychological needs, providing a coherent theoretical framework for explaining the mechanisms through which teacher support influences motivation, learning engagement, and psychological well-being. Alternatively, Patrick et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) conceptualized teacher support as a holistic perceptual variable, examining it alongside other socio-contextual classroom dimensions. This integrative approach foregrounds two core dimensions: emotional and academic support. It primarily assesses whether students perceive instructors as dependable sources of support.\u003c/p\u003e \u003cp\u003eGiven the methodological constraints of directly assessing behaviors comprising both explicit and implicit dimensions, perceived teacher support (PTS), operationalized as students\u0026rsquo; subjective evaluations of instructional assistance, has become the predominant measurement approach and research focus. Contemporary scholarship predominantly examines relationships between PTS and critical educational outcomes, including learning engagement, academic motivation, academic adjustment, and emotional development. Existing research consistently confirms moderate to strong positive correlations between PTS and multiple dimensions of learning engagement (Roorda et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), demonstrating robust cross-contextual generalizability across educational levels, disciplines, and cultural settings. In physical education contexts, instructor-provided autonomy, competence, and relatedness support significantly enhance students\u0026rsquo; behavioral and emotional engagement (Guo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). PTS exerts both direct effects on learning engagement and indirect influences mediated through achievement emotions (Strati et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wu \u0026amp; Kang, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At vocational and higher education levels, the predictive power of teacher support for learning engagement becomes particularly pronounced when students confront academic challenges (Xu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou \u0026amp; Wu, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, the facilitative effects of teacher support on engagement rarely operate through single direct pathways; rather, they propagate through multiple indirect routes involving learning motivation, positive emotions, and self-efficacy. This multifaceted mediation pattern underscores the necessity of examining the underlying mechanisms, thereby providing critical directions for subsequent research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Programming learning engagement\u003c/h2\u003e \u003cp\u003eLearning engagement denotes the intensity of students\u0026rsquo; active involvement in academic tasks or learning activities (Fredricks et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Scholarly consensus recognizes a tripartite structure encompassing emotional, cognitive, and behavioral dimensions. Specifically, emotional engagement encompasses students\u0026rsquo; affective reactions during learning processes. Cognitive engagement refers to the mental effort and intellectual activities learners invest in learning. Behavioral engagement manifests as the quantity and quality of students\u0026rsquo; active participation (Hiver et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Building upon this classical framework, Reeve \u0026amp; Tseng (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) augmented the model by introducing agentic engagement as a fourth core dimension. This construct is defined as \u0026ldquo;students\u0026rsquo; constructive contributions toward the instructional process they receive,\u0026rdquo; emphasizing learners\u0026rsquo; transcendence beyond passive participation or reactive responding to actively influence, enrich, and regulate their own learning processes and instructional environments through personal preferences, interests, and needs. Manifestations include proactively asking instructors questions, expressing learning preferences, offering suggestions for curricular improvement, and independently seeking supplementary learning resources.\u003c/p\u003e \u003cp\u003eResearch on programming learning engagement typically adapts these general theoretical frameworks to the distinctive demands of programming education, focusing on addressing pedagogical challenges arising from knowledge abstraction, procedural complexity, and high practical demands while exploring pathways to enhance student engagement through innovative instructional models, pedagogical strategies, and technological tools. Within programming learning contexts, each dimension assumes distinct disciplinary characteristics. Agentic engagement manifests through proactive inquiry, articulation of learning preferences, contribution to curricular improvements, and autonomous pursuit of personalized learning pathways. This dimension centers on the learner\u0026rsquo;s transition from passive task reception to active co-designer of their programming learning experiences. Behavioral engagement is evidenced by sustained concentration during coding, persistent practice, tenacity when confronting complex problems, and active participation in collaborative activities, highlighting the time investment, effort allocation, perseverance, and adherence to task requirements that learners exhibit. Emotional engagement encompasses interest in and curiosity about programming concepts, enjoyment during exploration, and appreciation of programming\u0026rsquo;s utility, fundamentally reflecting learners\u0026rsquo; subjective affective experiences. Cognitive engagement is characterized by deep elaboration and integration of programming knowledge, strategic approaches to coding practice, and the depth of cognitive processing and strategic deployment during problem-solving, examining whether learners construct knowledge through active mental elaboration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Research questions\u003c/h2\u003e \u003cp\u003eExisting research has identified associations among students\u0026rsquo; perceived teacher support (PTS), cognitive load (CL), self-efficacy, and learning engagement (Yu, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, whether extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE) act as sequential mediators in the relationship between PTS and programming learning engagement (PLE) remains insufficiently examined. Furthermore, the potential moderating role of gender in this process within vocational education is unclear. To address these gaps and provide targeted insights, this study is guided by the following research questions:\u003c/p\u003e \u003cp\u003eRQ1: Does perceived teacher support (PTS) have a direct effect on programming learning engagement (PLE) among secondary vocational students?\u003c/p\u003e \u003cp\u003eRQ2: Is the effect of PTS on PLE sequentially mediated by extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE)?\u003c/p\u003e \u003cp\u003eRQ3: Does student gender moderate the direct and/or mediated pathways in the proposed model?\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Hypothesis model\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 PTS, CL, and CPSE in programming learning\u003c/h2\u003e \u003cp\u003ePerceived teacher support (PTS) is defined as students\u0026rsquo; subjective evaluations of teachers\u0026rsquo; emotional care, cognitive guidance, and autonomy encouragement within learning environments. In the context of programming education, which is characterized by numerous challenges, these functions become particularly critical and multifaceted. On the one hand, the abstract nature and logical rigor of programming knowledge can generate frustration among learners. Almdahem (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicated that heightened perceptions of instructional support are associated with reduced experienced difficulties during learning. Emotional support directly alleviates learning anxiety, while clear cognitive guidance mitigates task-induced cognitive load. On the other hand, PTS significantly and positively predicts students\u0026rsquo; attitudes toward programming and their flow experiences during learning (Kong et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), rendering learners more inclined to perceive programming as an interesting and worthwhile challenge. Furthermore, Zheng et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that introducing intelligent agents with personalized emotional feedback and support capabilities (an extension of teacher support) into elementary programming classrooms substantially enhances student concentration and participation, corroborating the significance of emotional and cognitive support for sustaining learning behaviors. Hence, we hypothesize:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003ePerceived teacher support exerts a significant positive effect on students\u0026rsquo; programming learning engagement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAccording to cognitive load theory, cognitive demands during learning can be categorized into three distinct types: intrinsic load (IL), extraneous load (EL), and germane load (GL). IL is derived from the interaction between two factors: element interactivity and learners\u0026rsquo; prior knowledge levels. The former constitutes an immutable task attribute, while the latter is a stable individual difference. Neither of these factors is directly manipulable by teachers. Consequently, the present study excludes IL from consideration.\u003c/p\u003e \u003cp\u003eExcessive EL has been shown to deplete limited working memory capacity, trigger frustration, and divert attention resources, thereby significantly undermining learning engagement (Xiang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Teachers can effectively reduce EL arising from poor instructional design by providing clear task instructions, well-structured materials, and timely technical support, thereby enabling students to focus cognitive resources on core learning activities (Li, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Concurrently, moderate GL has been shown to correlate with deep learning and higher-order thinking development, thereby promoting cognitive engagement (Ayvaz-Tuncel \u0026amp; Demir, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Teachers assist students in managing intrinsic task difficulty and direct cognitive effort toward knowledge construction, thereby promoting deep engagement rather than surface-level coping through instructional scaffolding such as decomposing complex tasks, providing worked examples, and designing heuristic questions (Li, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, when confronted with elevated cognitive demands, teachers\u0026rsquo; emotional and cognitive support will enhance students\u0026rsquo; confidence and efficacy beliefs in meeting challenges, fostering persistence rather than abandonment (Dong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, we propose:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003ePerceived teacher support exerts a significant negative effect on extraneous load.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003ePerceived teacher support exerts a significant positive effect on germane load.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003eExtraneous load exerts a significant negative effect on programming learning engagement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5\u003c/strong\u003e \u003cp\u003eGermane load exerts a significant positive effect on programming learning engagement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe more pivotal function of perceived teacher support (PTS) lies in its indirect influence on deep learning outcomes through shaping students\u0026rsquo; intrinsic psychological mechanisms. When students perceive trust and support from their teachers, they are more inclined to develop positive beliefs in their own capabilities. Students with elevated self-efficacy tend to demonstrate greater persistence, more positive emotional states, and more sophisticated cognitive strategy use when confronted with learning challenges. This suggests a willingness to invest more time and effort in the learning process, while also deriving enjoyment from learning (Zhou \u0026amp; Wu, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Multiple studies across educational contexts have corroborated that PTS significantly and positively predicts academic self-efficacy (Yu, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jia et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This teacher-support-activated self-efficacy has been shown to directly translate into more sustained and profound learning behaviors and emotional engagement.\u003c/p\u003e \u003cp\u003eWhen conceptualizing self-efficacy within the specific domain of computer programming self-efficacy (CPSE), the relationship among teacher support, self-efficacy, and learning engagement has been empirically validated in information technology and programming learning contexts. Liu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) investigated the impact of teacher support on students\u0026rsquo; self-efficacy in high school information technology courses. Their findings indicated that teacher support significantly positively influenced students\u0026rsquo; self-efficacy (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.719). Computer self-efficacy was also found to have a substantial impact on learning engagement in information technology courses (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.709). Within the total effect of \u0026ldquo;teacher support \u0026rarr; learning engagement,\u0026rdquo; the mediating effect of computer programming self-efficacy accounts for 65.72% of the variance. These findings indicate that although teacher support directly facilitates learning engagement, its core function operates through enhancing students\u0026rsquo; programming confidence to indirectly catalyze sustained, deep learning behaviors. In the context of programming education, teacher support functions as a critical external resource, initiating virtuous cycles. Computer programming self-efficacy serves as the central psychological mediating mechanism, and learning engagement represents the ultimate outcome most directly influenced by students\u0026rsquo; programming confidence. Accordingly, we hypothesize:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH6\u003c/strong\u003e \u003cp\u003ePerceived teacher support exerts a significant positive effect on computer programming self-efficacy.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH7\u003c/strong\u003e \u003cp\u003eComputer programming self-efficacy exerts a significant positive effect on programming learning engagement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eWhen task-induced cognitive load, particularly extraneous load, chronically exceeds individual processing capacities, learners readily experience frustration and resource depletion. These negative experiences subsequently undermine self-efficacy beliefs, creating a vicious cycle (Tingting et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, when learners invest germane load and successfully construct knowledge schemata and master relevant skills, the resulting \u0026ldquo;mastery experiences\u0026rdquo; constitute the most potent source of self-efficacy enhancement (Hartelt \u0026amp; Martens, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such positive experiences serve to reinforce the notion that individuals possess the capacity to achieve their goals. Therefore, we propose:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH8\u003c/strong\u003e \u003cp\u003eExtraneous load exerts a significant negative effect on computer programming self-efficacy.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH9\u003c/strong\u003e \u003cp\u003eGermane load exerts a significant positive effect on computer programming self-efficacy.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 The mediating effects of CL and CPSE\u003c/h2\u003e \u003cp\u003eTeachers can directly regulate students\u0026rsquo; cognitive load levels by providing effective instructional support and optimizing instructional design (Wang \u0026amp; Cao, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Specifically, the objectives of teacher support typically encompass reducing extraneous load (EL) while deliberately increasing germane load (GL). Existing research has confirmed that EL exerts direct negative effects on learning engagement, whereas GL demonstrates significant positive correlations with learning outcomes. For instance, Xiang (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found in a study of adaptive learning environments that EL significantly and negatively influenced junior high school students\u0026rsquo; learning engagement. Similarly, Wu (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) demonstrated in multimedia learning research that enhanced GL substantially improved learners\u0026rsquo; learning effectiveness. Consequently, we hypothesize:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH10\u003c/strong\u003e \u003cp\u003eExtraneous load mediates the relationship between perceived teacher support and programming learning engagement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH11\u003c/strong\u003e \u003cp\u003eGermane load mediates the relationship between perceived teacher support and programming learning engagement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eEmpirical studies have confirmed the mediating role of self-efficacy in the relationship between teacher support and learning engagement (Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; He et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Alrashidi \u0026amp; Alshammari, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Concurrently, social cognitive theory posits that environmental factors shape behavioral manifestations through influencing individual cognitive factors. Both specific empirical investigations and overarching theoretical frameworks provide robust support for the mediating function of self-efficacy. Hence, we hypothesize:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH12\u003c/strong\u003e \u003cp\u003eComputer programming self-efficacy mediates the relationship between perceived teacher support and programming learning engagement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTeacher support, particularly autonomy support and clear structured guidance, effectively regulates students\u0026rsquo; extraneous load through instructional scaffolding and minimizing irrelevant information interference, thereby optimizing cognitive resource allocation (Evans et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Zhang (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) explicitly examined the mediating role of cognitive load between self-efficacy and English learning motivation, confirming the significant influence of cognitive load on self-efficacy. Additionally, extensive research demonstrates that self-efficacy constitutes a critical antecedent of learning engagement. Students with elevated self-efficacy demonstrate greater willingness to exert effort, persist longer, and engage more deeply across cognitive, affective, and behavioral dimensions (Martin \u0026amp; Rimm-Kaufman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Accordingly, we propose:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH13\u003c/strong\u003e \u003cp\u003eExtraneous load (negative) and computer programming self-efficacy serially mediate the relationship between perceived teacher support and programming learning engagement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH14\u003c/strong\u003e \u003cp\u003eGermane load and computer programming self-efficacy serially mediate the relationship between perceived teacher support and programming learning engagement.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Research model\u003c/h2\u003e \u003cp\u003eBased on the aforementioned hypotheses, the structural model of the present study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participants\u003c/h2\u003e \u003cp\u003eThis study employed cluster random sampling, recruiting students from a secondary vocational school in City Y, Guangdong Province, China. A total of 367 questionnaires were distributed. After implementing rigorous screening procedures that excluded invalid responses with completion times under three minutes or containing eight consecutive identical answers, 310 valid questionnaires were retained, yielding an effective response rate of 84.47%. The demographic characteristics of the sample were as follows: 150 male participants (48.39%) and 160 female participants (51.61%); 93 students with urban household registration (30.00%) and 217 students with rural household registration (70.00%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Instrument development\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Perceived teacher support scale\u003c/h2\u003e \u003cp\u003eThe measurement of perceived teacher support was assessed using the Teacher Support Scale revised by Patrick et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This eight-item instrument comprises two dimensions: teacher emotional support (TES) and teacher academic support (TAS). Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating stronger perceived teacher support. In this study, Cronbach\u0026rsquo;s α coefficients for the total scale and the two subscales were 0.880, 0.792, and 0.827, respectively, indicating satisfactory internal consistency reliability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Cognitive load scale\u003c/h2\u003e \u003cp\u003eThe measurement of cognitive load was measured using the Cognitive Load Scale revised by Leppink et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This nine-item instrument comprises three distinct dimensions: intrinsic, extraneous, and germane load, with three items assigned to each dimension. Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating elevated levels of the corresponding cognitive load type. In this study, Cronbach\u0026rsquo;s α coefficients for the intrinsic, extraneous, and germane load subscales were 0.900, 0.825, and 0.875, respectively, meeting acceptable reliability standards.\u003c/p\u003e \u003cp\u003eNotably, while intrinsic load (IL) was not incorporated into the statistical analyses of variable relationships, its inclusion in the questionnaire design and administration was imperative to ensure participants\u0026rsquo; accurate comprehension of the measurement construct. This comprehensive approach enabled respondents to clearly distinguish among and appropriately complete items related to intrinsic, extraneous, and germane load dimensions. Concurrently, measuring IL provided auxiliary insights into students\u0026rsquo; cognitive burden when engaging with programming knowledge and tasks, thereby providing reference points for subsequent instructional optimization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Computer programming self-efficacy scale\u003c/h2\u003e \u003cp\u003eThe measurement of computer programming self-efficacy was assessed using the Computer Programming Self-Efficacy Scale revised by Tsai et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This twelve-item instrument comprises four dimensions: logical thinking (LT), algorithm (AL), control (CO), and debug (DE). Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating stronger computer programming self-efficacy. In this study, Cronbach\u0026rsquo;s α coefficients for the total scale and the four subscales were 0.903, 0.844, 0.879, 0.860, and 0.810, respectively, meeting acceptable psychometric standards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Programming learning engagement scale\u003c/h2\u003e \u003cp\u003eThe measurement of programming learning engagement was measured using the Student Engagement Scale revised by Reeve \u0026amp; Tseng (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This twelve-item instrument comprises four dimensions: agentic engagement (AE), behavioral engagement (BE), emotional engagement (EE), and cognitive engagement (CE). Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater programming learning engagement. In this study, Cronbach\u0026rsquo;s α coefficients for the total scale and the four subscales were 0.892, 0.853, 0.813, 0.775, and 0.807, respectively, meeting acceptable standards for empirical research.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data analysis\u003c/h2\u003e \u003cp\u003eThe questionnaire survey was administered with informed consent from both school teachers and students, conducted collectively by class. During survey administration, students were required to complete it independently within a 20-minute time limit, with the option to withdraw at any point. Questionnaires were collected immediately upon completion. Following the collection of data, the statistical software SPSS 26.0 and AMOS 24.0 were employed for the analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eDescriptive statistics for the primary variables revealed secondary vocational students\u0026rsquo; experiential patterns within perceived teacher-supported programming learning environments. The mean for perceived teacher support (PTS) was 4.08 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.60), indicating moderately high levels of perceived instructional support. Intrinsic load (IL) yielded a mean of 3.58 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80), suggesting students experienced moderate yet manageable cognitive burden when comprehending programming knowledge and task requirements. Extraneous load (EL) averaged 2.73 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.75), indicating relatively low unnecessary cognitive demands imposed by learning materials or instructional design, thereby reflecting clear and comprehensible content organization and presentation in current programming instruction. Germane load (GL) registered a mean of 3.22 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.73), indicating students invested moderate cognitive effort in integrating novel programming knowledge with existing cognitive structures, facilitating deep processing and knowledge transfer. Computer programming self-efficacy (CPSE) averaged 3.53 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.60), reflecting moderate confidence among secondary vocational students in their capability to complete programming tasks, alongside foundational motivation and psychological readiness for programming learning. Programming learning engagement (PLE) demonstrated a mean of 3.53 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.55), underscoring students\u0026rsquo; heightened concentration and participation willingness, with demonstrated willingness to invest time and effort in learning activities.\u003c/p\u003e \u003cp\u003ePearson correlation coefficients were computed to assess relationships among key variables (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PTS demonstrated a significant negative correlation with EL (\u003cem\u003er\u003c/em\u003e = -0.238, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and significant positive correlations with GL (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.341, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), CPSE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.467, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and PLE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.487, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). EL exhibited significant negative correlations with CPSE (\u003cem\u003er\u003c/em\u003e = -0.287, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and PLE (\u003cem\u003er\u003c/em\u003e = -0.230, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). GL showed significant positive correlations with CPSE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.635, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and PLE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.576, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). CPSE and PLE were significantly and positively correlated (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.649, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These correlation patterns aligned with theoretical expectations, establishing a foundation for subsequent mediation and moderation analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics and correlation analysis of each variable (n\u0026thinsp;=\u0026thinsp;310)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.PTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.084\u0026thinsp;\u0026plusmn;\u0026thinsp;0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.IL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.EL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.732\u0026thinsp;\u0026plusmn;\u0026thinsp;0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.238**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.551**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.GL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.225\u0026thinsp;\u0026plusmn;\u0026thinsp;0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.341**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.436**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.328**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.CPSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.531\u0026thinsp;\u0026plusmn;\u0026thinsp;0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.467**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.330**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.287**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.635**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.526\u0026thinsp;\u0026plusmn;\u0026thinsp;0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.487**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.203**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.230**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.576**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.649**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: ** indicates statistical significance at the 0.01 level (two-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Common method bias test\u003c/h2\u003e \u003cp\u003eThis study employed Harman\u0026rsquo;s single-factor test to assess common method bias. Exploratory factor analysis of all items yielded eight factors with eigenvalues exceeding 1. The largest factor accounted for 31.665% of the variance, below the 50% threshold (Podsakoff et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), indicating that common method bias was not a significant concern in this data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Confirmatory factor analysis\u003c/h2\u003e \u003cp\u003eConfirmatory factor analysis was conducted to assess the discriminant validity of variables, including perceived teacher support (PTS), extraneous load (EL), germane load (GL), computer programming self-efficacy (CPSE), and programming learning engagement (PLE) (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results indicated that the baseline model (five-factor model) demonstrated optimal fit, with all fit indices falling within acceptable ranges, suggesting satisfactory discriminant validity among the measurement instruments employed in this study. In contrast, the four-factor, three-factor, two-factor, and one-factor models exhibited substantially inferior data fit compared to the baseline model, further corroborating that common method bias was not a significant threat in the present research.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfirmatory factor analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/df\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Model (Five-factor): PTS, EL, GL, CPSE, PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFour-factor Model: PTS, EL\u0026thinsp;+\u0026thinsp;GL, CPSE, PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e641.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree-factor Model: PTS, EL\u0026thinsp;+\u0026thinsp;GL+CPSE, PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e792.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo-factor Model: PTS, EL\u0026thinsp;+\u0026thinsp;GL+CPSE\u0026thinsp;+\u0026thinsp;PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e989.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne-factor Model: PTS\u0026thinsp;+\u0026thinsp;EL+GL+CPSE\u0026thinsp;+\u0026thinsp;PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1165.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Reliability and validity test\u003c/h2\u003e \u003cp\u003eThe structural integrity of constructs in the research model was rigorously evaluated through reliability and validity testing, with the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The internal consistency of the subdimensions/items for each construct was assessed using Cronbach\u0026rsquo;s alpha (α) and Composite Reliability (CR). For the construct of perceived teacher support (PTS), α and CR were determined to be 0.880 and 0.834, respectively. For the construct of extraneous load (EL), α and CR were determined to be 0.825 and 0.840, respectively. For the construct of germane load (GL), α and CR were determined to be 0.875 and 0.876, respectively. For the construct of computer programming self-efficacy (CPSE), α and CR were determined to be 0.903 and 0.802, respectively. For the construct of programming learning engagement (PLE), α and CR were determined to be 0.892 and 0.819, respectively. All Cronbach\u0026rsquo;s α coefficients and CR values exceeded the acceptable threshold of 0.7, indicating satisfactory internal consistency among subdimensions/items for each construct and confirming that measurement reliability met empirical research standards.\u003c/p\u003e \u003cp\u003eConvergent validity was assessed through Standardized Loading (Std.), Average Variance Extracted (AVE), and the associated Z and \u003cem\u003ep\u003c/em\u003e-values. First, the standardized loadings for all subdimensions/items on their respective constructs ranged from 0.586 to 0.934, exceeding the 0.5 benchmark, with all loadings significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This indicates that each subdimension/item effectively represented the core meaning of its corresponding construct. Second, the AVE values for PTS (0.717), EL (0.643), GL (0.705), CPSE (0.507), and PLE (0.535) all surpassed the 0.5 minimum threshold, demonstrating strong convergent validity among subdimensions/items for each construct.\u003c/p\u003e \u003cp\u003eSummarily, the results of the reliability and validity tests provided robust support for the appropriateness of the measurement model. The high values of Cronbach\u0026rsquo;s α and CR for all constructs confirmed satisfactory consistency among subdimensions/items when measuring target constructs. The attainment of acceptable standardized loadings and AVE values further validated the representativeness of subdimensions/items for their respective constructs. These findings indicate that the measurement instruments employed in this study demonstrated satisfactory validity and reliability when assessing PTS, EL, GL, CPSE, and PLE within programming learning contexts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability and validity test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubdimensions\u003c/p\u003e \u003cp\u003e/Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnstd.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCPSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: *** indicates statistical significance at the 0.001 level (two-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Model fit test\u003c/h2\u003e \u003cp\u003eThis study employed various key fit indices to evaluate the correspondence between the theoretical model and observed data, with results summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Model fit constitutes the core criterion for determining whether a theoretical framework effectively represents sample data, directly influencing the credibility of subsequent path analysis results. The Chi-square statistic (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e), a conventional metric for evaluating model fit, yielded a value of 114.126. Upon normalization by degrees of freedom (\u003cem\u003edf\u003c/em\u003e), the resultant \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/df\u003c/em\u003e ratio was 2.717, falling below the recommended threshold of 3. This finding suggests that the model exhibited minimal deviation from the observed data, thereby indicating adequate fit. The additional fit indices obtained met the established criteria. RMSEA was 0.075, and SRMR was 0.052, both falling below the 0.08 critical value. CFI, IFI, and TLI were 0.957, 0.958, and 0.933, respectively, all exceeding the 0.90 acceptable benchmark.\u003c/p\u003e \u003cp\u003eThe convergence of these fit indices indicates that the proposed structural model demonstrated robust goodness-of-fit, with the theoretical framework and hypothesized relationships among variables receiving strong empirical support. This model effectively captures the mechanisms through which perceived teacher support operates within programming learning contexts and its influence pathways on learning engagement. It provides a reliable foundation for subsequent mediation and moderation analyses and interpretation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fit test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/df\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026gt;0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Path hypothesis test\u003c/h2\u003e \u003cp\u003eThis study employed Structural Equation Modeling (SEM) to examine the variable pathways specified in theoretical hypotheses, with detailed results presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This analysis clarified direct effect pathways among variables, providing empirical evidence for hypothesis verification.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, hypotheses H1, H2, H3, H6, H7, and H9 received empirical support, with all path relationships among variables reaching statistical significance. Specifically, perceived teacher support (PTS) demonstrated significant effects on programming learning engagement (PLE), extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE); germane load (GL) exerted a significant positive effect on computer programming self-efficacy (CPSE); and computer programming self-efficacy (CPSE) demonstrated a significant positive effect on programming learning engagement (PLE). These results confirm that perceived teacher support plays a facilitative role in promoting germane load and computer programming self-efficacy formation, while concurrently suppressing the generation of extraneous load.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePath hypothesis test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath Relationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficient (Unstd.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Error (S.E.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandardized Coefficient (Std.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTS\u0026rarr;PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTS\u0026rarr;EL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTS\u0026rarr;GL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEL\u0026rarr;PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGL\u0026rarr;PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTS\u0026rarr;CPSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCPSE\u0026rarr;PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEL\u0026rarr;CPSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGL\u0026rarr;CPSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: *** indicates statistical significance at the 0.001 level (two-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Mediation effect test\u003c/h2\u003e \u003cp\u003eTo clarify the indirect mechanisms through which extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE) operate between perceived teacher support (PTS) and programming learning engagement (PLE), this study employed the percentile bootstrap method and bias-corrected percentile bootstrap method proposed by Taylor et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), with bootstrap samples set at 5,000 and a 95% confidence interval. Following Preacher \u0026amp; Hayes\u0026rsquo;s (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) criteria, mediation effects were deemed significant if the upper and lower bounds of the confidence interval did not contain zero. The results of the mediation effect test, which encompass direct and total effects, are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. These results systematically reveal the pathways and internal logic through which factors influencing perceived teacher support operate within secondary vocational students\u0026rsquo; programming learning contexts.\u003c/p\u003e \u003cp\u003eAs indicated by the examination results, two significant indirect pathways were identified. First, the relationship between PTS and PLE was significantly mediated by CPSE. Second, GL and CPSE constituted a significant serial mediation pathway between PTS and PLE. These findings validated the sequential transmission effects of GL and CPSE, demonstrating that PTS can elevate PLE by enhancing students\u0026rsquo; GL, subsequently strengthening their CPSE. This study underscores the imperative of simultaneously attending to the cultivation of GL and CPSE in programming instructional practice, with integrated interventions targeting both constructs holding substantial value for promoting student learning engagement.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u0026nbsp; Mediation effects test results\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath relationship\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoint estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProduct of coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootstrapping\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBia-Corrected 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eInd1: PTS\u0026rarr;EL\u0026rarr;PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eInd2: PTS\u0026rarr;CPSE\u0026rarr;PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eInd3: PTS\u0026rarr;EL\u0026rarr;CPSE\u0026rarr;PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eInd4: PTS\u0026rarr;GL\u0026rarr;PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eInd5: PTS\u0026rarr;GL\u0026rarr;CPSE\u0026rarr;PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirect Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003ePTS\u0026rarr;PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003ePTS\u0026rarr;PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Multigroup analysis: gender\u003c/h2\u003e \u003cp\u003eThe objective of this study was to examine whether path relationships among variables in the theoretical model exhibited gender differences. A multi-group analysis was conducted with gender as the grouping variable. The male group (n\u0026thinsp;=\u0026thinsp;150) and the female group (n\u0026thinsp;=\u0026thinsp;160) were compared. The results of this study are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, and Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, discrepancies in fit indices between the two models indicated gender heterogeneity in the mechanisms through which perceived teacher support influences learning engagement within programming learning contexts. Subsequent path coefficient comparisons (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) revealed that the male group\u0026rsquo;s path relationships exhibited congruence with full-sample results, whereas the female group demonstrated three significant divergences. First, H1 (PTS\u0026rarr;PLE) yielded a positive path coefficient in the female group that failed to reach statistical significance. Second, H5 (GL\u0026rarr;PLE) manifested as a significant negative effect in the female group, contradicting both full-sample and male group results. Third, H10 (PTS\u0026rarr;EL\u0026rarr;PLE) was significant exclusively within the female group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultigroup analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eχ2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eχ2/df\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (n\u0026thinsp;=\u0026thinsp;160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultigroup analysis: path hypothesis test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePath Relationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTS\u0026rarr;PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTS\u0026rarr;EL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-4.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTS\u0026rarr;GL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEL\u0026rarr;PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGL\u0026rarr;PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTS\u0026rarr;CPSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCPSE\u0026rarr;PLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEL\u0026rarr;CPSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGL\u0026rarr;CPSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eNote: *** indicates statistical significance at the 0.001 level (two-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u0026nbsp; Multigroup analysis: mediation effects test results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath relationship\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoint estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eProduct of coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootstrapping\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoint estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eProduct of coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootstrapping\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBia-Corrected 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBia-Corrected 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInd1: PTS→EL→PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInd2: PTS→CPSE→PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInd3: PTS→EL→CPSE→PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInd4: PTS→GL→PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInd5: PTS→GL→CPSE→PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDirect Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePTS→PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePTS→PLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eGrounded in an integrative framework synthesizing social support theory, cognitive load theory, and social cognitive theory, this study systematically investigated the mechanisms through which perceived teacher support (PTS) influences programming learning engagement (PLE). It focused particularly on validating the serial mediating roles of extraneous load (EL), germane load (GL), and computer programming self-efficacy (CPSE), alongside gender moderation effects. These findings revealed multi-level psychological mechanisms. PTS not only directly facilitated programming learning engagement (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.105, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), but also exerted indirect effects through the single mediation pathway of CPSE (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.085) and the serial mediation pathway of GL and CPSE (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). Concurrently, PTS significantly reduced EL (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;0.337, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and enhanced GL (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.352, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the latter subsequently influencing PLE via elevated CPSE (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.453, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A notable finding was the identification of gender moderation effects. In the female group, the direct effect of PTS on PLE was non-significant. However, GL demonstrated a negative effect on PLE (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;0.135, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), and EL exhibited a significant mediating effect between PTS and PLE. Conversely, in the male group, the direct effect of PTS on PLE remained significant. Additionally, the serial mediation effect of GL and CPSE proved more pronounced.\u003c/p\u003e \u003cp\u003eThese findings provide multi-level interpretations of the internal mechanisms through which teacher support translates into learning behaviors within secondary vocational programming contexts. First, the direct effect aligns with the core tenets of social support theory. As students\u0026rsquo; subjective cognition and internalization of supportive experiences in programming learning environments, PTS effectively addresses students\u0026rsquo; psychological needs during highly demanding programming learning. This alleviates frustration and anxiety while transforming into sustained and profound motivation for learning participation. This finding is consistent with the results reported by Xu et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in the field of vocational education research, which support the stable positive association between perceived teacher support and learning engagement. Second, the mediation mechanism validates the sequential logic of \u0026ldquo;cognitive regulation \u0026mdash; psychological construction \u0026mdash; behavioral engagement.\u0026rdquo; The significant single mediation effect of CPSE conforms to Bandura\u0026rsquo;s (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) core proposition in social cognitive theory that external environmental support must be transformed through individuals\u0026rsquo; intrinsic psychological beliefs to drive behavioral change. Given secondary vocational students\u0026rsquo; characteristics of weak programming foundations and heterogeneous prior knowledge, teachers\u0026rsquo; positive feedback and targeted guidance constitute critical pathways for accumulating successful learning experiences (Karsten et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), thereby strengthening self-efficacy beliefs regarding programming task completion and ultimately promoting enhanced learning engagement. The serial mediation effect of GL and CPSE further reveals that teacher support facilitates the construction and integration of programming knowledge schemata by guiding students to invest moderate GL. Such deep cognitive participation substantially elevates self-efficacy and subsequently drives sustained increases in learning engagement. This aligns profoundly with the theoretical assertion that GL promotes deep processing of knowledge (Sweller, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Third, the differentiated effects of cognitive load reflect the actual state of secondary vocational programming education. The non-significant mediating effect of EL may be attributed to current programming instruction having effectively controlled EL through structured design (e.g., clear task instructions, visualized instructional materials), as evidenced by the mean EL of 2.73 in this study. With direct interference with learning engagement reduced to minimal levels, the mediating effect failed to manifest. Conversely, GL exerted no direct effect on PLE but operated exclusively through CPSE, indicating that the core value of GL lies in promoting knowledge construction and self-efficacy enhancement rather than directly driving learning participation behaviors. This concurs with Quintero-Manes \u0026amp; Vieira (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) regarding pathways of cognitive load effects in programming learning. Fourth, gender-differentiated moderation effects mirror cognitive characteristics and learning style variations among secondary vocational students. Males have been shown to exhibit superior performance in logical thinking and challenge acceptance (Meehan, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Cassar \u0026amp; Rigdon, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests that males may more readily derive accomplishment through GL guided by teacher support and subsequently strengthen self-efficacy. Therefore, the serial mediation effect is more pronounced in males. Within the female group, the significant mediating effect of EL alongside the non-significant direct effect of PTS on PLE may relate to the high abstraction and strong logical demands characteristic of programming learning. When PTS fails to effectively reduce EL, superfluous cognitive consumption occupies limited working memory resources (Sweller, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), weakening direct facilitative effects on PLE and necessitating the mediating pathway of EL reduction to create conditions for females to focus on core programming knowledge. Additionally, the negative effect of GL on PLE within the female group may stem from females facing relatively greater cognitive challenges in programming learning, with excessive investment in GL readily inducing cognitive depletion (Phan \u0026amp; Ngu, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and consequently diminishing learning participation willingness. It underscores the necessity of targeted research on gender differences against the backdrop of demographic shifts in vocational education.\u003c/p\u003e \u003cp\u003eThe major contribution of this study lies in constructing and validating, through multi-theoretical integration and empirical examination, a comprehensive mechanism of how perceived teacher support influences learning engagement within secondary vocational programming contexts. On the one hand, this study transcends prior limitations of predominantly examining bivariate variable relationships by incorporating perceived teacher support, cognitive load, and self-efficacy within a unified analytical framework. Its integration clarifies the sequential transmission pathway of \u0026ldquo;environmental support \u0026mdash; cognitive processing \u0026mdash; motivational beliefs \u0026mdash; learning behaviors.\u0026rdquo; On the other hand, this investigation clarifies the learning psychological mechanisms characteristic of their \u0026ldquo;high support dependency, low foundational starting point\u0026rdquo; profile by centering on secondary vocational students as a distinctive population. It enhances the explanatory power and contextual applicability of relevant theories within vocational education settings. Concurrently, this study provides empirical support for attending to individual differences in programming education, particularly addressing instructional adaptation needs amid demographic shifts in vocational education by specifying gender-differentiated moderation effects.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Implications\u003c/h2\u003e \u003cp\u003eThe theoretical and practical implications of this study operate at multiple levels. Theoretically, the findings support the development of cognitive-motivational integration models by incorporating environmental support variables into the cognitive load theoretical framework. This integration provides novel theoretical perspectives for the learning sciences. Future research may further examine this model\u0026rsquo;s adaptability in emerging technology courses such as data science and artificial intelligence, while exploring novel manifestations of teacher support within digitally intelligent instructional environments, such as intelligent pedagogical agents with capabilities encompassing knowledge-based responses, emotional support, and personalized guidance (Wu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, the identification of gender differences contributes to a more comprehensive understanding of individual differences in programming education, thereby establishing a theoretical foundation for subsequent investigations that focus on differential variables, such as gender and learning foundation.\u003c/p\u003e \u003cp\u003ePractically, this study provides precise instructional improvement strategies for secondary vocational programming education. First, curriculum design should adhere to the \u0026ldquo;cognitive scaffolding\u0026rdquo; principle. Initial tasks should employ visual programming tools and code templates to minimize EL; as students\u0026rsquo; skills develop, progressively adopt open-ended projects and interdisciplinary application problems to increase GL. Second, the cultivation of CPSE should be prioritized at the instructional core. The accumulation of students\u0026rsquo; successful experiences is facilitated through task decomposition, staged feedback, and modeling exemplars, particularly through the design of \u0026ldquo;small-step, rapid-progress\u0026rdquo; pathways to success tailored to secondary vocational students\u0026rsquo; weak foundational characteristics. Third, the implementation of gender-sensitive teacher support strategies is imperative. Female students should be provided with cognitive tasks that incorporate a gradual increase in difficulty, thereby enhancing their ability to regulate EL and prevent cognitive depletion from excessive cognitive investment. Conversely, male students should be given more exploratory tasks to stimulate active cognitive participation. It is essential to ensure that the effects of cognitive load regulation and perceived teacher support are synergistic. Fourth, the enhancement of programming instructional support systems is crucial. It is imperative to strengthen teacher training in support provision and cognitive load regulation capabilities, develop gender-sensitive instructional resources and task systems, and establish virtuous cycles of \u0026ldquo;support \u0026mdash; cognition \u0026mdash; motivation \u0026mdash; behavior\u0026rdquo; to promote sustained enhancement of student learning engagement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the cross-sectional design imposes limitations on causal inference. While the theoretical framework supports the existence of directional relationships among variables, experimental or longitudinal designs would provide more conclusive evidence. Future research may employ instructional intervention experiments to measure dynamic changes in student psychological variables before and after modifications in teacher support strategies. Second, the sample was restricted to a school in a specific region of China, which may have affected the generalizability of the results. The learning experiences of students enrolled in vocational schools are influenced by a variety of factors, including cultural contexts, educational policies, and industry demands. Future research should aim to expand the scope of sampling and conduct cross-regional and cross-cultural comparative studies to examine the robustness of the models. Third, this study did not adequately control for potential confounding variables such as prior programming experience and family background. These factors may moderate the effects of teacher support. Future research should construct more comprehensive predictive models incorporating these individual difference variables. Fourth, this study focused on short-term learning process variables without tracking long-term learning outcomes. Future research may establish complete evidence chains of \u0026ldquo;psychological mechanisms \u0026mdash; learning processes \u0026mdash; long-term outcomes,\u0026rdquo; such as tracking students\u0026rsquo; subsequent application of programming skills in specialized courses, thereby enhancing practical value.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEU \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEuropean Union\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUNESCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnited Nations Educational, Scientific and Cultural Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived Teacher Support\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProgramming Learning Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCognitive Load\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntrinsic Load\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtraneous Load\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGermane Load\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCPSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComputer Programming Self-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTeacher Emotional Support\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTeacher Academic Support\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLT \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Logical Thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlgorithm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDebug\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAE \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAgentic Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBehavioral Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEmotional Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCognitive Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStructural Equation Modeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study adhered to the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Hunan University of Science and Technology. Prior to data collection, all participants were fully informed of the purpose and procedures of the study. Completion of the questionnaire was deemed as informed consent to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, X.W., methodology, J.L., software, Y.W., validation, H.Z., data curation, Y.W. and Y.X.W., writing—original draft preparation, X.W. and Y.W., writing—review and editing, X.W. and Y.W., visualization, Y.W. and Y.X.W., supervision, X.W., J.L., and H.Z. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the individuals who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmdahem, A. (2023). \u003cem\u003eTeaching programming in the computer science strand of the 2014 National Curriculum for computing at key stage 4: Challenges, difficulties and prospects\u003c/em\u003e [Doctoral dissertation]. University of Wolverhampton]. http://hdl.handle.net/2436/625189\u003c/li\u003e\n\u003cli\u003eAlrashidi, O., \u0026amp; Alshammari, S. H. (2025). The effects of self-efficacy, teacher support, and positive academic emotions on student engagement in online courses among EFL university students. \u003cem\u003eEducation and Information Technologies\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(6), 8139\u0026ndash;8157. https://doi.org/10.1007/s10639-024-13139-3\u003c/li\u003e\n\u003cli\u003eAyvaz-Tuncel, Z., \u0026amp; Demir, O. (2024). The Mediating Role of Cognitive Load in the Relationship between Metacognitive Skills Perceptions and Problem-solving Skills in Pre-service Teachers. \u003cem\u003eInternational Journal on Social and Education Sciences\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4), 577\u0026ndash;599. https://doi.org/10.46328/ijonses.685\u003c/li\u003e\n\u003cli\u003eBandura, A. (1997). Self-efficacy: The exercise of control. Macmillan. \u003c/li\u003e\n\u003cli\u003eBennedsen, J., \u0026amp; Caspersen, M. E. (2019). Failure rates in introductory programming: 12 years later. \u003cem\u003eACM Inroads\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 30\u0026ndash;36. https://doi.org/10.1145/3324888\u003c/li\u003e\n\u003cli\u003eBerkman, L. F., \u0026amp; Syme, S. L. (1979). Social networks, host resistance, and mortality: a nine-year follow-up study of Alameda County residents. American journal of Epidemiology, 109(2), 186-204. https://doi.org/10.1093/aje/kwx103\u003c/li\u003e\n\u003cli\u003eBrewster, A. B., \u0026amp; Bowen, G. L. (2004). Teacher Support and the School Engagement of Latino Middle and High School Students at Risk of School Failure. \u003cem\u003eChild and Adolescent Social Work Journal\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 47\u0026ndash;67. https://doi.org/10.1023/B:CASW.0000012348.83939.6b\u003c/li\u003e\n\u003cli\u003eCassar, A., \u0026amp; Rigdon, M. L. (2021). Option to cooperate increases women\u0026rsquo;s competitiveness and closes the gender gap. \u003cem\u003eEvolution and Human Behavior\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(6), 556\u0026ndash;572. https://doi.org/10.1016/j.evolhumbehav.2021.06.001\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eDigital Education Action Plan: Policy background - European Education Area\u003c/em\u003e. (2025, November 18). https://education.ec.europa.eu/focus-topics/digital-education/plan\u003c/li\u003e\n\u003cli\u003eDong, A., Jong, M. S.-Y., \u0026amp; King, R. B. (2020). How Does Prior Knowledge Influence Learning Engagement? The Mediating Roles of Cognitive Load and Help-Seeking. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 591203. https://doi.org/10.3389/fpsyg.2020.591203\u003c/li\u003e\n\u003cli\u003eEvans, P., Vansteenkiste, M., Parker, P., Kingsford-Smith, A., \u0026amp; Zhou, S. (2024). Cognitive Load Theory and Its Relationships with Motivation: A Self-Determination Theory Perspective. \u003cem\u003eEducational Psychology Review\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(1), 7. https://doi.org/10.1007/s10648-023-09841-2\u003c/li\u003e\n\u003cli\u003eFeng, L., He, L., \u0026amp; Ding, J. (2023). The Association between Perceived Teacher Support, Students\u0026rsquo; ICT Self-Efficacy, and Online English Academic Engagement in the Blended Learning Context. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(8), 6839. https://doi.org/10.3390/su15086839\u003c/li\u003e\n\u003cli\u003eFredricks, J. A., Blumenfeld, P. C., \u0026amp; Paris, A. H. (2004). School Engagement: Potential of the Concept, State of the Evidence. \u003cem\u003eReview of Educational Research\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(1), 59\u0026ndash;109. https://doi.org/10.3102/00346543074001059\u003c/li\u003e\n\u003cli\u003eGuo, Q., Samsudin, S., Yang, X., Gao, J., Ramlan, M. A., Abdullah, B., \u0026amp; Farizan, N. H. (2023). Relationship between Perceived Teacher Support and Student Engagement in Physical Education: A Systematic Review. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(7), 6039. https://doi.org/10.3390/su15076039\u003c/li\u003e\n\u003cli\u003eHartelt, T., \u0026amp; Martens, H. (2024). Self-regulatory and metacognitive instruction regarding student conceptions: Influence on students\u0026rsquo; self-efficacy and cognitive load. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 1450947. https://doi.org/10.3389/fpsyg.2024.1450947\u003c/li\u003e\n\u003cli\u003eHe, L., Feng, L., \u0026amp; Ding, J. (2024). The Relationship between Perceived Teacher Emotional Support, Online Academic Burnout, Academic Self-Efficacy, and Online English Academic Engagement of Chinese EFL Learners. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(13), 5542. https://doi.org/10.3390/su16135542\u003c/li\u003e\n\u003cli\u003eHiver, P., Al-Hoorie, A. H., Vitta, J. P., \u0026amp; Wu, J. (2024). 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A study on the relationship between higher vocational students\u0026rsquo; perceived teacher support and learning engagement: The chain mediation of academic self-efficacy and professional commitment. \u003cem\u003eNurture\u003c/em\u003e, 17(4), 595\u0026ndash;606. https://doi.org/10.55951/nurture.v17i4.435 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Perceived teacher support, Programming learning engagement, Cognitive load, Computer programming self-efficacy, Serial mediation, Secondary vocational education","lastPublishedDoi":"10.21203/rs.3.rs-8966481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8966481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study integrates social support theory, cognitive load theory, and social cognitive theory to address a persistent pedagogical challenge: bridging the gap between programming\u0026rsquo;s high abstraction and logical rigor and vocational students\u0026rsquo; often weaker foundational skills, which typically leads to low engagement. Using structural equation modeling and multi-group analysis of survey data from 310 secondary vocational programming students, our findings revealed that perceived teacher support significantly and positively predicted programming learning engagement (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.105, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005); computer programming self-efficacy served as a significant single mediator (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.085); germane load and self-efficacy sequentially mediated the effect of teacher support (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042); gender moderation effects were significant, with direct and serial mediation effects pronounced in the male group, whereas for female students, the influence operated indirectly through reduced extraneous load, with germane load demonstrating a significant negative effect (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;0.135, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). Taken together, these findings clarify the core mechanism of \u0026ldquo;environmental support \u0026mdash; cognitive processing \u0026mdash; motivational beliefs \u0026mdash; learning behaviors\u0026rdquo;. The study provides an evidence-based framework for designing gender-differentiated instructional strategies, advancing both pedagogical effectiveness and equity in vocational STEM education.\u003c/p\u003e","manuscriptTitle":"Germane Cognitive Load and Programming Self-Efficacy as Mediators of Teacher Effects on Student Engagement in Vocational Programming Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 19:55:58","doi":"10.21203/rs.3.rs-8966481/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T09:09:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T00:06:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263891089950269717038611603642436119928","date":"2026-04-27T06:16:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T14:03:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199590829521339460616425531382347593521","date":"2026-04-25T00:08:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90307177306854328898440670058869339636","date":"2026-04-24T14:15:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298445340867994545883225674469275183493","date":"2026-04-01T14:12:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-30T10:29:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-03T05:44:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T03:21:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T03:20:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-02-25T10:10:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90857e75-a47d-4a16-b602-dc1ba968df6b","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T09:09:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T00:06:53+00:00","index":212,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T03:23:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 19:55:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8966481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8966481","identity":"rs-8966481","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}