Mobile Devices and Social-Emotional Development: School Engagement as Mediator and Its Compensation for Left-behind Children in China

Mobile Devices and Social-Emotional Development: School Engagement as Mediator and Its Compensation for Left-behind Children in China | 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 Mobile Devices and Social-Emotional Development: School Engagement as Mediator and Its Compensation for Left-behind Children in China Yunlei Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5675984/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Digital transformation has fundamentally reshaped educational landscapes and profoundly influences youth development post-COVID-19, particularly affecting vulnerable populations like left-behind children (LBC). This study investigates how adolescents' mobile device use influences their social-emotional learning (SEL) competencies through school engagement, examining differential patterns between LBC and non-LBC. Through structural equation modeling (SEM) of data from 405 junior secondary school students (201 LBC) in rural China, this study analyzed four device-related factors: usage frequency, attitudes, adult supervision, and technoference (technology interference). Results demonstrated that technology's developmental influence operated primarily through school engagement mediation, with technoference emerging as the primary risk factor (total effect = -0.341) and supervision showing consistent protective effects (total effect = 0.228). The mediation patterns demonstrated heightened intensity among LBC, particularly for technoference (β = -0.428 vs -0.261) and supervision (β = 0.293 vs 0.142), suggesting increased vulnerability coupled with enhanced responsiveness to protective factors. Despite family separation and LBC's higher mobile phone reliance (t = 2.166) and decreased SEL competencies (t = -2.701), school engagement exhibited compensatory effects by maintaining developmental trajectories. While behavioral engagement showed group differences, affective and cognitive dimensions remained stable, indicating that family separation primarily affects engagement manifestation rather than underlying educational connection. These findings advance understanding of how digital experiences transfer between home and school while highlighting educational institutions' potential compensatory role in stabilizing developmental trajectories for vulnerable populations. The findings advocate coordinated interventions to leverage technology's potential while supporting diverse family structures in increasingly digital learning environments. Mobile Devices School Engagement Social-Emotional Learning (SEL) Adolescent Development Left-Behind Children (LBC) Structural Equation Modeling (SEM) Figures Figure 1 Figure 2 1. Introduction The global COVID-19 pandemic accelerated children's reliance on mobile technologies for both learning and social connections. This shift has raised important questions about children's social-emotional development. As an increasing academic focus, social-emotional development refers to the abilities to experience, express, and manage emotions while establishing positive relationships with others [1]. In China, digital transformation is pronounced, with over 90% of minors accessing the internet primarily through mobile phones [2]. The widespread mobile device usage presents both opportunities and challenges for achieving educational equity. Left-behind children (LBC) represent a uniquely vulnerable population within this digital landscape. These children experience prolonged separation from one or both migrant parents, creating distinct developmental circumstances. With approximately 66 million LBC in rural China as of 2020 [3], it has become crucial to understand how their increased dependence on mobile devices for parent-child communication associates with their development. Identified as a vulnerable population by the United Nation requiring targeted support [4], LBC may experience both benefits and risks of mobile devices amplified by their distinct family dynamics. Digital transformation has fundamentally reshaped educational landscapes. School engagement has been recognized as a crucial factor for investigations, which refers to how students participate in, connect with, and invest in their learning [5]. These investigations become particularly important when child-parent relationships and parent-teacher connections heavily rely on digital communication technologies. While research has established broad associations between technology usage and developmental trajectories [6], significant gaps persist regarding how specific mobile device usage patterns relate to social-emotional learning (SEL) competencies while considering institutional functions, particularly among vulnerable populations like LBC. This study investigated the relationships among adolescents' spare-time mobile device usage patterns at home, school engagement, and SEL competencies, while comparing LBC and non-LBC populations. Four key aspects of mobile device usage were examined: frequency of mobile phone usage, children's attitudes toward devices, adult supervision of device usage, and technoference (technology-related interruptions in interpersonal interactions). Through this investigation, it was aimed to provide evidence-based insights for supporting diverse family structures in increasingly digital educational environments. 2. Literature Review 2.1 SEL in Digital Contexts Scholarly attention to children's social-emotional development has grown considerably within the field of developmental psychology. Though variously conceptualized across disciplines, Collaborative for Academic, Social, and Emotional Learning (CASEL) defines SEL as the process of developing and practicing core life skills that enable individuals to form healthy identities, regulate emotions, achieve goals, empathize with others, create positive relationships, and make responsible decisions [7]. Digital transformation has fundamentally altered how these competencies develop. While traditional SEL interventions have historically relied on face-to-face interactions and direct feedback [8], digital landscape introduces both opportunities and challenges. This transformation into digital learning environments even after the global pandemic prompted more intentional approaches to relationship-building, requiring innovative strategies to cultivate meaningful interpersonal connections within virtual spaces [9]. Recent Research has presented a nuanced picture of digital impacts on SEL. Some studies reveal extended screen time correlating with diminished psychological health [10], yet other research suggests that purposeful and mindful digital engagement can enhance SEL competencies through expanded social connections and emotional expression opportunities [11]. Cultural context significantly moderates these relationships, with evidence from 29 countries showing substantial variation in how intensive social media usage affects social-emotional well-being [11]. Despite growing research on digital impacts, understanding of how specific digital behaviors associate with SEL outcomes in Chinese cultural contexts remains limited, particularly for vulnerable populations. 2.2 Mobile Device Factors and Adolescent SEL Development This study examined four primary factors of mobile device usage patterns: mobile phone usage frequency, child attitudes, adult supervision, and technoference. Each plays a distinct role in adolescents' digital experiences and development. As a traditional focus, usage frequency of technologies shows intricate connections to developmental trajectories than previously assumed. Recent longitudinal research challenges simplistic assumptions about uniform digital impacts, uncovering varied temporal patterns across developmental windows [12]. Though leisure-time device behaviors influence academic engagement, usage intensity alone demonstrates inconsistent relationships with developmental outcomes [13]. A meta-analysis found negative associations between problematic social media patterns and well-being, while mere excessive usage showed non-significant relationships [14]. These findings suggest the need to examine factors beyond simplistic screen-time metrics. Child attitudes toward mobile devices emerge as consequential. Richter et al. [15] documented age-related differences in risk awareness, with older adolescents demonstrating more balanced perspectives compared to younger users' focus on social advantages. Such evolving attitudinal frames suggest that perspectives on mobile devices appear to shape usage habits, with those viewing devices primarily as learning tools exhibiting more regulated engagement and better academic performance [16]. Adult supervision represents a critical external factor in shaping adolescents' device usage. Research has evolved beyond simple supervision models toward active participation and guidance in content selection and usage patterns [17]. Recent studies emphasize rights-based approaches that prioritize guided participation over restrictive control [18]. Technoference—technology-related interruptions in interpersonal interactions [19]—has emerged as another significant factor. Research has documented negative associations between child-parent technology interference and both cognitive and social-emotional development [20]. Recent evidence shows that these effects persist through adolescence, with child-parent technoference contributing to problematic smartphone usage through pathways mediated by parent-child relationship quality among Chinese adolescents [21]. These factors offer a foundational understanding of adolescent digital experience. Collectively, previous findings suggest that qualitative aspects of device usage (attitudes, supervision, and technoference) may prove more crucial than mere usage frequency in shaping adolescent development, particularly SEL competencies, which need further detailed investigations. 2.3 School Engagement in Digital Era Digital integration has transformed school engagement while maintaining its multidimensional structure of behavioral participation, emotional connection, and cognitive investment [22]. In China, where digital education has rapidly expanded with 342 million online education users active in the first half of 2023 [23], these dimensions acquire new significance in technology-mediated learning environments. Research reveals distinct engagement trajectories in digital contexts. Studies identified differential patterns where digital tools enhanced cognitive engagement while potentially challenging behavioral engagement through increased distraction [24]. Post-COVID-19 transformations introduced new complexities, with senior high school students showing greater adaptability than junior high students in internal resilience and school engagement [25]. These digital engagement gaps appear particularly pronounced in rural China, where infrastructure limitations and varied family support create systematic disparities in students' ability to maintain engagement across learning spaces [26]. The home-school engagement interface has gained heightened significance. Evidence shows that home digital environments significantly predict school engagement, varying systematically by regional development level and family structure [27]. Cross-national research spanning 47 countries reveals that digital experiences at home create distinct transfer effects on school engagement, amplified in contexts of socioeconomic disparity [28]. The evolving nature of schooling in digital contexts, particularly in China, highlights the need to examine complex associations among digital engagement, adolescent social-emotional development, and school engagement. 2.4 Mediation Mechanisms in Digital Context Recent research emphasizes understanding specific pathways linking mobile device usage to developmental outcomes, with school engagement emerging as a crucial mediation mechanism. This mediation framework aligns with theoretical models suggesting that external influences typically operate through students' patterns of educational participation and investment [29; 30]. Studies reveal how environmental factors, including technological elements, affect student outcomes through their impact on school engagement and social-emotional development [31]. Research further demonstrated school engagement's role in mediating between family-related factors and social-emotional development [32]. The mediation patterns show systematic variation across populations and contexts, suggesting that mechanisms may operate differently based on family structure and cultural context [30]. While research has established school's mediating role between environmental factors and developmental outcomes, how digital contexts and behavioral might alter traditional engagement-outcome relationships remain unclear, particularly for vulnerable populations navigating digital engagement across different contexts. 2.5 LBC and Their Digital Experience LBC represent a unique demographic in China, referring to minors who remain in their household registration while one or both parents have migrated elsewhere for over six months [3]. This phenomenon emerged from China's rapid urbanization, creating a significant rural LBC population typically cared for by surrogate caregivers, most commonly grandparents. This arrangement creates unique challenges in child-parent communication, emotional bonding, and developmental support, particularly in an increasingly digital world [33]. Considering their circumstance, LBC face distinct challenges and opportunities in digital engagement. Meta-analysis reveals significantly higher rates of mobile phone addiction among rural LBC compared to non-LBC peers, particularly during adolescence [34, 35]. This vulnerability often stems from reduced parental supervision and increased reliance on digital communication for maintaining family connections. While digital devices offer connections to migrant parents, inadequate supervision often leads to problematic usage patterns [36]. This risk is particularly pronounced given that LBC typically experience weaker family cohesion and poorer social-emotional development [37]. Further, the quality of digital-mediated child-parent interactions stands out as a pivotal consideration, with regular, meaningful digital interactions associated with enhanced psychological adjustment among LBC [38]. Digital technologies can fulfill vital compensatory roles within geographically separated families. Wang and colleagues [39] identified how mobile phones facilitated emotion socialization in separated families, with digital communication maintaining child-parent emotional bonds across distances. Studies further elaborate that mobile phone "domestication", the process of integrating devices into family routines, can benefit LBC developmental trajectories when structured appropriately [40]. These findings highlight an urgent need for targeted interventions capable of amplifying technology's potential advantages while simultaneously minimizing associated risks for LBC populations. Effective support requires understanding both vulnerabilities and resilience factors unique to LBC's digital experiences, particularly where mobile technology serves as a primary medium for family connections [33]. LBC's unique circumstances in digital engagement and potential disadvantages in family and development outcomes suggest they may be particularly sensitive to both the benefits and risks of mobile devices. Considering governmental efforts on LBC in China and schools' role in mitigating family disadvantages [41], these considerations underscore the pressing need for empirical investigation into how LBC navigate digital experiences and educational engagement. 2.6 Theoretical Integration and Current Study Relevant theoretical frameworks require integration to fully capture associations between mobile devices and adolescent development through school engagement. Bronfenbrenner's [42] ecological systems theory, enhanced by Johnson and Puplampu's [43] techno-subsystem extension, illuminates how digital experiences transfer between contexts. This proves relevant where technology evolves in interactions across physically separated microsystems. Finn's [29] participation-identification model complements this by explaining how engagement patterns create self-reinforcing cycles of educational outcomes, offering insights into how digital behaviors may differently associate with developmental trajectories for vulnerable populations. Current literature reveals several critical gaps. First, while extensive research documents general associations between digital usage and development [10, 11], specific mechanisms between digital experiences and developmental outcomes remain unclear, particularly regarding spare-time device usage at home and school engagement. Second, despite growing recognition of LBC's unique challenges in the digital era [34-37], research has not fully explored how device usage patterns operate across different family contexts. Third, existing theoretical frameworks and validated measurements, largely developed in Western settings, require adaptation to understand circumstances in Chinese cultural contexts. This study examined three key questions: (1) What are the associations among adolescents' mobile device usage, their school engagement, and SEL competencies? (2) What are the mediation mechanisms of school engagement in the relationships between mobile device usage and adolescents' SEL competencies? (3) How do these mediation mechanisms differ between LBC and non-LBC? Building on theoretical frameworks and literature, this study proposed three sets of hypotheses: First, given the differential impacts of qualitative versus quantitative aspects of device usage, this study expects significant interrelations among mobile device usage, school engagement, and SEL competencies, with distinct associations for different device-related factors (H1a). Usage quality (attitudes, supervision and technoference) should show stronger correlations than usage quantity (usage frequency), with negative associations from technoference and positive associations from supervision (H1b). School engagement and SEL competencies should demonstrate significant positive associations (H1c). Second, following Finn's participation-identification model, school engagement should significantly mediate relationships between device-related factors and SEL competencies (H2a), with stronger indirect effects for technoference and supervision (H2b). Third, considering LBC's unique vulnerabilities in digital contexts, they may experience multidimensional disadvantages in spare-time device usage, school engagement and SEL competencies (H3a). Regarding SEL competencies, LBC may show greater sensitivity to digital and educational engagement, particularly for technoference and supervision (H3b). The mediation mechanisms likely differ between LBC and non-LBC, with more intense mediation effects through school engagement among LBC (H3c). 3. Methodology 3.1 Research Design and Sample Selection This study employed a quantitative cross-sectional design to investigate the relationships between mobile device usage, school engagement, and SEL competencies through hierarchical statistical approaches while examining group differences. Data collection occurred in 2024 at a junior secondary school in Hong'an County, Huanggang City, Hubei Province. Hubei Province is a region with one of China's largest migration and LBC populations, and Hong'an County shows typical characteristics of rural development with comprehensive digital infrastructure in the region [44; 45]. The sampling process employed a two-stage cluster random sampling approach. First, one school was randomly selected from three junior secondary schools in Hong'an County using SPSS 25.0. Second, entire classrooms were randomly chosen as clusters to maintain the natural classroom environment and minimize disruption. Invalid responses (n = 63) were excluded based on three criteria: (1) logically inconsistent responses to reverse-coded items, (2) uniform response patterns indicating insufficient engagement, and (3) invalid response time ( 45 minutes). The final sample comprised 405 students (validity rate: 86.5%), including 201 LBC according to their official definition [3], and 204 non-LBC. Gender distribution showed 227 girls (56%) and 178 boys (44%), aged 11-16 years. This study focused on adolescence because this developmental period coincides with first-time personal device ownership and involves sufficient cognitive abilities for survey participation. Further, adolescence represents the second major window of brain development handling executive function and emotional control and is critical for identity formation and social cognition development [46]. 3.2 Measures and Instruments The study examined adolescents' spare-time mobile device usage patterns that were mainly child-determined and occurred at home. The four key device-related factors were: usage frequency (screen time of mobile phones), child attitudes (three items assessing device utility perspectives), supervision (two items examining adult oversight), and technoference (three items measuring technology-related interruptions, adapted from McDaniel and Coyne [19]). School engagement was measured using a ten-item scale adapted from the Student Engagement in Schools Questionnaire (SESQ) [47], examining affective (three items), behavioral (four items), and cognitive engagement (three items). SEL competencies were assessed through a 25-item scale adapted from Social Skills Improvement System Social Emotional Learning Brief Scales-Student Form (SSIS SELb-S) [48], measuring self-awareness, self-management, social awareness, relationship skills, and responsible decision-making. Additional data included mobile device types, usage activities (learning, communication, entertainment, and parent-teacher connection), and demographic information (gender, age, family income, and migration status). This comprehensive approach provided a better understanding of participants' circumstances, particularly for LBC. All scales employed 5-point Likert formats (1 = strongly disagree, 5 = strongly agree) and demonstrated strong internal consistency (school engagement: α = 0.91; SEL: α = 0.89). Cultural adaptation included translation-back-translation procedures and pilot testing with 45 students to ensure contextual appropriateness. 3.3 Data Collection Procedures After obtaining institutional approval and consent from the principal, teachers, participating students, and their guardians, data collection employed a dual-mode strategy during regular school hours. Both online and offline questionnaire options used identical content and administration procedures to accommodate student preferences and technological access. Teachers were trained in standardized administration procedures to provide instructions while maintaining participant confidentiality through an anonymous response collection. Online responses utilized a secure digital platform (Wenjuanxing) with data encryption and secure storage. 3.4 Analytical Strategy The analytical approach proceeded through several systematic phases. First, initial screening examined missing values, outliers, and variable distributions using SPSS 25.0. Preliminary correlation analyses investigated relationships among key study variables (device-related factors, school engagement, and SEL competencies) to address Research Question 1 and provide a foundation for subsequent SEM analysis. Confirmatory factor analysis (CFA) validated measurement models by examining factor loadings, composite reliability (CR; acceptable fit ≥ 0.70), and average variance extracted (AVE; acceptable fit ≥ 0.50). Model evaluation employed multiple fit indices, including Root Mean Square Error of Approximation (RMSEA; acceptable fit ≤ 0.08), Goodness-of-Fit Index (GFI; acceptable fit ≥ 0.90), Comparative Fit Index (CFI; acceptable fit ≥ 0.95), following Kline's guidelines [49]. Next, employing Structural Equation Modeling (SEM) approach, this study tested four models examining specific device-related factors (usage frequency of mobile phones, child attitudes, supervision, and technoference) using Amos 24.0. Figure 1 presents the primary analytical model. Given the substantial sample size (n=405) exceeding the recommended minimum ratio of 20:1 for observed variables to cases [49], this study employed a parametric method in SEM approach for estimating effects. This approach assessed structural pathways and effect sizes (direct, indirect, and total) while examining school engagement's mediating role. After completing the SEM analysis of the total sample, independent samples t-tests were conducted to identify group differences between LBC and non-LBC across multiple domains. Subsequent group-specific SEM analyses investigated whether the established patterns varied between LBC and non-LBC populations, with particular attention to path coefficients and model fit indices across the two groups. 4. Results 4.1 Preliminary Analysis 4.1.1 Demographic Information of the Sample As shown in Table 1, the sample was balanced between LBC (n = 201, 49.6%) and non-LBC (n = 204, 50.4%) with comparable gender distributions. Among LBC families, fathers constituted the primary migrant parents (97.5%), highlighting the gendered nature of rural Chinese labor migration. Most participants (93.086%) came from middle-low-income families, reflecting typical rural socioeconomic conditions. Table 1 Demographic Information of the Sample Characteristics LBC Non-LBC Total Sample n % n % n % Gender Female 116 57.711 111 54.412 227 56.049 Male 85 42.289 93 45.588 178 43.951 Age 11-13 156 77.612 144 70.588 300 74.074 14-16 45 22.388 60 29.412 105 25.926 Family Income High-middle 19 9.453 9 4.412 28 6.914 Middle-low 182 90.547 195 95.588 377 93.086 Migration Migrant Father 196 97.512 0 0.000 196 48.395 Migrant Mother 53 26.4368 0 0.000 53 13.086 Both Migrant 48 23.9881 0 0.000 48 11.852 Non-migrant 0 0.000 204 100.000 204 50.370 Total 201 100.000 204 100.000 405 100.000 LBC = Left-behind Children; Non-LBC = Non-left behind Children Mobile phones emerged as the primary device used by participants (M = 3.200, SD = 1.004), exceeding usage of smart watches (M = 1.470, SD = 1.013) and tablets (M = 1.500, SD = 0.935). Device activities demonstrated balanced distribution across educational (M = 2.920, SD = 0.990), communication (M = 2.810, SD = 1.131), and entertainment activities (M = 2.510, SD = 1.002). Parental connections with school teachers through digital means showed substantial presence (M = 3.29, SD = 0.938). 4.1.2 Correlation Analysis of Key Variables Table 2 presents descriptive statistics and correlations among variables. Regarding device usage patterns, technoference demonstrated strong correlations with other three device-related factors, particularly child attitudes (r = 0.577, p < 0.01). Supervision showed consistent negative associations with use frequency (r = -0.108, p < 0.05) and technoference (r = -0.137, p < 0.01). Table 2 Descriptive Statistics and Correlations Variable M SD 1 2 3 4 5 6 Frequency 3.200 1.004 - Attitudes 7.116 2.510 0.181** - Supervision 7.319 1.629 -0.108* -0.073 - Technoference 7.215 2.257 0.276** 0.577** -0.137** - SEL 90.943 11.774 -0.099* -0.151** 0.218** -0.327** - SE 37.254 6.712 -0.123* -0.254** 0.318** -0.342** 0.737** - Gender - - -0.103* -0.059 0.025 -0.014 -0.031 -0.014 N = 405. SE = School Engagement; SEL = Social-emotional Learning. Gender was coded as 0 = Female, 1 = Male. **Correlation is significant at the 0.01 level (2-tailed); *Correlation is significant at the 0.05 level (2-tailed). Development-related variables exhibited robust interconnections, with school engagement and SEL competencies strongly correlating (r = 0.737, p < 0.01). Technoference showed the strongest negative correlations with both outcomes (SEL: r = -0.327; school engagement: r = -0.342, p < 0.01), while supervision presented consistent positive correlations (SEL: r = 0.218; school engagement: r = 0.318, p < 0.01). Gender showed weak correlations with all key study variables (r < 0.15), justifying its exclusion as a covariate in subsequent SEM analyses. 4.2 SEM Analysis of Total Sample 4.2.1 Normality Tests Prior to SEM analysis, normality tests confirmed that all variables met the statistical assumptions required for maximum likelihood estimation (Table 3). Variables showed acceptable skewness (ranging from -0.229 to 0.422) and kurtosis (ranging from -0.794 to 1.024) values. The critical ratios for most variables fell within ±2.58, except for self-awareness, which showed a slightly higher kurtosis critical ratio (2.963). The sample size (n=405) exceeded the recommended minimum ratio of 20:1 for observed variables to cases [49], further supporting the appropriateness of the SEM approach with a parametric method. Table 3 Skewness and Kurtosis Values of Study Variables Indicator Skewness C.R. Kurtosis C.R. Device-related Factors Frequency 0.095 0.548 -0.128 -0.370 Child Attitudes 0.033 0.189 0.299 0.865 Supervision -0.079 -0.459 0.178 0.516 Technoference 0.300 1.735 0.410 1.187 SEL Self-awareness 0.154 0.892 1.024 2.963 Self-management 0.422 2.441 0.322 0.931 Social Awareness 0.165 0.954 -0.736 -2.129 Relationship Skills -0.229 -1.326 -0.794 -2.297 Responsible Decision-Making 0.019 0.113 0.649 1.878 SE Affective -0.209 -1.211 -0.192 -0.556 Behavioral -0.059 -0.342 -0.762 -2.206 Cognitive 0.096 0.557 -0.058 -0.169 N= 405. SE= School Engagement; SEL= Social-emotional Learning. 4.2.2 Measurement Model Validation CFA demonstrated robust measurement quality across all constructs. Factor loadings for SEL competencies (ranging from 0.689 to 0.810) and school engagement (ranging from 0.770 to 0.900) exceeded conventional thresholds. All measurement models exhibited strong reliability (school engagement: CR = 0.860; SEL: CR = 0.866) and satisfactory convergent validity (school engagement: AVE = 0.673; SEL: AVE = 0.564). 4.2.3 Structural Model Results All four structural models demonstrated acceptable fit indices (Table 4), with the mobile phone use frequency model showing particularly strong fit (χ² = 28.059, p = 0.138, RMSEA = 0.029, GFI = 0.984, CFI = 0.996). Table 4 Model Fit Indices Across Four Models Model χ² p df RMSEA GFI CFI X1: Frequency 28.059 0.138 21 0.029 0.984 0.996 X2: Child Attitudes 80.423 0.000 21 0.084 0.959 0.970 X3: Supervision 64.208 0.000 21 0.071 0.966 0.978 X4: Technoference 41.920 0.004 21 0.050 0.977 0.989 N= 405. Mode1: X1 = Frequency; Model 2: X2 = Child Attitudes; Model 3: X3 = Supervision; Model 4: X4 = Technoference. RMSEA= Root Means Square error of Approximation; GFI= Goodness-of-Fit Index; CFI= Comparative Fit Index; TLI= Tucker-Lewis Index. SEM analysis revealed consistent patterns of full mediation across models, with school engagement serving as the primary mechanism linking device-related factors to SEL competencies (Fig. 2). Table 5 presents standardized effect size among variables. Table 5 Standardized Total, Direct, and Indirect Effects Across Four Models Model Outcome: SEL Outcome: SE Predictor: Device Predictor: SE Predictor: Device Total Direct Indirect Only Direct Path Only Direct Path X1: Frequency -0.098 0.011 -0.108 0.842 -0.129 X2: Child Attitudes -0.167 0.051 -0.217 0.854 -0.255 X3: Supervision 0.228 -0.065 0.293 0.862 0.339 X4: Technoference -0.341 -0.045 -0.296 0.824 -0.360 N= 405. Mode1: X1 = Frequency; Model 2: X2 = Child Attitudes; Model 3: X3 = Supervision; Model 4: X4 = Technoference. SE= School Engagement; SEL= Social-emotional Learning. Total effects= direct effects + indirect effects. Technoference emerged as the strongest negative factor (total effect = -0.341), with a full mediation pattern operating entirely through its negative pathway to school engagement (β = -0.360). Child attitudes showed medium negative effects (total effect = -0.167), similarly manifesting through decreased school engagement (β = -0.255). Supervision showed consistent positive effects (total effect = 0.228) through enhanced school engagement (β = 0.339). Usage frequency showed the weakest total effect (-0.098), suggesting usage quantity may be less crucial than other quality factors. No significant direct pathways emerged from device-related factors to SEL competencies, while pathways from school engagement to SEL remained strong and stable across all models (β ranging from 0.824 to 0.862). These findings, visualized in Fig. 2(a-d), demonstrate that school engagement fully mediates the relationship between device-related factors and SEL competencies. 4.3 Group Comparison Analysis 4.3.1 Initial Group Differences Independent samples t-tests revealed distinct patterns between LBC and non-LBC (Table 6). Despite comparable age and gender distributions, LBC and non-LBC showed significant differences in several key areas. Regarding digital engagement, LBC exhibited higher mobile phone usage frequency (t = 2.166, p < 0.05) and perceived less device-based parent-teacher connection (t = 2.081, p < 0.05). However, both groups showed similar patterns in device-related attitudes, adult supervision, and technoference, with balanced distribution of digital activities across learning, communication, and entertainment purposes. For developmental outcomes, LBC exhibited significantly lower SEL competencies overall (t = 2.701, p < 0.01), particularly in self-management and responsible decision-making. Although overall school engagement levels were comparable, LBC showed lower behavioral engagement specifically (t = 2.012, p < 0.05). Table 6 Results of T-test on Variables Between LBC and Non-LBC Variable LBC (n=201) Non-LBC (n=204) t MD M SD M SD Device-related Factors Frequency 3.300 0.996 3.09 1.003 -2.166* 0.215 Attitudes 7.338 2.351 6.897 2.646 -1.773 0.441 Supervision 7.234 1.606 7.402 1.651 1.039 -0.168 Technoference 7.304 2.120 7.128 2.387 -0.785 0.176 SEL 89.363 11.465 92.500 11.894 2.701** -3.137 Self-awareness 17.368 2.656 17.730 2.634 1.378 -0.362 Self-management 15.378 3.243 16.544 3.189 3.648*** -1.166 Social Awareness 18.955 2.974 19.529 2.910 1.964* -0.574 Relationship Skills 20.304 2.965 20.441 3.069 0.459 -0.138 Responsible Decision-making 17.358 2.779 18.255 2.927 3.161** -0.897 SE 36.602 7.151 37.897 6.201 1.948 -1.295 Affective 11.249 2.492 11.578 2.317 1.379 -0.330 Behavioral 14.766 3.076 15.348 2.737 2.012* -0.582 Cognitive 10.587 2.335 10.971 2.168 1.713 -0.384 Device Type Mobile Phone 3.300 0.996 3.090 1.003 -2.166* 0.215 Smart Watch 1.510 1.040 1.430 0.988 0.755 0.076 Pad 1.500 0.901 1.500 0.970 0.079 0.007 Others 1.290 0.859 1.200 0.636 1.298 0.097 Device Activities Communication 2.860 1.133 2.770 1.131 -0.722 0.081 Education 2.930 1.008 2.910 0.976 -0.238 0.023 Entertainment 2.500 1.025 2.510 0.980 0.173 -0.017 Parent-teacher 3.190 0.909 3.390 0.958 2.081* -0.193 D emographics Gender - - - - -0.668 - Age 13.224 0.587 13.338 0.799 0.102 0.114 N = 405. LBC = Left-behind Children; Non-LBC = Non-left behind Children; SE = School Engagement; SEL = Social-emotional Learning. Gender was coded as 0 = Female, 1 = Male. MD (Mean Difference) = M(LBC) - M (Non-LBC). *p < .05, **p < .01, ***p < .001. 4.3.2 Group-specific SEM Analysis Model fit indices varied systematically between groups (Table 7), with non-LBC showing superior fit for usage frequency (RMSEA = 0.062 vs 0.078) and technoference models (RMSEA = 0.059 vs 0.098), while the supervision model demonstrated a better fit for LBC (RMSEA = 0.087 vs 0.109). Analysis revealed distinct mediation patterns between groups (Table 7). LBC exhibited higher standardized total effects across all models, with pronounced differences in supervision (0.293 vs. 0.142) and technoference (-0.428 vs. -0.261). Usage frequency showed significant pathways only among LBC, operating entirely through school engagement (β = -0.167). Child attitudes showed partial mediation among LBC with both significant direct and indirect pathways through school engagement, whereas non-LBC exhibited full mediation solely through the school engagement pathway. The supervision model demonstrated full mediation for LBC with effects entirely through school engagement (β = 0.299) but partial mediation for non-LBC, with competing effects: a positive indirect pathway through school engagement (β = 0.396) alongside a negative direct pathway to SEL (β = -0.201). The technoference model demonstrated full mediation for both groups, with stronger mediation among LBC (β = -0.424) compared to non-LBC (β = -0.305). The school engagement → SEL pathways demonstrated consistently stronger regression weights for LBC across all models (β ranging from 0.872 to 0.934, p<0.001) compared to non-LBC (β ranging from 0.776 to 0.867, p<0.001). Moreover, LBC showed superior coefficients on all device-related factors → school engagement pathways except for supervision, underscoring how digital-developmental associations systematically differ across family structures. Table 7 Results of Group-specific Analyses Between LBC and Non-LBC Across Four Models Model/ Pathway LBC Non-LBC Model 1: X1 = Frequency RMSEA = 0.078 RMSEA = 0.062 Use Frequency → SE -0.167* -0.072 Use Frequency → SEL 0.029 0.022 SE → SEL 0.900*** 0.789*** Total Effects on SEL -0.121 -0.035 Model 2: X2 = Child Attitudes RMSEA = 0.106 RMSEA = 0.101 Child Attitudes → SE -0.309*** -0.196* Child Attitudes → SEL 0.115* 0.027 SE → SEL 0.934*** 0.789*** Total Effects on SEL -0.174 -0.127 Model 3: X3 = Supervision RMSEA = 0.087 RMSEA = 0.109 Supervision → SE 0.299*** 0.396*** Supervision → SEL 0.029 -0.201** SE → SEL 0.885*** 0.867*** Total Effects on SEL 0.293 0.142 Model 4: X4 = Technoference RMSEA = 0.098 RMSEA = 0.059 Technoference → SE -0.424*** -0.305*** Technoference → SEL -0.058 -0.025 SE → SEL 0.872*** 0.776*** Total Effects on SEL -0.428 -0.261 SE = School Engagement; SEL = Social-emotional Learning. LBC = Left-behind Children (n = 201); Non-LBC = Non-left behind Children (n = 204). *p < .05, **p < .01, ***p < .001. Path coefficients are standardized. 5. Discussion This investigation revealed systematic associations between mobile device usage patterns and adolescents' SEL competencies, with school engagement functioning as a key mediation mechanism that demonstrates distinct patterns between left-behind and non-left-behind families. Three major findings emerged: (1) different device-related factors showed varied associations with developmental outcomes, with technoference emerging as most detrimental while supervision offered consistent protection; (2) school engagement consistently and fully mediated the relationships between device-related factors and SEL competencies; and (3) these associations demonstrated stronger patterns among LBC, coupled with more robust school engagement mediation effects, suggesting both their heightened sensitivity to digital experiences and school's enhanced compensatory role in supporting this vulnerable population. 5.1 Patterns of Associations: Mobile Device Factors, School Engagement, and SEL Competencies Different mobile device factors relate to adolescents' developmental outcomes, both school engagement and SEL competences, in distinct ways. Examining specific aspects of digital engagement provided several key insights. The modest association between usage frequency and SEL competencies supports prioritizing quality over quantity in digital engagement. Simple screen time metrics appear less crucial than interaction quality in understanding technology's relationship with adolescent social-emotional outcomes [6, 12]. The balanced distribution across educational, communication, and entertainment activities in this study further indicates that usage patterns, rather than duration of digital activities, shape developmental trajectories. Technoference emerged as the primary risk factor, operating almost exclusively through educational engagement pathways, which extends understanding of how digital interruptions correlate to individual development. Its strong correlations with other device-related factors, particularly child attitudes, indicate that technology interruption behaviors are embedded in broader digital engagement patterns. While previous research documented technoference's negative impact on child-parent relationships and cognitive development [20], this study reveals its close associations with and through educational engagement pathways. This also extends Shao et al.'s [21] work by demonstrating how digital interruptions affect development through institutional mechanisms. Supervision showed a consistent protective role in both school engagement and SEL competencies, supporting approaches that emphasize guided participation over control [18]. The negative correlations between supervision and both usage frequency and technoference suggest that effective oversight relates to more regulated usage patterns and shields against disruptive technology behaviors. This finding aligns with Tang et al.'s [40] study while revealing specific mechanisms through which this protection operates. Child attitudes toward mobile devices exhibited complex relationships with developmental outcomes. These patterns suggest the need for nuanced approaches to foster balanced technology perspectives rather than simple restrictions, extending Park and Lee's [16] work by illuminating how child's technology perspectives shape development through educational pathways. 5.2 School Engagement as Mediation Mechanism School engagement consistently emerged as the primary mediator between digital behaviors and social-emotional development. This finding can be theoretically grounded in Finn's [29] participation-identification model, which argues that engagement creates self-reinforcing cycles of academic and social-emotional outcomes. This study extends this theoretical framework to the digital era by demonstrating how technology-related factors may influence these cycles through students' educational participation patterns. The SEM results provide clear evidence for full mediation across all device-related factors, with significant indirect pathways and no significant direct pathways, indicating that the relationships between digital usage patterns and SEL competencies operate entirely through their associations with students' educational engagement. This finding extends Bronfenbrenner's [42] ecological systems theory by demonstrating how digital experiences relate to developmental outcomes through immediate environments such as school, while challenging Johnson and Puplampu's [43] techno-subsystem framework that positions digital technologies between microsystem and individual. The findings suggest that digital engagement manifests through complex pathways within broader contexts rather than functioning directly on individual development. Mediation effects varied across different device-related factors, with stronger effects for interaction quality factors (technoference and supervision) than for usage metrics. This pattern aligns with Bergdahl et al.'s [24] findings while revealing specific transfer mechanisms. Educational engagement appears to respond more strongly to factors shaping interaction quality than to mere device exposure, a crucial distinction for understanding technology's role in development. 5.3 Differential Patterns Between LBC and Non-LBC Mobile devices correlated with educational engagement and SEL competencies differently across family structures. Prior research has documented LBC's weaker family dynamics and heightened vulnerability to problematic device usage [35, 37], and this study further uncovered distinct digital engagement patterns among LBC. LBC showed higher mobile phone usage coupled with reduced device-based parent-teacher communication, yet maintained comparable supervision levels, suggesting distinct digital needs in separated families and compensatory oversight from extended family networks [40]. LBC's digital engagement patterns provide critical context for understanding the differential associations between device factors and developmental outcomes for this vulnerable group. The decreased SEL competencies and amplified effects found among LBC, particularly regarding technoference and supervision, highlight fundamental reconfiguration of technology's role in separated families. These patterns enrich Boss et al.'s [50] family stress theory by demonstrating how geographic separation contributes to both vulnerabilities and resilience in digital contexts. The theory proposes that when families experience boundary ambiguity due to physical separation, family members develop heightened sensitivity to both stressors and support mechanisms—a prediction that aligns with the findings of amplified effects among LBC for both technoference and supervision. The nearly doubled negative effect of technoference among LBC suggests heightened consequences of digital interruptions when physical interaction opportunities are limited. Digital engagement patterns appear to shape both vulnerability and resilience among LBC. While they showed greater vulnerability to negative digital engagement, LBC also demonstrated enhanced benefits from supervision, revealing digital vulnerability alongside resilience. This dual pattern advances Wang et al.'s [39] digital compensation framework by showing how technology's multi-dimensional functions operate through specific educational mechanisms. The finding that supervision benefits manifest primarily through school engagement among LBC, versus direct and indirect effects for non-LBC, suggests fundamentally different mechanisms through which digital oversight supports development in separated families. Regarding school engagement, the stability of affective and cognitive engagement dimensions across groups reflects the fundamental human need for belonging and achievement that persist regardless of family structure [30, 38]. Meanwhile, the lower behavioral engagement among LBC likely represents an adaptation to family separation, where reduced parental physical envelopment affects visible behavioral patterns without compromising underlying psychological investment. These patterns extend Wang and Degol's [31] work by suggesting family separation primarily affects behavioral manifestation of engagement rather than underlying educational connection, aligning with Martinez-Yarza et al.'s [32] findings on engagement's compensatory role in educational resilience. These findings suggest that schools may play a crucial role in stabilizing developmental trajectories for LBC's social-emotional well-being, particularly in digital contexts where technology mediates both educational and family connections. Meanwhile, the lower behavioral engagement among LBC calls for behavior-targeted interventions with cooperative efforts from various stakeholders. 5.4 Implications and Recommendations 5.4.1 Theoretical Implications This study advances the understanding of technology's role in development across multiple frameworks. The consistent full mediation pattern extends ecological systems theory [42] by illuminating how digital experiences transfer between individual, microsystems, and family-school contexts, while differential patterns between groups demonstrate technology's role in non-traditional family structure. Meanwhile, this study challenges the techno-subsystem framework [43] by revealing that digital factors operate primarily through broader contextual pathways rather than directly on development. The findings also advance participation-identification theory [29] by showing how digital behaviors reshape engagement-outcome relationships. The stability of measurement models across groups, coupled with distinct mediation patterns, supports the generalizability of these theoretical extensions while demonstrating differences in how engagement mechanisms operate based on family structure. Furthermore, this research extends the digital compensation framework [39] through the identification of specific institutional engagement pathways in separated families, illuminating how compensation operates through institutional engagement. These theoretical extensions prove significant insights as educational systems navigate post-pandemic digital integration. 5.4.2 Practical and Policy Implications These findings advocate for coordinated interventions across multiple stakeholders. At the family level, interventions should prioritize managing technoference and enhancing supervision effectiveness, particularly given LBC's sensitivity to digital interruptions. Supervision operated through enhanced educational engagement, particularly among LBC group, suggesting the importance of equipping extended family networks with effective digital oversight strategies and relevant social support programs from communities. Considering the risks of technoference, educational institutions should implement targeted digital literacy programs that address technology interruptions while fostering positive school engagement. These initiatives should strengthen home-school partnerships in ways that leverage technology's benefits while mitigating risks, with differentiated support systems recognizing distinct vulnerabilities. At the policy level, educational frameworks require adaptation to better integrate digital family engagement strategies, particularly for separated families. Guidelines should recognize different family structures while establishing systems which can reinforce schools' compensatory function for vulnerable populations. In the post-COVID-19 context, these frameworks should evolve to address both immediate digital integration needs and long-term educational equity considerations. Given the differential patterns in digital behaviors, developmental outcomes, and mediation mechanism across family structures, comprehensive family monitoring strategies should be implemented to support vulnerable populations. Success requires coordinated efforts through both technical and social interventions, emphasizing positive educational engagement rather than merely controlling device usage. 5.5 Limitations and Future Research There are several limitations which frame these findings and suggest directions for future study. First, cross-sectional design cannot establish definitive causality, despite the efforts of strengthening inferences with theoretically grounded hypotheses and hierarchical statistical approaches. Future longitudinal investigations should track how device-school-development relationships evolve over time, particularly for adaptation patterns in separated families. Second, the statistical analysis, while sophisticated, necessarily simplified complex family processes and technology interactions, with school engagement as the sole mediator. Future studies should more comprehensively examine the complex interplay between different contextual domains, exploring additional factors, such as family dynamics, as potential mechanisms while examining how multi-domain factors impact digital engagement patterns across different family structures. Third, the reliance on self-reported measures may introduce response bias, particularly regarding sensitive perspectives and technology usage patterns. Future research should incorporate objective measures for capturing the dynamic nature of technology-mediated experiences, including digital behavior and culturally sensitive assessments in school and family. Fourth, the sample's specificity to one region of rural China and focus on junior secondary students may limit generalizability. Cross-cultural studies should examine how these relationships manifest across different contexts, particularly investigating how cultural values influence technology developmental associations through engagement mechanisms. Conclusion This study reveals three key insights into how mobile devices associate with adolescent SEL competencies through school engagement across different family structures. First, supervision demonstrated consistent protective associations with developmental outcomes, while technoference emerged as the primary risk factor, both operating through school engagement. These findings challenge screen-time paradigms by highlighting interaction quality over usage quantity. Second, the consistent full mediation patterns through school engagement across various device-related factors indicate that digital experiences correlate with development primarily through their associations with educational participation and investment. Third, the mediation effects showed greater intensity among LBC, particularly for supervision and technoference, suggesting increased vulnerability coupled with enhanced responsiveness to protective factors. While behavioral engagement showed group differences, affective and cognitive dimensions remained stable, indicating that family separation primarily affects the manifestation of school behavior rather than underlying educational connection and quality. Given the findings of LBC's lower SEL competencies, alongside significant associations between device usage patterns and developmental outcomes, this pattern suggests that educational institutions play a crucial stabilizing role for vulnerable populations in digital contexts, potentially compensating for family-related disadvantages. These findings advance theoretical understanding by demonstrating specific mediation mechanisms through which digital experiences transfer between educational institutions and individual development across various family structures. Successfully addressing these challenges requires coordinated efforts from multiple stakeholders. Families need support managing digital interruptions and enhancing supervision effectiveness. Schools should implement targeted digital literacy programs while strengthening home-school partnerships. Communities can provide support networks for vulnerable families, while policymakers should develop frameworks that recognize and monitor diverse family structures and promote technology's positive educational potential. Such collaborative approaches are essential for leveraging digital technologies to enhance educational equity while supporting positive development across various family structures. Declarations Funding Statement No funding was received to assist with the preparation of this manuscript. Competing interests The author declares no competing interests. Ethics Declarations: Ethics Approval and Consent to Participate The research protocol received ethical approval from the Research Ethics Review Board of Hiroshima University. Informed consent was obtained from all participants in the study, including assent from participating students and consent from their parents/guardians and teachers. Special ethical considerations were implemented for LBC participants, and all participants were informed of their right to withdraw without consequence. Consent to Publish Additional informed consent was obtained from all participants for their anonymized data to be included and published in this article. Data Availability Statement The data that supports the findings of this study are available from the corresponding author upon reasonable request. Author Contributions Yunlei Hu was responsible for all aspects of this study and prepared the entire manuscript. Acknowledgement The author thanks the school administrators, teachers, students and their families in Qiliping Town, Hong'an County, for their generous participation and support in this research. Special thanks are extended to the participating left-behind children and their families for sharing their experiences. The author also appreciates the insightful recommendations and comments made by editors and reviewers during the review process. References Cohen J, Onunaku N, Clothier S, Poppe J. Helping young children succeed: strategies to promote early childhood social and emotional development. Washington, D.C.: National Conference of State Legislatures; 2005. China Internet Network Information Center (CNNIC). [The 5th national survey report of minors' Internet usage]. Beijing: CNNIC; 2023 Dec. https://qnzz.youth.cn/qckc/202312/P020231223672191910610.pdf National Bureau of Statistics of China, UNICEF China, & UNFPA. What the 2020 Census can tell us about children in China: facts and figures. 2023 Apr. https://www.unicef.cn/en/reports/population-status-children-china-2020-census UN China. United Nations sustainable development cooperation framework for the Peoples Republic of China (2021-2025). 2021 May. https://unsdg.un.org/sites/default/files/2020-11/China-UNSDCF-2021-2025.pdf Fredricks JA, Blumenfeld PC, Paris AH. School engagement: potential of the concept, state of the evidence. Review of Educational Research. 2004 Mar 1;74(1):59-109. https://doi.org/10.3102/00346543074001059 Kardefelt-Winther D. How does the time children spend using digital technology impact their mental well-being, social relationships and physical activity? An evidence-focused literature review. UNICEF; 2017 Dec. https://www.unicef.org/innocenti/documents/how-does-time-children-spend-using-digital-technology-impact-their-mental-well-being CASEL. CASEL's SEL framework: what are the core competence areas and where are they promoted? [Internet]. Collaborative for Academic, Social, and Emotional Learning. 2020 Oct [cited 2024 Aug 19]. Available from: https://casel.org/casel-sel-framework-11-2020/ Durlak JA, Weissberg RP, Dymnicki AB, Taylor RD, Schellinger KB. The impact of enhancing students' social and emotional learning: a meta‐analysis of school‐based universal interventions. Child development. 2011 Feb 03; 82(1):405-32. https://doi.org/10.1111/j.1467-8624.2010.01564.x CASEL. Reunite, renew, and thrive: social and emotional learning (SEL) roadmap for reopening school. Collaborative for Academic, Social, and Emotional Learning. 2020 Jul. https://casel.org/casel-gateway-sel-roadmap-for-reopening/?view=1 Twenge JM, Martin GN, Campbell WK. Decreases in psychological well-being among American adolescents after 2012 and links to screen time during the rise of smartphone technology. Emotion. 2018 Jan 22;18(6):765-80. https://doi.org/10.1037/emo0000403 Boniel-Nissim M, van den Eijnden RJJM, Furstova J, Marino C, Lahti H, Inchley J, Šmigelskas K, Vieno A, Badura P. International perspectives on social media use among adolescents: implications for mental and social well-being and substance use. Computers in Human Behavior. 2022 Apr;129:107144. https://doi.org/10.1016/j.chb.2021.107144 Orben A, Przybylski AK, Blakemore SJ, Kievit RA. Windows of developmental sensitivity to social media. Nature Communications. 2022 Mar 28;13(1):1649. https://doi.org/10.1038/s41467-022-29296-3 Giunchiglia F, Zeni M, Gobbi E, Bignotti E, Bison I. Mobile social media usage and academic performance. Computers in Human Behavior. 2018 May;82:177-85. https://doi.org/10.1016/j.chb.2017.12.041 Ansari S, Iqbal N, Asif R, Hashim M, Farooqi SR, Alimoradi Z. Social media use and well-being: a systematic review and meta-analysis. Cyberpsychology, Behavior, and Social Networking. 2024 Oct 10;27(10):704-19. https://doi.org/10.1089/cyber.2024.0001 Richter A, Adkins V, Selkie E. Youth perspectives on the recommended age of mobile phone adoption: survey study. JMIR Pediatrics and Parenting. 2022 Oct 31;5(4):40704. https://doi.org/10.2196/40704 Park N, Lee H. Social implications of smartphone use: Korean college students' smartphone use and psychological well-being. Cyberpsychology, Behavior, and Social Networking. 2012 Sep 13;15(9): 491-7. https://doi.org/10.1089/cyber.2011.0580 Ponti M. Screen time and preschool children: promoting health and development in a digital world. Paediatrics & Child Health. 2023 Jun; 28(3): 184-92. https://doi.org/10.1093/pch/pxac125 Livingstone S, Third A. Children and young people's rights in the digital age: an emerging agenda. New Media & Society. 2017 May 10;19(5):657-70. https://doi.org/10.1177/1461444816686318 McDaniel BT, Coyne SM. "Technoference": the interference of technology in couple relationships and implications for women's personal and relational well-being. Psychology of Popular Media Culture. 2016;5(1):85-98. https://doi.org/10.1037/ppm0000065 Carson V, Kuzik N. The association between parent–child technology interference and cognitive and social–emotional development in preschool-aged children. Child: Care, Health and Development. 2021 Feb 25;47(4):477-83. https://doi.org/10.1111/cch.12859 Shao T, Zhu C, Lei H, Jiang Y, Wang H, Zhang C. The relationship of parent-child technoference and child problematic smartphone use: the roles of parent-child relationship, negative parenting styles, and children's gender. Psychology Research and Behavior Management. 2024 May 20;17:2067-81. https://doi.org/10.2147/PRBM.S456411 Li Y, Lerner RM. Trajectories of school engagement during adolescence: implications for grades, depression, delinquency, and substance use. Developmental psychology. 2011 Jan;47(1):233-47. https://doi.org/10.1037/a0021307 Insight and Info. [In-depth research and development prospect analysis report on China's online education industry (2024-2031)] [Internet] . Beijing: Insight and Info; 2024 [cited 2024 Sep 12]. Available from: https://www.chinabaogao.com/baogao/202404/702472.html Bergdahl N, Nouri J, Fors U. Disengagement, engagement and digital skills in technology-enhanced learning. Education and Information Technologies. 2020;25:957-83. https://doi.org/10.1007/s10639-019-09998-w Burger J, Newman K, Stevens D. Student engagement—pre and post Covid-19 pandemic. Canadian Journal of School Psychology. 2024 Feb 5;39(1):53-71. https://doi.org/10.1177/08295735241228392 Zhou J, Yang X. The digital divide in online learning during COVID-19: a study of rural students in China. Children and Youth Services Review. 2022 Nov;139:102122. https://doi.org/10.1016/j.techsoc.2022.102122 Liu F, Gai X, Xu L, Wu X, Wang H. School engagement and context: a multilevel analysis of adolescents in 31 provincial-level regions in China. Frontiers in Psychology. 2021 Oct 26;12:724819. https://doi.org/10.3389/fpsyg.2021.724819 Ma JKH. The digital divide at school and at home: a comparison between schools by socioeconomic level across 47 countries. International Journal of Comparative Sociology. 2021 Aug 19;62(2):115-40. https://doi.org/10.1177/00207152211023540 Finn JD. Withdrawing from school. Review of Educational Research. 1989;59(2):117-42. https://doi.org/10.3102/00346543059002117 Wang MT, Degol JL, Henry DA. An integrative development-in-sociocultural-context model for children's engagement in learning. American Psychologist. 2019 Dec;74(9):1086-102. https://doi.org/10.1037/amp0000522 Wang MT, Degol JL. School climate: a review of the construct, measurement, and impact on student outcomes. Educational Psychology Review. 2016;28(2):315-52. https://doi.org/10.1007/s10648-015-9319-1 Martinez-Yarza N, Solabarrieta-Eizaguirre J, Santibáñez-Gruber R. The impact of family involvement on students' social-emotional development: the mediational role of school engagement. European Journal of Psychology of Education. 2024 Jun 26;1-31. https://doi.org/10.1007/s10212-024-00862-1 Chang F, Shi Y, Shen A, Kohrman A, Li K, Wan Q, Kenny K, Rozelle S. Understanding the situation of China's left-behind children: a mixed-methods analysis. The Developing Economies. 2019;57(1):3-35. https://doi.org/10.1111/deve.12188 Li M, Ren Y. Mobile phone addiction among left-behind children in rural China: a meta-analysis. Current Psychology. 2024 Sep 02;43:29823-32. https://doi.org/10.1007/s12144-024-06588-z Zou S, Zhou LH. China Daily. Report: China's 'left-behind children' addicted to cellphones [Internet]. Hongkong: China Daily; 2023 March 2 [cited 2024 Aug 08]. Available from: https://www.chinadailyhk.com/hk/article/318084 Hung J, Chen J, Chen O. The practice of social protection policies in China: a systematic review on how left-behind children's mental health can be optimized. Perspectives in Public Health. 2023 Oct 27;17579139231205491. https://doi.org/10.1177/17579139231205491 Hu, Y. Family features and academic and social-emotional development of left-behind children in Hubei, China. Malaysian Online Journal of Educational Sciences. 2024;12(3):1-14. https://mojes.um.edu.my/index.php/MOJES/article/view/56550/17587 Su S, Li X, Lin D, Xu X, Zhu M. Psychological adjustment among left-behind children in rural China: the role of parental migration and parent-child communication. Child: Care, Health and Development. 2013 Jun 18;39(2):162-70. https://doi.org/10.1111/j.1365-2214.2012.01400.x Wang Q, Zheng X, Zhang S. Digital compensation: smartphone use in the emotion socialisation of left-behind children in rural China. New Media & Society. 2023 Nov 28. https://doi.org/10.1177/14614448231213954 Tang J, Wang K, Luo Y. The bright side of digitization: assessing the impact of mobile phone domestication on left-behind children in China's rural migrant families. Frontiers in Psychology. 2022 Oct 19;13:1003379. https://doi.org/10.3389/fpsyg.2022.1003379 Alexander K. Is it family or school? Getting the question right. RSF: The Russell Sage Foundation Journal of the Social Sciences. 2016 Sep;2(5):18-33. https://doi.org/10.7758/RSF.2016.2.5.02 Bronfenbrenner U. The ecology of human development: Experiments by nature and design. Cambridge, MA: Harvard University Press;1979. Johnson GM, Puplampu KP. Internet use during childhood and the ecological techno-subsystem. Canadian Journal of Learning and Technology. 2008;34(1):19-28. http://dx.doi.org/10.21432/T2CP4T National Bureau of Statistics of China. [Annual monitoring report on rural migrant workers 2022] [Internet]. Beijing: National Bureau of Statistics of China; 2023 Apr 28 [cited 2024 Dec]. Available from: http://www.stats.gov.cn/sj/zxfb/202304/t20230427_1939124.html Wei JY. [Being together! Report on the development of children of migrant population in China, 2021] [Internet]. Beijing: New Citizen Program; 2022 Jan 12 [cited 2024 Dec]. Available from: https://m.thepaper.cn/baijiahao_16255384 Arain M, Haque M, Johal L, Mathur P, Nel W, Rais A, Sandhu R, Sharma S. Maturation of the adolescent brain. Neuropsychiatric disease and treatment. PubMed Central. 2013 Apr 3;9: 449-61. https://doi.org/10.2147/NDT.S39776 Hart SR, Stewart K, Jimerson SR. The student engagement in schools questionnaire (SESQ) and the teacher engagement report form-new (TERF-N): examining the preliminary evidence. Contemporary School Psychology. 2011;15(1):67-79. https://doi.org/10.1007/BF03340964 Gresham FM, Elliott SN. Social skills improvement system social emotional learning edition rating forms. Bloomington, MN: Pearson Assessments; 2017. Kline RB. Principles and practice of structural equation modeling. 4th ed. New York: Guilford Press; 2016. Boss P, Bryant CM, Mancini, JA. Family stress management: a contextual approach.3rd ed. Sage Publications; 2017. https://doi.org/10.4135/9781506352206 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Oct, 2025 Editor assigned by journal 31 Jul, 2025 Reviews received at journal 20 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers invited by journal 20 Jun, 2025 Submission checks completed at journal 20 Jun, 2025 First submitted to journal 19 Jun, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5675984","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507263747,"identity":"ce970d5a-3a4f-4ca9-8c40-35628e50a43a","order_by":0,"name":"Yunlei Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie2PsYrCUBBFJww8t7iaNpLsPwwIEVHMxwSsUgg2u1hs4U/4GzavVgKmEWyFNEbB3mJht9p9sbBMXrmw7xTDHZgDd4gcjj+IPOackOR+dTYRPTtFCFSA6wXKViE6QgV1blWGQRaHc5lE3qq7f//MppEiri6nBmW0NspaZmDuzcpXnZpiajDImoqdjALJoZjisq/ZKFChhfIDGGXR1x/WyhYBI/buOrdQDrfFGJJCWKWhpwvTsO2XIt2UeJsm4ue7+7deJn5nVV2bFKIXeUbGYzae13TOz+h9tV47HA7Hf+QXwnQ6pE1vub0AAAAASUVORK5CYII=","orcid":"","institution":"Hiroshima University","correspondingAuthor":true,"prefix":"","firstName":"Yunlei","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-12-19 10:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5675984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5675984/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90812201,"identity":"04b9e7bd-6cf6-4192-92fb-83546383d98f","added_by":"auto","created_at":"2025-09-08 12:17:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68829,"visible":true,"origin":"","legend":"\u003cp\u003ePrimary Analytical Model for SEM\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5675984/v1/7d77c1251af4607040d00f5e.png"},{"id":90812200,"identity":"085d121c-1bb3-47bb-95ab-cccf3e852779","added_by":"auto","created_at":"2025-09-08 12:17:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":262273,"visible":true,"origin":"","legend":"\u003cp\u003eSEM Results Across Four Models\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5675984/v1/29427df8c3286a1f46f23d6d.png"},{"id":90813136,"identity":"b037130a-0a14-4a1f-8279-5125de99784b","added_by":"auto","created_at":"2025-09-08 12:25:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1705792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5675984/v1/f3887848-916d-4958-9b75-539221a16ace.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mobile Devices and Social-Emotional Development: School Engagement as Mediator and Its Compensation for Left-behind Children in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global COVID-19 pandemic accelerated children\u0026apos;s reliance on mobile technologies for both learning and social connections. This shift has raised important questions about children\u0026apos;s social-emotional development. As an increasing academic focus, social-emotional development refers to the abilities to experience, express, and manage emotions while establishing positive relationships with others [1]. In China, digital transformation is pronounced, with over 90% of minors accessing the internet primarily through mobile phones [2]. The widespread mobile device usage presents both opportunities and challenges for achieving educational equity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLeft-behind children (LBC) represent a uniquely vulnerable population within this digital landscape. These children experience prolonged separation from one or both migrant parents, creating distinct developmental circumstances. \u0026nbsp;With approximately 66 million LBC in rural China as of 2020 [3], it has become crucial to understand how their increased dependence on mobile devices for parent-child communication associates with their development. Identified as a vulnerable population by the United Nation requiring targeted support [4], LBC may experience both benefits and risks of mobile devices amplified by their distinct family dynamics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDigital transformation has fundamentally reshaped educational landscapes. School engagement has been recognized as a crucial factor for investigations, which refers to how students participate in, connect with, and invest in their learning [5]. These investigations become particularly important when child-parent relationships and parent-teacher connections heavily rely on digital communication technologies. While research has established broad associations between technology usage and developmental trajectories [6], significant gaps persist regarding how specific mobile device usage patterns relate to social-emotional learning (SEL) competencies while considering institutional functions, particularly among vulnerable populations like LBC. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study investigated the relationships among adolescents\u0026apos; spare-time mobile device usage patterns at home, school engagement, and SEL competencies, while comparing LBC and non-LBC populations. Four key aspects of mobile device usage were examined: frequency of mobile phone usage, children\u0026apos;s attitudes toward devices, adult supervision of device usage, and technoference (technology-related interruptions in interpersonal interactions). Through this investigation, it was aimed to provide evidence-based insights for supporting diverse family structures in increasingly digital educational environments.\u003c/p\u003e"},{"header":"2.\tLiterature Review","content":"\u003cp\u003e\u003cstrong\u003e2.1 SEL in Digital Contexts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScholarly attention to children\u0026apos;s social-emotional development has grown considerably within the field of developmental psychology. Though variously conceptualized across disciplines, Collaborative for Academic, Social, and Emotional Learning (CASEL) defines SEL as the process of developing and practicing core life skills that enable individuals to form healthy identities, regulate emotions, achieve goals, empathize with others, create positive relationships, and make responsible decisions [7].\u003c/p\u003e\n\u003cp\u003eDigital transformation has fundamentally altered how these competencies develop. While traditional SEL interventions have historically relied on face-to-face interactions and direct feedback [8], digital landscape introduces both opportunities and challenges. This transformation into digital learning environments even after the global pandemic prompted more intentional approaches to relationship-building, requiring innovative strategies to cultivate meaningful interpersonal connections within virtual spaces [9].\u003c/p\u003e\n\u003cp\u003eRecent Research has presented a nuanced picture of digital impacts on SEL. Some studies reveal extended screen time correlating with diminished psychological health [10], yet other research suggests that purposeful and mindful digital engagement can enhance SEL competencies through expanded social connections and emotional expression opportunities [11]. Cultural context significantly moderates these relationships, with evidence from 29 countries showing substantial variation in how intensive social media usage affects social-emotional well-being [11].\u003c/p\u003e\n\u003cp\u003eDespite growing research on digital impacts, understanding of how specific digital behaviors associate with SEL outcomes in Chinese cultural contexts remains limited, particularly for vulnerable populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Mobile Device Factors and Adolescent SEL Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examined four primary factors of mobile device usage patterns: mobile phone usage frequency, child attitudes, adult supervision, and technoference. Each plays a distinct role in adolescents\u0026apos; digital experiences and development.\u003c/p\u003e\n\u003cp\u003eAs a traditional focus, usage frequency of technologies shows intricate connections to developmental trajectories than previously assumed. Recent longitudinal research challenges simplistic assumptions about uniform digital impacts, uncovering varied temporal patterns across developmental windows [12]. Though leisure-time device behaviors influence academic engagement, usage intensity alone demonstrates inconsistent relationships with developmental outcomes [13]. A meta-analysis found negative associations between problematic social media patterns and well-being, while mere excessive usage showed non-significant relationships [14]. These findings suggest the need to examine factors beyond simplistic screen-time metrics.\u003c/p\u003e\n\u003cp\u003eChild attitudes toward mobile devices emerge as consequential. Richter et al. [15] documented age-related differences in risk awareness, with older adolescents demonstrating more balanced perspectives compared to younger users\u0026apos; focus on social advantages. Such evolving attitudinal frames suggest that perspectives on mobile devices appear to shape usage habits, with those viewing devices primarily as learning tools exhibiting more regulated engagement and better academic performance [16].\u003c/p\u003e\n\u003cp\u003eAdult supervision represents a critical external factor in shaping adolescents\u0026apos; device usage. Research has evolved beyond simple supervision models toward active participation and guidance in content selection and usage patterns [17]. Recent studies emphasize rights-based approaches that prioritize guided participation over restrictive control [18].\u003c/p\u003e\n\u003cp\u003eTechnoference\u0026mdash;technology-related interruptions in interpersonal interactions [19]\u0026mdash;has emerged as another significant factor. Research has documented negative associations between child-parent technology interference and both cognitive and social-emotional development [20]. Recent evidence shows that these effects persist through adolescence, with child-parent technoference contributing to problematic smartphone usage through pathways mediated by parent-child relationship quality among Chinese adolescents [21].\u003c/p\u003e\n\u003cp\u003eThese factors offer a foundational understanding of adolescent digital experience. Collectively, previous findings suggest that qualitative aspects of device usage (attitudes, supervision, and technoference) may prove more crucial than mere usage frequency in shaping adolescent development, particularly SEL competencies, which need further detailed investigations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 School Engagement in Digital Era\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital integration has transformed school engagement while maintaining its multidimensional structure of behavioral participation, emotional connection, and cognitive investment [22]. In China, where digital education has rapidly expanded with 342 million online education users active in the first half of 2023 [23], these dimensions acquire new significance in technology-mediated learning environments.\u003c/p\u003e\n\u003cp\u003eResearch reveals distinct engagement trajectories in digital contexts. Studies identified differential patterns where digital tools enhanced cognitive engagement while potentially challenging behavioral engagement through increased distraction [24]. Post-COVID-19 transformations introduced new complexities, with senior high school students showing greater adaptability than junior high students in internal resilience and school engagement [25]. These digital engagement gaps appear particularly pronounced in rural China, where infrastructure limitations and varied family support create systematic disparities in students\u0026apos; ability to maintain engagement across learning spaces [26].\u003c/p\u003e\n\u003cp\u003eThe home-school engagement interface has gained heightened significance. Evidence shows that home digital environments significantly predict school engagement, varying systematically by regional development level and family structure [27]. Cross-national research spanning 47 countries reveals that digital experiences at home create distinct transfer effects on school engagement, amplified in contexts of socioeconomic disparity [28].\u003c/p\u003e\n\u003cp\u003eThe evolving nature of schooling in digital contexts, particularly in China, highlights the need to examine complex associations among digital engagement, adolescent social-emotional development, and school engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Mediation Mechanisms in Digital Context\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecent research emphasizes understanding specific pathways linking mobile device usage to developmental outcomes, with school engagement emerging as a crucial mediation mechanism. This mediation framework aligns with theoretical models suggesting that external influences typically operate through students\u0026apos; patterns of educational participation and investment [29; 30].\u003c/p\u003e\n\u003cp\u003eStudies reveal how environmental factors, including technological elements, affect student outcomes through their impact on school engagement and social-emotional development [31]. Research further demonstrated school engagement\u0026apos;s role in mediating between family-related factors and social-emotional development [32]. The mediation patterns show systematic variation across populations and contexts, suggesting that mechanisms may operate differently based on family structure and cultural context [30].\u003c/p\u003e\n\u003cp\u003eWhile research has established school\u0026apos;s mediating role between environmental factors and developmental outcomes, how digital contexts and behavioral might alter traditional engagement-outcome relationships remain unclear, particularly for vulnerable populations navigating digital engagement across different contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 LBC and Their Digital Experience\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLBC represent a unique demographic in China, referring to minors who remain in their household registration while one or both parents have migrated elsewhere for over six months [3]. This phenomenon emerged from China\u0026apos;s rapid urbanization, creating a significant rural LBC population typically cared for by surrogate caregivers, most commonly grandparents. This arrangement creates unique challenges in child-parent communication, emotional bonding, and developmental support, particularly in an increasingly digital world [33].\u003c/p\u003e\n\u003cp\u003eConsidering their circumstance, LBC face distinct challenges and opportunities in digital engagement. Meta-analysis reveals significantly higher rates of mobile phone addiction among rural LBC compared to non-LBC peers, particularly during adolescence [34, 35]. This vulnerability often stems from reduced parental supervision and increased reliance on digital communication for maintaining family connections. While digital devices offer connections to migrant parents, inadequate supervision often leads to problematic usage patterns [36]. This risk is particularly pronounced given that LBC typically experience weaker family cohesion and poorer social-emotional development [37]. Further, the quality of digital-mediated child-parent interactions stands out as a pivotal consideration, with regular, meaningful digital interactions associated with enhanced psychological adjustment among LBC [38].\u003c/p\u003e\n\u003cp\u003eDigital technologies can fulfill vital compensatory roles within geographically separated families. Wang and colleagues [39] identified how mobile phones facilitated emotion socialization in separated families, with digital communication maintaining child-parent emotional bonds across distances. Studies further elaborate that mobile phone \u0026quot;domestication\u0026quot;, the process of integrating devices into family routines, can benefit LBC developmental trajectories when structured appropriately [40].\u003c/p\u003e\n\u003cp\u003eThese findings highlight an urgent need for targeted interventions capable of amplifying technology\u0026apos;s potential advantages while simultaneously minimizing associated risks for LBC populations. Effective support requires understanding both vulnerabilities and resilience factors unique to LBC\u0026apos;s digital experiences, particularly where mobile technology serves as a primary medium for family connections [33]. LBC\u0026apos;s unique circumstances in digital engagement and potential disadvantages in family and development outcomes suggest they may be particularly sensitive to both the benefits and risks of mobile devices. Considering governmental efforts on LBC in China and schools\u0026apos; role in mitigating family disadvantages [41], these considerations underscore the pressing need for empirical investigation into how LBC navigate digital experiences and educational engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Theoretical Integration and Current Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelevant theoretical frameworks require integration to fully capture associations between mobile devices and adolescent development through school engagement. Bronfenbrenner\u0026apos;s [42] ecological systems theory, enhanced by Johnson and Puplampu\u0026apos;s [43] techno-subsystem extension, illuminates how digital experiences transfer between contexts. This proves relevant where technology evolves in interactions across physically separated microsystems. Finn\u0026apos;s [29] participation-identification model complements this by explaining how engagement patterns create self-reinforcing cycles of educational outcomes, offering insights into how digital behaviors may differently associate with developmental trajectories for vulnerable populations.\u003c/p\u003e\n\u003cp\u003eCurrent literature reveals several critical gaps. First, while extensive research documents general associations between digital usage and development [10, 11], specific mechanisms between digital experiences and developmental outcomes remain unclear, particularly regarding spare-time device usage at home and school engagement. Second, despite growing recognition of LBC\u0026apos;s unique challenges in the digital era [34-37], research has not fully explored how device usage patterns operate across different family contexts. Third, existing theoretical frameworks and validated measurements, largely developed in Western settings, require adaptation to understand circumstances in Chinese cultural contexts.\u003c/p\u003e\n\u003cp\u003eThis study examined three key questions:\u003c/p\u003e\n\u003cp\u003e(1) What are the associations among adolescents\u0026apos; mobile device usage, their school engagement, and SEL competencies?\u003c/p\u003e\n\u003cp\u003e(2) What are the mediation mechanisms of school engagement in the relationships between mobile device usage and adolescents\u0026apos; SEL competencies?\u003c/p\u003e\n\u003cp\u003e(3) How do these mediation mechanisms differ between LBC and non-LBC?\u003c/p\u003e\n\u003cp\u003eBuilding on theoretical frameworks and literature, this study proposed three sets of hypotheses:\u003c/p\u003e\n\u003cp\u003eFirst, given the differential impacts of qualitative versus quantitative aspects of device usage, this study expects significant interrelations among mobile device usage, school engagement, and SEL competencies, with distinct associations for different device-related factors (H1a). Usage quality (attitudes, supervision and technoference) should show stronger correlations than usage quantity (usage frequency), with negative associations from technoference and positive associations from supervision (H1b). School engagement and SEL competencies should demonstrate significant positive associations (H1c).\u003c/p\u003e\n\u003cp\u003eSecond, following Finn\u0026apos;s participation-identification model, school engagement should significantly mediate relationships between device-related factors and SEL competencies (H2a), with stronger indirect effects for technoference and supervision (H2b).\u003c/p\u003e\n\u003cp\u003eThird, considering LBC\u0026apos;s unique vulnerabilities in digital contexts, they may experience multidimensional disadvantages in spare-time device usage, school engagement and SEL competencies (H3a). Regarding SEL competencies, LBC may show greater sensitivity to digital and educational engagement, particularly for technoference and supervision (H3b). The mediation mechanisms likely differ between LBC and non-LBC, with more intense mediation effects through school engagement among LBC (H3c).\u003c/p\u003e"},{"header":"3.\tMethodology","content":"\u003cp\u003e\u003cstrong\u003e3.1 Research Design and Sample Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a quantitative cross-sectional design to investigate the relationships between mobile device usage, school engagement, and SEL competencies through hierarchical statistical approaches while examining group differences.\u003c/p\u003e\n\u003cp\u003eData collection occurred in 2024 at a junior secondary school in Hong\u0026apos;an County, Huanggang City, Hubei Province. Hubei Province is a region with one of China\u0026apos;s largest migration and LBC populations, and Hong\u0026apos;an County shows typical characteristics of rural development with comprehensive digital infrastructure in the region [44; 45]. The sampling process employed a two-stage cluster random sampling approach. First, one school was randomly selected from three junior secondary schools in Hong\u0026apos;an County using SPSS 25.0. Second, entire classrooms were randomly chosen as clusters to maintain the natural classroom environment and minimize disruption.\u003c/p\u003e\n\u003cp\u003eInvalid responses (n = 63) were excluded based on three criteria: (1) logically inconsistent responses to reverse-coded items, (2) uniform response patterns indicating insufficient engagement, and (3) invalid response time (\u0026lt; 5 minutes or \u0026gt; 45 minutes). The final sample comprised 405 students (validity rate: 86.5%), including 201 LBC according to their official definition [3], and 204 non-LBC. Gender distribution showed 227 girls (56%) and 178 boys (44%), aged 11-16 years. This study focused on adolescence because this developmental period coincides with first-time personal device ownership and involves sufficient cognitive abilities for survey participation. Further, adolescence represents the second major window of brain development handling executive function and emotional control and is critical for identity formation and social cognition development [46].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Measures and Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study examined adolescents\u0026apos; spare-time mobile device usage patterns that were mainly child-determined and occurred at home. The four key device-related factors were: usage frequency (screen time of mobile phones), child attitudes (three items assessing device utility perspectives), supervision (two items examining adult oversight), and technoference (three items measuring technology-related interruptions, adapted from McDaniel and Coyne [19]).\u003c/p\u003e\n\u003cp\u003eSchool engagement was measured using a ten-item scale adapted from the Student Engagement in Schools Questionnaire (SESQ) [47], examining affective (three items), behavioral (four items), and cognitive engagement (three items). SEL competencies were assessed through a 25-item scale adapted from Social Skills Improvement System Social Emotional Learning Brief Scales-Student Form (SSIS SELb-S) [48], measuring self-awareness, self-management, social awareness, relationship skills, and responsible decision-making.\u003c/p\u003e\n\u003cp\u003eAdditional data included mobile device types, usage activities (learning, communication, entertainment, and parent-teacher connection), and demographic information (gender, age, family income, and migration status). This comprehensive approach provided a better understanding of participants\u0026apos; circumstances, particularly for LBC.\u003c/p\u003e\n\u003cp\u003eAll scales employed 5-point Likert formats (1 = strongly disagree, 5 = strongly agree) and demonstrated strong internal consistency (school engagement: \u0026alpha; = 0.91; SEL: \u0026alpha; = 0.89). Cultural adaptation included translation-back-translation procedures and pilot testing with 45 students to ensure contextual appropriateness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Data Collection Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter obtaining institutional approval and consent from the principal, teachers, participating students, and their guardians, data collection employed a dual-mode strategy during regular school hours. Both online and offline questionnaire options used identical content and administration procedures to accommodate student preferences and technological access. Teachers were trained in standardized administration procedures to provide instructions while maintaining participant confidentiality through an anonymous response collection. Online responses utilized a secure digital platform (Wenjuanxing) with data encryption and secure storage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Analytical Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analytical approach proceeded through several systematic phases. First, initial screening examined missing values, outliers, and variable distributions using SPSS 25.0. Preliminary correlation analyses investigated relationships among key study variables (device-related factors, school engagement, and SEL competencies) to address Research Question 1 and provide a foundation for subsequent SEM analysis.\u003c/p\u003e\n\u003cp\u003eConfirmatory factor analysis (CFA) validated measurement models by examining factor loadings, composite reliability (CR; acceptable fit \u0026ge; 0.70), and average variance extracted (AVE; acceptable fit \u0026ge; 0.50). Model evaluation employed multiple fit indices, including Root Mean Square Error of Approximation (RMSEA; acceptable fit \u0026le; 0.08), Goodness-of-Fit Index (GFI; acceptable fit \u0026ge; 0.90), Comparative Fit Index (CFI; acceptable fit \u0026ge; 0.95), following Kline\u0026apos;s guidelines [49].\u003c/p\u003e\n\u003cp\u003eNext, employing Structural Equation Modeling (SEM) approach, this study tested four models examining specific device-related factors (usage frequency of mobile phones, child attitudes, supervision, and technoference) using Amos 24.0. Figure 1 presents the primary analytical model. Given the substantial sample size (n=405) exceeding the recommended minimum ratio of 20:1 for observed variables to cases [49], this study employed a parametric method in SEM approach for estimating effects. This approach assessed structural pathways and effect sizes (direct, indirect, and total) while examining school engagement\u0026apos;s mediating role.\u003c/p\u003e\n\u003cp\u003eAfter completing the SEM analysis of the total sample, independent samples t-tests were conducted to identify group differences between LBC and non-LBC across multiple domains. Subsequent group-specific SEM analyses investigated whether the established patterns varied between LBC and non-LBC populations, with particular attention to path coefficients and model fit indices across the two groups.\u003c/p\u003e"},{"header":"4.\tResults ","content":"\u003cp\u003e\u003cstrong\u003e4.1 Preliminary Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.1 Demographic Information of the Sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 1, the sample was balanced between LBC (n = 201, 49.6%) and non-LBC (n = 204, 50.4%) with comparable gender distributions. Among LBC families, fathers constituted the primary migrant parents (97.5%), highlighting the gendered nature of rural Chinese labor migration. Most participants (93.086%) came from middle-low-income families, reflecting typical rural socioeconomic conditions.\u003c/p\u003e\n\u003cp\u003eTable 1 Demographic Information of the Sample\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-LBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e57.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e54.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e56.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e42.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e45.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e43.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e11-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e77.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e70.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e74.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e14-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e22.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e29.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e25.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHigh-middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e6.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMiddle-low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e95.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e93.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMigration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMigrant Father\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e97.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e48.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMigrant Mother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e26.4368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e13.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eBoth Migrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e23.9881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e11.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNon-migrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e100.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e50.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e100.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e100.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e100.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLBC = Left-behind Children; Non-LBC = Non-left behind Children\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMobile phones emerged as the primary device used by participants (M = 3.200, SD = 1.004), exceeding usage of smart watches (M = 1.470, SD = 1.013) and tablets (M = 1.500, SD = 0.935). Device activities demonstrated balanced distribution across educational (M = 2.920, SD = 0.990), communication (M = 2.810, SD = 1.131), and entertainment activities (M = 2.510, SD = 1.002). Parental connections with school teachers through digital means showed substantial presence (M = 3.29, SD = 0.938).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.2 Correlation Analysis of Key Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents descriptive statistics and correlations among variables. Regarding device usage patterns, technoference demonstrated strong correlations with other three device-related factors, particularly child attitudes (r = 0.577, p \u0026lt; 0.01). Supervision showed consistent negative associations with use frequency (r = -0.108, p \u0026lt; 0.05) and technoference (r = -0.137, p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003eTable 2 Descriptive Statistics and Correlations\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003col\u003e\n \u003cli\u003eFrequency\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.200\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003eAttitudes\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e7.116\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e2.510\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.181**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003eSupervision\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e7.319\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.629\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.108*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003eTechnoference\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e7.215\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e2.257\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.276**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.577**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.137**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003eSEL\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e90.943\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e11.774\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.099*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.151**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.218**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.327**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003col start=\"6\"\u003e\n \u003cli\u003eSE\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e37.254\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6.712\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.123*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.254**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.318**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.342**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.737**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003col start=\"7\"\u003e\n \u003cli\u003eGender\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.103*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN = 405.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSE = School Engagement; SEL = Social-emotional Learning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGender was coded as 0 = Female, 1 = Male.\u003c/p\u003e\n\u003cp\u003e**Correlation is significant at the 0.01 level (2-tailed); *Correlation is significant at the 0.05 level (2-tailed).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDevelopment-related variables exhibited robust interconnections, with school engagement and SEL competencies strongly correlating (r = 0.737, p \u0026lt; 0.01). Technoference showed the strongest negative correlations with both outcomes (SEL: r = -0.327; school engagement: r = -0.342, p \u0026lt; 0.01), while supervision presented consistent positive correlations (SEL: r = 0.218; school engagement: r = 0.318, p \u0026lt; 0.01). Gender showed weak correlations with all key study variables (r \u0026lt; 0.15), justifying its exclusion as a covariate in subsequent SEM analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 SEM Analysis of Total Sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.1 Normality Tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to SEM analysis, normality tests confirmed that all variables met the statistical assumptions required for maximum likelihood estimation (Table 3). Variables showed acceptable skewness (ranging from -0.229 to 0.422) and kurtosis (ranging from -0.794 to 1.024) values. The critical ratios for most variables fell within \u0026plusmn;2.58, except for self-awareness, which showed a slightly higher kurtosis critical ratio (2.963). The sample size (n=405) exceeded the recommended minimum ratio of 20:1 for observed variables to cases [49], further supporting the appropriateness of the SEM approach with a parametric method. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Skewness and Kurtosis Values of Study Variables\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkewness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.R.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKurtosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.R.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevice-related Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eChild Attitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eSupervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eTechnoference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eSelf-awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eSelf-management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eSocial Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-2.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eRelationship Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-1.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-2.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eResponsible Decision-Making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eAffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-1.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eBehavioral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-2.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eCognitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN= 405.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSE= School Engagement; SEL= Social-emotional Learning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.2 Measurement Model Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCFA demonstrated robust measurement quality across all constructs. Factor loadings for SEL competencies (ranging from 0.689 to 0.810) and school engagement (ranging from 0.770 to 0.900) exceeded conventional thresholds. All measurement models exhibited strong reliability (school engagement: CR = 0.860; SEL: CR = 0.866) and satisfactory convergent validity (school engagement: AVE = 0.673; SEL: AVE = 0.564).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.3 Structural Model Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll four structural models demonstrated acceptable fit indices (Table 4), with the mobile phone use frequency model showing particularly strong fit (\u0026chi;\u0026sup2; = 28.059, p = 0.138, RMSEA = 0.029, GFI = 0.984, CFI = 0.996).\u003c/p\u003e\n\u003cp\u003eTable 4 Model Fit Indices Across Four Models\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eX1: Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e28.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eX2: Child Attitudes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e80.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eX3: Supervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e64.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eX4: Technoference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e41.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN= 405.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMode1: X1 = Frequency; Model 2: X2 = Child Attitudes; Model 3: X3 = Supervision; Model 4: X4 = Technoference.\u003c/p\u003e\n\u003cp\u003eRMSEA= Root Means Square error of Approximation; GFI= Goodness-of-Fit Index; CFI= Comparative Fit Index; TLI= Tucker-Lewis Index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSEM analysis revealed consistent patterns of full mediation across models, with school engagement serving as the primary mechanism linking device-related factors to SEL competencies (Fig. 2). Table 5 presents standardized effect size among variables.\u003c/p\u003e\n\u003cp\u003eTable 5 Standardized Total, Direct, and Indirect Effects Across Four Models\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome: SEL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome: SE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor: Device\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor: SE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor: Device\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOnly Direct Path\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOnly Direct Path\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eX1: Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eX2: Child Attitudes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eX3: Supervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eX4: Technoference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN= 405.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMode1: X1 = Frequency; Model 2: X2 = Child Attitudes; Model 3: X3 = Supervision; Model 4: X4 = Technoference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSE= School Engagement; SEL= Social-emotional Learning.\u003c/p\u003e\n\u003cp\u003eTotal effects= direct effects + indirect effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTechnoference emerged as the strongest negative factor (total effect = -0.341), with a full mediation pattern operating entirely through its negative pathway to school engagement (\u0026beta; = -0.360). Child attitudes showed medium negative effects (total effect = -0.167), similarly manifesting through decreased school engagement (\u0026beta; = -0.255). Supervision showed consistent positive effects (total effect = 0.228) through enhanced school engagement (\u0026beta; = 0.339). Usage frequency showed the weakest total effect (-0.098), suggesting usage quantity may be less crucial than other quality factors.\u003c/p\u003e\n\u003cp\u003eNo significant direct pathways emerged from device-related factors to SEL competencies, while pathways from school engagement to SEL remained strong and stable across all models (\u0026beta; ranging from 0.824 to 0.862). These findings, visualized in Fig. 2(a-d), demonstrate that school engagement fully mediates the relationship between device-related factors and SEL competencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Group Comparison Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.1 Initial Group Differences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent samples t-tests revealed distinct patterns between LBC and non-LBC (Table 6). Despite comparable age and gender distributions, LBC and non-LBC showed significant differences in several key areas.\u003c/p\u003e\n\u003cp\u003eRegarding digital engagement, LBC exhibited higher mobile phone usage frequency (t = 2.166, p \u0026lt; 0.05) and perceived less device-based parent-teacher connection (t = 2.081, p \u0026lt; 0.05). However, both groups showed similar patterns in device-related attitudes, adult supervision, and technoference, with balanced distribution of digital activities across learning, communication, and entertainment purposes.\u003c/p\u003e\n\u003cp\u003eFor developmental outcomes, LBC exhibited significantly lower SEL competencies overall (t = 2.701, p \u0026lt; 0.01), particularly in self-management and responsible decision-making. Although overall school engagement levels were comparable, LBC showed lower behavioral engagement specifically (t = 2.012, p \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6 Results of T-test on Variables Between LBC and Non-LBC\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLBC (n=201)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-LBC (n=204)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevice-related Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.300\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e-2.166*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.338\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e6.897\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e-1.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eSupervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.234\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e7.402\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eTechnoference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.304\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e7.128\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e89.363\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e11.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e92.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e11.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2.701**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-3.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eSelf-awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e17.368\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e17.730\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eSelf-management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e15.378\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e16.544\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e3.648***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-1.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eSocial Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e18.955\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e19.529\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.964*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eRelationship Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e20.304\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e20.441\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eResponsible Decision-making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e17.358\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e18.255\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3.161**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e36.602\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e7.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e37.897\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e6.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-1.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eAffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e11.249\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e11.578\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eBehavioral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e14.766\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e15.348\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eCognitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e10.587\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e10.971\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevice Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eMobile Phone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e3.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e-2.166*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eSmart Watch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003ePad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevice Activities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eCommunication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.860\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eEntertainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eParent-teacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e3.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2.081*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003eemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e13.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e13.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN = 405.\u003c/p\u003e\n\u003cp\u003eLBC = Left-behind Children; Non-LBC = Non-left behind Children; SE = School Engagement; SEL = Social-emotional Learning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGender was coded as 0 = Female, 1 = Male.\u003c/p\u003e\n\u003cp\u003eMD (Mean Difference) = M(LBC) - M (Non-LBC). *p \u0026lt; .05, **p \u0026lt; .01, ***p \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.2 Group-specific SEM Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel fit indices varied systematically between groups (Table 7), with non-LBC showing superior fit for usage frequency (RMSEA = 0.062 vs 0.078) and technoference models (RMSEA = 0.059 vs 0.098), while the supervision model demonstrated a better fit for LBC (RMSEA = 0.087 vs 0.109).\u003c/p\u003e\n\u003cp\u003eAnalysis revealed distinct mediation patterns between groups (Table 7). LBC exhibited higher standardized total effects across all models, with pronounced differences in supervision (0.293 vs. 0.142) and technoference (-0.428 vs. -0.261). Usage frequency showed significant pathways only among LBC, operating entirely through school engagement (\u0026beta; = -0.167). Child attitudes showed partial mediation among LBC with both significant direct and indirect pathways through school engagement, whereas non-LBC exhibited full mediation solely through the school engagement pathway.\u003c/p\u003e\n\u003cp\u003eThe supervision model demonstrated full mediation for LBC with effects entirely through school engagement (\u0026beta; = 0.299) but partial mediation for non-LBC, with competing effects: a positive indirect pathway through school engagement (\u0026beta; = 0.396) alongside a negative direct pathway to SEL (\u0026beta; = -0.201). The technoference model demonstrated full mediation for both groups, with stronger mediation among LBC (\u0026beta; = -0.424) compared to non-LBC (\u0026beta; = -0.305).\u003c/p\u003e\n\u003cp\u003eThe school engagement \u0026rarr; SEL pathways demonstrated consistently stronger regression weights for LBC across all models (\u0026beta; ranging from 0.872 to 0.934, p\u0026lt;0.001) compared to non-LBC (\u0026beta; ranging from 0.776 to 0.867, p\u0026lt;0.001). Moreover, LBC showed superior coefficients on all device-related factors \u0026rarr; school engagement pathways except for supervision, underscoring how digital-developmental associations systematically differ across family structures.\u003c/p\u003e\n\u003cp\u003eTable 7 Results of Group-specific Analyses Between LBC and Non-LBC Across Four Models\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel/ Pathway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-LBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1: X1 = Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eRMSEA = 0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eRMSEA = 0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eUse Frequency \u0026rarr; SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.167*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eUse Frequency \u0026rarr; SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eSE \u0026rarr; SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.900***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.789***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eTotal Effects on SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2: X2 = Child Attitudes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eRMSEA = 0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eRMSEA = 0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eChild Attitudes \u0026rarr; SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.309***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e-0.196*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eChild Attitudes \u0026rarr; SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.115*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eSE \u0026rarr; SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.934***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.789***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eTotal Effects on SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3: X3 = Supervision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eRMSEA = 0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eRMSEA = 0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eSupervision \u0026rarr; SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.299***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.396***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eSupervision \u0026rarr; SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e-0.201**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eSE \u0026rarr; SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.885***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.867***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eTotal Effects on SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4: X4 = Technoference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eRMSEA = 0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eRMSEA = 0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eTechnoference \u0026rarr; SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.424***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e-0.305***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eTechnoference \u0026rarr; SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eSE \u0026rarr; SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.872***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.776***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eTotal Effects on SEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e-0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSE = School Engagement; SEL = Social-emotional Learning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLBC = Left-behind Children (n = 201); Non-LBC = Non-left behind Children (n = 204).\u003c/p\u003e\n\u003cp\u003e*p \u0026lt; .05, **p \u0026lt; .01, ***p \u0026lt; .001. Path coefficients are standardized. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"5.\tDiscussion ","content":"\u003cp\u003eThis investigation revealed systematic associations between mobile device usage patterns and adolescents\u0026apos; SEL competencies, with school engagement functioning as a key mediation mechanism that demonstrates distinct patterns between left-behind and non-left-behind families. Three major findings emerged: (1) different device-related factors showed varied associations with developmental outcomes, with technoference emerging as most detrimental while supervision offered consistent protection; (2) school engagement consistently and fully mediated the relationships between device-related factors and SEL competencies; and (3) these associations demonstrated stronger patterns among LBC, coupled with more robust school engagement mediation effects, suggesting both their heightened sensitivity to digital experiences and school\u0026apos;s enhanced compensatory role in supporting this vulnerable population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1 Patterns of Associations: Mobile Device Factors, School Engagement, and SEL Competencies \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferent mobile device factors relate to adolescents\u0026apos; developmental outcomes, both school engagement and SEL competences, in distinct ways. Examining specific aspects of digital engagement provided several key insights.\u003c/p\u003e\n\u003cp\u003eThe modest association between usage frequency and SEL competencies supports prioritizing quality over quantity in digital engagement. Simple screen time metrics appear less crucial than interaction quality in understanding technology\u0026apos;s relationship with adolescent social-emotional outcomes [6, 12]. The balanced distribution across educational, communication, and entertainment activities in this study further indicates that usage patterns, rather than duration of digital activities, shape developmental trajectories.\u003c/p\u003e\n\u003cp\u003eTechnoference emerged as the primary risk factor, operating almost exclusively through educational engagement pathways, which extends understanding of how digital interruptions correlate to individual development. Its strong correlations with other device-related factors, particularly child attitudes, indicate that technology interruption behaviors are embedded in broader digital engagement patterns. While previous research documented technoference\u0026apos;s negative impact on child-parent relationships and cognitive development [20], this study reveals its close associations with and through educational engagement pathways. This also extends Shao et al.\u0026apos;s [21] work by demonstrating how digital interruptions affect development through institutional mechanisms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSupervision showed a consistent protective role in both school engagement and SEL competencies, supporting approaches that emphasize guided participation over control [18]. The negative correlations between supervision and both usage frequency and technoference suggest that effective oversight relates to more regulated usage patterns and shields against disruptive technology behaviors. This finding aligns with Tang et al.\u0026apos;s [40] study while revealing specific mechanisms through which this protection operates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChild attitudes toward mobile devices exhibited complex relationships with developmental outcomes. These patterns suggest the need for nuanced approaches to foster balanced technology perspectives rather than simple restrictions, extending Park and Lee\u0026apos;s [16] work by illuminating how child\u0026apos;s technology perspectives shape development through educational pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 School Engagement as Mediation Mechanism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchool engagement consistently emerged as the primary mediator between digital behaviors and social-emotional development. This finding can be theoretically grounded in Finn\u0026apos;s [29] participation-identification model, which argues that engagement creates self-reinforcing cycles of academic and social-emotional outcomes. This study extends this theoretical framework to the digital era by demonstrating how technology-related factors may influence these cycles through students\u0026apos; educational participation patterns.\u003c/p\u003e\n\u003cp\u003eThe SEM results provide clear evidence for full mediation across all device-related factors, with significant indirect pathways and no significant direct pathways, indicating that the relationships between digital usage patterns and SEL competencies operate entirely through their associations with students\u0026apos; educational engagement. This finding extends Bronfenbrenner\u0026apos;s [42] ecological systems theory by demonstrating how digital experiences relate to developmental outcomes through immediate environments such as school, while challenging Johnson and Puplampu\u0026apos;s [43] techno-subsystem framework that positions digital technologies between microsystem and individual. The findings suggest that digital engagement manifests through complex pathways within broader contexts rather than functioning directly on individual development.\u003c/p\u003e\n\u003cp\u003eMediation effects varied across different device-related factors, with stronger effects for interaction quality factors (technoference and supervision) than for usage metrics. This pattern aligns with Bergdahl et al.\u0026apos;s [24] findings while revealing specific transfer mechanisms. Educational engagement appears to respond more strongly to factors shaping interaction quality than to mere device exposure, a crucial distinction for understanding technology\u0026apos;s role in development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Differential Patterns Between LBC and Non-LBC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMobile devices correlated with educational engagement and SEL competencies differently across family structures. Prior research has documented LBC\u0026apos;s weaker family dynamics and heightened vulnerability to problematic device usage [35, 37], and this study further uncovered distinct digital engagement patterns among LBC. LBC showed higher mobile phone usage coupled with reduced device-based parent-teacher communication, yet maintained comparable supervision levels, suggesting distinct digital needs in separated families and compensatory oversight from extended family networks [40]. LBC\u0026apos;s digital engagement patterns provide critical context for understanding the differential associations between device factors and developmental outcomes for this vulnerable group.\u003c/p\u003e\n\u003cp\u003eThe decreased SEL competencies and amplified effects found among LBC, particularly regarding technoference and supervision, highlight fundamental reconfiguration of technology\u0026apos;s role in separated families. These patterns enrich Boss et al.\u0026apos;s [50] family stress theory by demonstrating how geographic separation contributes to both vulnerabilities and resilience in digital contexts. The theory proposes that when families experience boundary ambiguity due to physical separation, family members develop heightened sensitivity to both stressors and support mechanisms\u0026mdash;a prediction that aligns with the findings of amplified effects among LBC for both technoference and supervision. The nearly doubled negative effect of technoference among LBC suggests heightened consequences of digital interruptions when physical interaction opportunities are limited.\u003c/p\u003e\n\u003cp\u003eDigital engagement patterns appear to shape both vulnerability and resilience among LBC. While they showed greater vulnerability to negative digital engagement, LBC also demonstrated enhanced benefits from supervision, revealing digital vulnerability alongside resilience. This dual pattern advances Wang et al.\u0026apos;s [39] digital compensation framework by showing how technology\u0026apos;s multi-dimensional functions operate through specific educational mechanisms. The finding that supervision benefits manifest primarily through school engagement among LBC, versus direct and indirect effects for non-LBC, suggests fundamentally different mechanisms through which digital oversight supports development in separated families.\u003c/p\u003e\n\u003cp\u003eRegarding school engagement, the stability of affective and cognitive engagement dimensions across groups reflects the fundamental human need for belonging and achievement that persist regardless of family structure [30, 38]. Meanwhile, the lower behavioral engagement among LBC likely represents an adaptation to family separation, where reduced parental physical envelopment affects visible behavioral patterns without compromising underlying psychological investment. These patterns extend Wang and Degol\u0026apos;s [31] work by suggesting family separation primarily affects behavioral manifestation of engagement rather than underlying educational connection, aligning with Martinez-Yarza et al.\u0026apos;s [32] findings on engagement\u0026apos;s compensatory role in educational resilience. These findings suggest that schools may play a crucial role in stabilizing developmental trajectories for LBC\u0026apos;s social-emotional well-being, particularly in digital contexts where technology mediates both educational and family connections. Meanwhile, the lower behavioral engagement among LBC calls for behavior-targeted interventions with cooperative efforts from various stakeholders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Implications and Recommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4.1 Theoretical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study advances the understanding of technology\u0026apos;s role in development across multiple frameworks. The consistent full mediation pattern extends ecological systems theory [42] by illuminating how digital experiences transfer between individual, microsystems, and family-school contexts, while differential patterns between groups demonstrate technology\u0026apos;s role in non-traditional family structure. Meanwhile, this study challenges the techno-subsystem framework [43] by revealing that digital factors operate primarily through broader contextual pathways rather than directly on development. The findings also advance participation-identification theory [29] by showing how digital behaviors reshape engagement-outcome relationships.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe stability of measurement models across groups, coupled with distinct mediation patterns, supports the generalizability of these theoretical extensions while demonstrating differences in how engagement mechanisms operate based on family structure. Furthermore, this research extends the digital compensation framework [39] through the identification of specific institutional engagement pathways in separated families, illuminating how compensation operates through institutional engagement. These theoretical extensions prove significant insights as educational systems navigate post-pandemic digital integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4.2 Practical and Policy Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings advocate for coordinated interventions across multiple stakeholders. At the family level, interventions should prioritize managing technoference and enhancing supervision effectiveness, particularly given LBC\u0026apos;s sensitivity to digital interruptions. Supervision operated through enhanced educational engagement, particularly among LBC group, suggesting the importance of equipping extended family networks with effective digital oversight strategies and relevant social support programs from communities.\u003c/p\u003e\n\u003cp\u003eConsidering the risks of technoference, educational institutions should implement targeted digital literacy programs that address technology interruptions while fostering positive school engagement. These initiatives should strengthen home-school partnerships in ways that leverage technology\u0026apos;s benefits while mitigating risks, with differentiated support systems recognizing distinct vulnerabilities.\u003c/p\u003e\n\u003cp\u003eAt the policy level, educational frameworks require adaptation to better integrate digital family engagement strategies, particularly for separated families. Guidelines should recognize different family structures while establishing systems which can reinforce schools\u0026apos; compensatory function for vulnerable populations. In the post-COVID-19 context, these frameworks should evolve to address both immediate digital integration needs and long-term educational equity considerations. Given the differential patterns in digital behaviors, developmental outcomes, and mediation mechanism across family structures, comprehensive family monitoring strategies should be implemented to support vulnerable populations. Success requires coordinated efforts through both technical and social interventions, emphasizing positive educational engagement rather than merely controlling device usage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.5 Limitations and Future Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are several limitations which frame these findings and suggest directions for future study. First, cross-sectional design cannot establish definitive causality, despite the efforts of strengthening inferences with theoretically grounded hypotheses and hierarchical statistical approaches. Future longitudinal investigations should track how device-school-development relationships evolve over time, particularly for adaptation patterns in separated families.\u003c/p\u003e\n\u003cp\u003eSecond, the statistical analysis, while sophisticated, necessarily simplified complex family processes and technology interactions, with school engagement as the sole mediator. Future studies should more comprehensively examine the complex interplay between different contextual domains, exploring additional factors, such as family dynamics, as potential mechanisms while examining how multi-domain factors impact digital engagement patterns across different family structures.\u003c/p\u003e\n\u003cp\u003eThird, the reliance on self-reported measures may introduce response bias, particularly regarding sensitive perspectives and technology usage patterns. Future research should incorporate objective measures for capturing the dynamic nature of technology-mediated experiences, including digital behavior and culturally sensitive assessments in school and family.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFourth, the sample\u0026apos;s specificity to one region of rural China and focus on junior secondary students may limit generalizability. Cross-cultural studies should examine how these relationships manifest across different contexts, particularly investigating how cultural values influence technology developmental associations through engagement mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals three key insights into how mobile devices associate with adolescent SEL competencies through school engagement across different family structures. First, supervision demonstrated consistent protective associations with developmental outcomes, while technoference emerged as the primary risk factor, both operating through school engagement. These findings challenge screen-time paradigms by highlighting interaction quality over usage quantity. Second, the consistent full mediation patterns through school engagement across various device-related factors indicate that digital experiences correlate with development primarily through their associations with educational participation and investment. Third, the mediation effects showed greater intensity among LBC, particularly for supervision and technoference, suggesting increased vulnerability coupled with enhanced responsiveness to protective factors.\u003c/p\u003e\n\u003cp\u003eWhile behavioral engagement showed group differences, affective and cognitive dimensions remained stable, indicating that family separation primarily affects the manifestation of school behavior rather than underlying educational connection and quality. Given the findings of LBC\u0026apos;s lower SEL competencies, alongside significant associations between device usage patterns and developmental outcomes, this pattern suggests that educational institutions play a crucial stabilizing role for vulnerable populations in digital contexts, potentially compensating for family-related disadvantages.\u003c/p\u003e\n\u003cp\u003eThese findings advance theoretical understanding by demonstrating specific mediation mechanisms through which digital experiences transfer between educational institutions and individual development across various family structures. Successfully addressing these challenges requires coordinated efforts from multiple stakeholders. Families need support managing digital interruptions and enhancing supervision effectiveness. Schools should implement targeted digital literacy programs while strengthening home-school partnerships. Communities can provide support networks for vulnerable families, while policymakers should develop frameworks that recognize and monitor diverse family structures and promote technology\u0026apos;s positive educational potential. Such collaborative approaches are essential for leveraging digital technologies to enhance educational equity while supporting positive development across various family structures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research protocol received ethical approval from the Research Ethics Review Board of Hiroshima University. Informed consent was obtained from all participants in the study, including assent from participating students and consent from their parents/guardians and teachers. Special ethical considerations were implemented for LBC participants, and all participants were informed of their right to withdraw without consequence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional informed consent was obtained from all participants for their anonymized data to be included and published in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYunlei Hu was responsible for all aspects of this study and prepared the entire manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author thanks the school administrators, teachers, students and their families in Qiliping Town, Hong\u0026apos;an County, for their generous participation and support in this research. Special thanks are extended to the participating left-behind children and their families for sharing their experiences. The author also appreciates the insightful recommendations and comments made by editors and reviewers during the review process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCohen J, Onunaku N, Clothier S, Poppe J. Helping young children succeed: strategies to promote early childhood social and emotional development. Washington, D.C.: National Conference of State Legislatures; 2005.\u003c/li\u003e\n\u003cli\u003eChina Internet Network Information Center (CNNIC). [The 5th national survey report of minors\u0026apos; Internet usage]. Beijing: CNNIC; 2023 Dec. https://qnzz.youth.cn/qckc/202312/P020231223672191910610.pdf\u003c/li\u003e\n\u003cli\u003eNational Bureau of Statistics of China, UNICEF China, \u0026amp; UNFPA. What the 2020 Census can tell us about children in China: facts and figures. 2023 Apr. https://www.unicef.cn/en/reports/population-status-children-china-2020-census\u003c/li\u003e\n\u003cli\u003eUN China. United Nations sustainable development cooperation framework for the Peoples Republic of China (2021-2025). 2021 May. https://unsdg.un.org/sites/default/files/2020-11/China-UNSDCF-2021-2025.pdf\u003c/li\u003e\n\u003cli\u003eFredricks JA, Blumenfeld PC, Paris AH. School engagement: potential of the concept, state of the evidence. Review of Educational Research. 2004 Mar 1;74(1):59-109. https://doi.org/10.3102/00346543074001059\u003c/li\u003e\n\u003cli\u003eKardefelt-Winther D. How does the time children spend using digital technology impact their mental well-being, social relationships and physical activity? An evidence-focused literature review. UNICEF; 2017 Dec. https://www.unicef.org/innocenti/documents/how-does-time-children-spend-using-digital-technology-impact-their-mental-well-being\u003c/li\u003e\n\u003cli\u003eCASEL. CASEL\u0026apos;s SEL framework: what are the core competence areas and where are they promoted? [Internet]. Collaborative for Academic, Social, and Emotional Learning. 2020 Oct [cited 2024 Aug 19]. Available from: https://casel.org/casel-sel-framework-11-2020/\u003c/li\u003e\n\u003cli\u003eDurlak JA, Weissberg RP, Dymnicki AB, Taylor RD, Schellinger KB. The impact of enhancing students\u0026apos; social and emotional learning: a meta‐analysis of school‐based universal interventions. Child development. 2011 Feb 03; 82(1):405-32. https://doi.org/10.1111/j.1467-8624.2010.01564.x\u003c/li\u003e\n\u003cli\u003eCASEL. Reunite, renew, and thrive: social and emotional learning (SEL) roadmap for reopening school. Collaborative for Academic, Social, and Emotional Learning. 2020 Jul. https://casel.org/casel-gateway-sel-roadmap-for-reopening/?view=1\u003c/li\u003e\n\u003cli\u003eTwenge JM, Martin GN, Campbell WK. Decreases in psychological well-being among American adolescents after 2012 and links to screen time during the rise of smartphone technology. Emotion. 2018 Jan 22;18(6):765-80. https://doi.org/10.1037/emo0000403\u003c/li\u003e\n\u003cli\u003eBoniel-Nissim M, van den Eijnden RJJM, Furstova J, Marino C, Lahti H, Inchley J, \u0026Scaron;migelskas K, Vieno A, Badura P. International perspectives on social media use among adolescents: implications for mental and social well-being and substance use. Computers in Human Behavior. 2022 Apr;129:107144. https://doi.org/10.1016/j.chb.2021.107144\u003c/li\u003e\n\u003cli\u003eOrben A, Przybylski AK, Blakemore SJ, Kievit RA. Windows of developmental sensitivity to social media. Nature Communications. 2022 Mar 28;13(1):1649. https://doi.org/10.1038/s41467-022-29296-3\u003c/li\u003e\n\u003cli\u003eGiunchiglia F, Zeni M, Gobbi E, Bignotti E, Bison I. Mobile social media usage and academic performance. Computers in Human Behavior. 2018 May;82:177-85. https://doi.org/10.1016/j.chb.2017.12.041\u003c/li\u003e\n\u003cli\u003eAnsari S, Iqbal N, Asif R, Hashim M, Farooqi SR, Alimoradi Z. Social media use and well-being: a systematic review and meta-analysis. Cyberpsychology, Behavior, and Social Networking. 2024 Oct 10;27(10):704-19. https://doi.org/10.1089/cyber.2024.0001\u003c/li\u003e\n\u003cli\u003eRichter A, Adkins V, Selkie E. Youth perspectives on the recommended age of mobile phone adoption: survey study. JMIR Pediatrics and Parenting. 2022 Oct 31;5(4):40704. https://doi.org/10.2196/40704\u003c/li\u003e\n\u003cli\u003ePark N, Lee H. Social implications of smartphone use: Korean college students\u0026apos; smartphone use and psychological well-being. Cyberpsychology, Behavior, and Social Networking. 2012 Sep 13;15(9): 491-7. https://doi.org/10.1089/cyber.2011.0580\u003c/li\u003e\n\u003cli\u003ePonti M. Screen time and preschool children: promoting health and development in a digital world. Paediatrics \u0026amp; Child Health. 2023 Jun; 28(3): 184-92. https://doi.org/10.1093/pch/pxac125\u003c/li\u003e\n\u003cli\u003eLivingstone S, Third A. Children and young people\u0026apos;s rights in the digital age: an emerging agenda. New Media \u0026amp; Society. 2017 May 10;19(5):657-70. https://doi.org/10.1177/1461444816686318\u003c/li\u003e\n\u003cli\u003eMcDaniel BT, Coyne SM. \u0026quot;Technoference\u0026quot;: the interference of technology in couple relationships and implications for women\u0026apos;s personal and relational well-being. Psychology of Popular Media Culture. 2016;5(1):85-98. https://doi.org/10.1037/ppm0000065\u003c/li\u003e\n\u003cli\u003eCarson V, Kuzik N. The association between parent\u0026ndash;child technology interference and cognitive and social\u0026ndash;emotional development in preschool-aged children. Child: Care, Health and Development. 2021 Feb 25;47(4):477-83. https://doi.org/10.1111/cch.12859\u003c/li\u003e\n\u003cli\u003eShao T, Zhu C, Lei H, Jiang Y, Wang H, Zhang C. The relationship of parent-child technoference and child problematic smartphone use: the roles of parent-child relationship, negative parenting styles, and children\u0026apos;s gender. Psychology Research and Behavior Management. 2024 May 20;17:2067-81. https://doi.org/10.2147/PRBM.S456411\u003c/li\u003e\n\u003cli\u003eLi Y, Lerner RM. Trajectories of school engagement during adolescence: implications for grades, depression, delinquency, and substance use. Developmental psychology. 2011 Jan;47(1):233-47. https://doi.org/10.1037/a0021307\u003c/li\u003e\n\u003cli\u003eInsight and Info. [In-depth research and development prospect analysis report on China\u0026apos;s online education industry (2024-2031)] [Internet]\u003cem\u003e.\u003c/em\u003e Beijing: Insight and Info; 2024 [cited 2024 Sep 12]. Available from: https://www.chinabaogao.com/baogao/202404/702472.html\u003c/li\u003e\n\u003cli\u003eBergdahl N, Nouri J, Fors U. Disengagement, engagement and digital skills in technology-enhanced learning. Education and Information Technologies. 2020;25:957-83. https://doi.org/10.1007/s10639-019-09998-w\u003c/li\u003e\n\u003cli\u003eBurger J, Newman K, Stevens D. Student engagement\u0026mdash;pre and post Covid-19 pandemic. Canadian Journal of School Psychology. 2024 Feb 5;39(1):53-71. https://doi.org/10.1177/08295735241228392\u003c/li\u003e\n\u003cli\u003eZhou J, Yang X. The digital divide in online learning during COVID-19: a study of rural students in China. Children and Youth Services Review. 2022 Nov;139:102122. https://doi.org/10.1016/j.techsoc.2022.102122\u003c/li\u003e\n\u003cli\u003eLiu F, Gai X, Xu L, Wu X, Wang H. School engagement and context: a multilevel analysis of adolescents in 31 provincial-level regions in China. Frontiers in Psychology. 2021 Oct 26;12:724819. https://doi.org/10.3389/fpsyg.2021.724819\u003c/li\u003e\n\u003cli\u003eMa JKH. The digital divide at school and at home: a comparison between schools by socioeconomic level across 47 countries. International Journal of Comparative Sociology. 2021 Aug 19;62(2):115-40. https://doi.org/10.1177/00207152211023540\u003c/li\u003e\n\u003cli\u003eFinn JD. Withdrawing from school. Review of Educational Research. 1989;59(2):117-42. https://doi.org/10.3102/00346543059002117\u003c/li\u003e\n\u003cli\u003eWang MT, Degol JL, Henry DA. An integrative development-in-sociocultural-context model for children\u0026apos;s engagement in learning. American Psychologist. 2019 Dec;74(9):1086-102. https://doi.org/10.1037/amp0000522\u003c/li\u003e\n\u003cli\u003eWang MT, Degol JL. School climate: a review of the construct, measurement, and impact on student outcomes. Educational Psychology Review. 2016;28(2):315-52. https://doi.org/10.1007/s10648-015-9319-1\u003c/li\u003e\n\u003cli\u003eMartinez-Yarza N, Solabarrieta-Eizaguirre J, Santib\u0026aacute;\u0026ntilde;ez-Gruber R. The impact of family involvement on students\u0026apos; social-emotional development: the mediational role of school engagement. European Journal of Psychology of Education. 2024 Jun 26;1-31. https://doi.org/10.1007/s10212-024-00862-1\u003c/li\u003e\n\u003cli\u003eChang F, Shi Y, Shen A, Kohrman A, Li K, Wan Q, Kenny K, Rozelle S. Understanding the situation of China\u0026apos;s left-behind children: a mixed-methods analysis. The Developing Economies. 2019;57(1):3-35. https://doi.org/10.1111/deve.12188\u003c/li\u003e\n\u003cli\u003eLi M, Ren Y. Mobile phone addiction among left-behind children in rural China: a meta-analysis. Current Psychology. 2024 Sep 02;43:29823-32. https://doi.org/10.1007/s12144-024-06588-z\u003c/li\u003e\n\u003cli\u003eZou S, Zhou LH. China Daily. Report: China\u0026apos;s \u0026apos;left-behind children\u0026apos; addicted to cellphones [Internet]. Hongkong: China Daily; 2023 March 2 [cited 2024 Aug 08]. Available from: https://www.chinadailyhk.com/hk/article/318084\u003c/li\u003e\n\u003cli\u003eHung J, Chen J, Chen O. The practice of social protection policies in China: a systematic review on how left-behind children\u0026apos;s mental health can be optimized. Perspectives in Public Health. 2023 Oct 27;17579139231205491. https://doi.org/10.1177/17579139231205491\u003c/li\u003e\n\u003cli\u003eHu, Y. Family features and academic and social-emotional development of left-behind children in Hubei, China. Malaysian Online Journal of Educational Sciences. 2024;12(3):1-14. https://mojes.um.edu.my/index.php/MOJES/article/view/56550/17587\u003c/li\u003e\n\u003cli\u003eSu S, Li X, Lin D, Xu X, Zhu M. Psychological adjustment among left-behind children in rural China: the role of parental migration and parent-child communication. Child: Care, Health and Development. 2013 Jun 18;39(2):162-70. https://doi.org/10.1111/j.1365-2214.2012.01400.x\u003c/li\u003e\n\u003cli\u003eWang Q, Zheng X, Zhang S. Digital compensation: smartphone use in the emotion socialisation of left-behind children in rural China. New Media \u0026amp; Society. 2023 Nov 28. https://doi.org/10.1177/14614448231213954\u003c/li\u003e\n\u003cli\u003eTang J, Wang K, Luo Y. The bright side of digitization: assessing the impact of mobile phone domestication on left-behind children in China\u0026apos;s rural migrant families. Frontiers in Psychology. 2022 Oct 19;13:1003379. https://doi.org/10.3389/fpsyg.2022.1003379\u003c/li\u003e\n\u003cli\u003eAlexander K. Is it family or school? Getting the question right. RSF: The Russell Sage Foundation Journal of the Social Sciences. 2016 Sep;2(5):18-33. https://doi.org/10.7758/RSF.2016.2.5.02\u003c/li\u003e\n\u003cli\u003eBronfenbrenner U. The ecology of human development: Experiments by nature and design. Cambridge, MA: Harvard University Press;1979.\u003c/li\u003e\n\u003cli\u003eJohnson GM, Puplampu KP. Internet use during childhood and the ecological techno-subsystem. Canadian Journal of Learning and Technology. 2008;34(1):19-28. http://dx.doi.org/10.21432/T2CP4T\u003c/li\u003e\n\u003cli\u003eNational Bureau of Statistics of China. [Annual monitoring report on rural migrant workers 2022] [Internet]. Beijing: National Bureau of Statistics of China; 2023 Apr 28 [cited 2024 Dec]. Available from: http://www.stats.gov.cn/sj/zxfb/202304/t20230427_1939124.html\u003c/li\u003e\n\u003cli\u003eWei JY. [Being together! Report on the development of children of migrant population in China, 2021] [Internet]. Beijing: New Citizen Program; 2022 Jan 12 [cited 2024 Dec]. Available from: https://m.thepaper.cn/baijiahao_16255384\u003c/li\u003e\n\u003cli\u003eArain M, Haque M, Johal L, Mathur P, Nel W, Rais A, Sandhu R, Sharma S. Maturation of the adolescent brain. Neuropsychiatric disease and treatment. PubMed Central. 2013 Apr 3;9: 449-61. https://doi.org/10.2147/NDT.S39776\u003c/li\u003e\n\u003cli\u003eHart SR, Stewart K, Jimerson SR. The student engagement in schools questionnaire (SESQ) and the teacher engagement report form-new (TERF-N): examining the preliminary evidence. Contemporary School Psychology. 2011;15(1):67-79. https://doi.org/10.1007/BF03340964\u003c/li\u003e\n\u003cli\u003eGresham FM, Elliott SN. Social skills improvement system social emotional learning edition rating forms. Bloomington, MN: Pearson Assessments; 2017.\u003c/li\u003e\n\u003cli\u003eKline RB. Principles and practice of structural equation modeling.\u003cem\u003e \u003c/em\u003e4th ed. New York: Guilford Press; 2016.\u003c/li\u003e\n\u003cli\u003eBoss P, Bryant CM, Mancini, JA. Family stress management: a contextual approach.3rd ed. Sage Publications; 2017. https://doi.org/10.4135/9781506352206\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mobile Devices, School Engagement, Social-Emotional Learning (SEL), Adolescent Development, Left-Behind Children (LBC), Structural Equation Modeling (SEM)","lastPublishedDoi":"10.21203/rs.3.rs-5675984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5675984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Digital transformation has fundamentally reshaped educational landscapes and profoundly influences youth development post-COVID-19, particularly affecting vulnerable populations like left-behind children (LBC). This study investigates how adolescents' mobile device use influences their social-emotional learning (SEL) competencies through school engagement, examining differential patterns between LBC and non-LBC. Through structural equation modeling (SEM) of data from 405 junior secondary school students (201 LBC) in rural China, this study analyzed four device-related factors: usage frequency, attitudes, adult supervision, and technoference (technology interference). Results demonstrated that technology's developmental influence operated primarily through school engagement mediation, with technoference emerging as the primary risk factor (total effect = -0.341) and supervision showing consistent protective effects (total effect = 0.228). The mediation patterns demonstrated heightened intensity among LBC, particularly for technoference (β = -0.428 vs -0.261) and supervision (β = 0.293 vs 0.142), suggesting increased vulnerability coupled with enhanced responsiveness to protective factors. Despite family separation and LBC's higher mobile phone reliance (t = 2.166) and decreased SEL competencies (t = -2.701), school engagement exhibited compensatory effects by maintaining developmental trajectories. While behavioral engagement showed group differences, affective and cognitive dimensions remained stable, indicating that family separation primarily affects engagement manifestation rather than underlying educational connection. These findings advance understanding of how digital experiences transfer between home and school while highlighting educational institutions' potential compensatory role in stabilizing developmental trajectories for vulnerable populations. The findings advocate coordinated interventions to leverage technology's potential while supporting diverse family structures in increasingly digital learning environments.","manuscriptTitle":"Mobile Devices and Social-Emotional Development: School Engagement as Mediator and Its Compensation for Left-behind Children in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 12:17:42","doi":"10.21203/rs.3.rs-5675984/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-16T13:58:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-31T22:11:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-21T01:56:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74348065854151710269638669105908214804","date":"2025-06-20T11:43:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-20T06:00:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-20T04:55:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Psychology","date":"2025-06-19T21:21:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e62a83e2-b2ef-4777-8270-4244829f9b92","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T14:24:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 12:17:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5675984","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5675984","identity":"rs-5675984","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}