Adaptive Learning Control Mechanisms of University Students in the Digital Age

Adaptive Learning Control Mechanisms of University Students in the Digital Age | 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 Adaptive Learning Control Mechanisms of University Students in the Digital Age Xiao Lu Guan, Wei Nan Chen, Yanru Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8402334/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the digital era, the development of information technology has brought profound changes to the self-regulated learning mode of university students. However, how students engage in adaptive control learning under the integration of information technology has yet to be empirically investigated. This study introduces adaptive control theory into the field of university students’ learning, defines the concept of students’ adaptive control learning, and proposes four key elements of adaptive control learning: expected learning goals, learning strategies design, learning outcomes assessment, and feedback regulation mechanisms. The confirmatory factor analysis (CFA) and structural equation model (SEM) were conducted to assess the current status of students’ adaptive control learning in one university of China. The results showed that (1) The overall level of Chinese undergraduate learners' learning outcomes assessment is generally low; (2) Learning strategies design could directly and significantly positively predict the achievement of expected learning goals; (3) Learning strategies design indirectly affects the achievement of expected learning goals through the sequential mediating role of learning outcomes assessment and feedback regulation mechanisms. The research reveals the operation mechanism of adaptive control learning, and tests the positive effects of adaptive control theory on learning in the digital era, providing insights into the change of learning pattern in the digital era. Adaptive control theory University students learning Learning strategies design Learning outcomes assessment Feedback regulation mechanisms Figures Figure 1 Figure 2 Figure 3 Introduction In the digital era, as "Internet Plus", "big data" and "artificial intelligence technology" enter the field of higher education, the deep integration of information technology and educational methods has become an inevitable trend of future higher education (Yan, 2024 ). In particular, how university students, as highly individualized cognitive subjects, can adapt to the changes in information technology, constantly update learning patterns, obtain better learning results, and meet their diverse learning needs has become a critical issue of academic research (Zhong & Wang, 2019 ; Li, 2024 ). Previous studies indicate that the key to changing the learning mode influenced by information technology is the transformation of learning subjects from a "passive recipient of knowledge" to an "active constructor of knowledge" (Yang, 2020 ). The more intuitive impact of learning in the digital age is the change in the channels and ways in which learners acquire knowledge, while the more profound and implicit change is the empowerment of learning subjects by information technology, which inspires learners to actively update their learning concepts and build learning systems (Yan & Zhang, 2023 ). However, Previous studies have generally focused on the construction of adaptive learning systems at the technological level and have not sufficiently explored learners' autonomous learning behaviors, especially the lack of exploration of the relationship between students' learning goals, learning processes, and learning outcomes in the context of information technology (Kautish, 2021; García-Pérez, 2021). Therefore, by placing learners at the center of the learning process and considering their unique characteristics, enhancing their subjective initiative, updating learning concepts and behavior patterns, transforming the learning process and knowledge processing paradigms, exploring a learning approach suitable for the digital age has considerable theoretical value and practical significance. This study has applied the theory of adaptive control to the field of higher education, defined the concept of adaptive control learning, and constructed a theoretical model of adaptive control learning. It deeply analyzed the key elements and their role relationship of adaptive control learning, focused on exploring the adaptive change process of Chinese undergraduate students in the context of information technology integration learning, and proposed a learning mode that conforms to learners' learning styles and meets their learning needs. Literature Review Adaptive Control Theory: A Novel Perspective on University Students' Learning in the Context of Information Technology Integration Adaptive control theory, which aims to overcome or reduce the deterioration of the system control effect caused by external disturbances, is derived from the automatic control theory of the control engineering field. The theoretical assumption is that any system faces uncertainties from within or outside the system during its actual operation. These objective uncertainties will reduce the effect of system operation. Thus, adaptive control is a feedback control system that can automatically adjust its characteristics according to environmental changes. The system will continuously adjust the parameters of the controller based on the actual control effect, so that it can better adapt to the changes in the controlled object, to make the system operate in an optimal state according to some set standards (Yu & Wang, 2012). Specifically, the adaptive control system usually consists of two loops, with the inner loop consisting of the regulator and the controlled object as an adjustable system, and the outer loop consisting of the reference model and the adaptive mechanism (see Fig. 1 ). The characteristics of the reference model are set to the desired state of the control system, in effect an ideal control system whose output represents the desired performance. The adjustable system consists of a controlled object, a feedforward controller and a feedback controller. When the output of the reference model differs from that of the actual controlled object, the adaptive mechanism must make a decision to change the parameters of the regulators (including the feedforward controller and feedback controller ) or generate auxiliary inputs to eliminate errors and make the process output consistent with the output of the reference model (Han, 2011). Wang, Q. L. (2012), introduced adaptive control theory into the research of adaptive career development management systems for enterprise core employees. Based on the actual management of enterprises and focusing on the current enterprise career development management problems and challenges, starting from the career development demands of core employees, she proposed the concept of adaptive career development management and constructed an adaptive career development management system for core employees in enterprises (p. 5). This study introduces the theory of adaptive control into the study of learners' learning behavior in the digital era, based on the actual learning situation of learners, focusing on the need to face the changes in their characteristics and the external environment when they learn and develop, and proposes the concept of adaptive control of learning from the learners' existing cognition and learning styles. Adaptive control learning refers to the constructive process of selecting appropriate learning strategies and methods in the learning environment of information technology, and providing continuous feedback to control learning to achieve the optimal state according to some set standards, so as to achieve individual specific learning goals in terms of knowledge, competence and quality (Wang & Xie, 2017 ). From the perspective of adaptive control theory, the learning of adaptive control includes four key elements: expected learning goals, learning strategies design, learning outcomes assessment, and feedback regulation mechanisms. Among these, firstly, the expected learning goals are what learners will get at the end of learning a certain content, and the effectiveness of learning depends on whether the learners have achieved the learning goals. In other words, the expected learning goals are both the starting point of adaptive control learning and the end point of the learners' learning activities, and by setting learning goals, it provides reference standards for subsequent learning and ensures that learning is fully controlled. Previous studies have shown that, under the driving force of learning objectives, the basis for organizing and implementing the learning process, the standards for monitoring and evaluating the learning quality, and the feedback adjustment orientation are important indicators of learners' active learning (Hsieh, 2022 ). Secondly, learning strategies design, is a fundamental step in ensuring the successful implementation of adaptive control learning. Nowadays, learners have gained more ability to control learning, including the choice of personalized learning methods, learning time and place, Relevant studies have shown that learners organize and construct their own learning contexts by using resource management strategies to manage their learning environment and external resources, which has a positive impact on the achievement of learning goals (Lee, 2021). Thirdly, the learning outcomes assessment is the intermediate link between learning strategies design and feedback assessment mechanisms. Continuous monitoring through the assessment of the learner's self-learning behavior and objective test evaluation, real-time monitoring of the degree of match between learning achievements and learning objectives, and then adjust the starting point for the next round of learning after feedback, is an important link to promote learners to reach the best level of learning state under the integration of information technology, to achieve the expected learning outcomes, and to continue iterative and cyclic learning (Meina & Min, 2022 ). While, the role of learning monitoring and evaluation, which refers to the behavior of individuals who actively monitor their own learning processes and evaluate learning outcomes, in mediating between the learning strategies design and the learning goals achievement is unclear. Finally, the feedback assessment mechanisms refer to the process of regulating and controlling the cognitive activities of learning through the use of various strategies when learners realize that the learning outcomes do not meet the set learning objectives, which is an important part of ensuring adaptive control of learning outcomes and achieving learning objectives (Kadioglu-Akbulut & Uzuntiryaki-Kondakci, 2021 ). The feedback assessment mechanism is influenced by many factors, such as the management of the learning environment and external resources, particularly time management, the learning environment, and seeking help externally. Furthermore, the most important factor is the learner's ability to engage in feedback regulation as the learning subject, including the ability to think, act, adjust, monitor, and control (Li, Wong, Yang, & Bell, 2020 ). Related studies have confirmed that effective evaluative feedback enhances learning performance and the level of self-regulated learning (Liao, et al, 2024 ). However, the mediating role of the feedback regulation mechanisms between learning strategies design and learning goals achievement is still unclear, and few studies have explored the chain of mediating roles in learning outcomes assessment and feedback regulation mechanisms. In summary, this study takes as its theme "How do university students learning in the digital era" uses adaptive control theory as its analytical framework, and explores the impact of learning strategies design on the learning goals achievement through the intermediation of learning outcomes assessment and feedback regulation mechanism. Learning Strategies Design and Learning Goals Achievement Learning strategies design includes the choice of learning methods and the design of learning environments. Previous studies have mostly focused on the relationship between learning strategies and learning outcomes, and researches have shown that university students' learning strategies have a significant positive impact on learning outcomes (Guterman & Neuman, 2021 ). Research has confirmed that students' motivation, learning methods, and types of learning content can affect learning outcomes, and that students' motivation and engagement in learning are crucial to their achievement (Shi & Du, 2022). In addition, studies have also confirmed that there is a significant positive correlation between learning strategies and students' satisfaction with learning (Matcha, 2019 ). Meanwhile, students who flexibly use learning strategies have been shown to have better control over the learning process, which affects their self-efficacy, academic emotions and learning outcomes (Wang & Hsieh, 2022 ). As Händel, M. (2020) confirms, when learners have the ability to initiate metacognitive, cognitive, affective, motivational, and behavioral processes in order to take action to achieve their learning goals and persevere until they succeed and do not give up easily when they encounter setbacks, because their learning satisfaction will increase when they are fully engaged in their learning activities (Händel, Harder, & Drese, 2020). Learning Outcomes Assessment as a Mediator Constructivist theory suggests that the sudden shift in control of learning from teachers to students presents a challenge to the learner and requires them to take more responsibility for constructing the individual learning process (Bonk & Doo, 2020 ). Learning outcomes assessment in this study is related to learners' cognitive and meta-cognitive processes, and is an assessment of their cognitive, emotional, and behavioral inputs into the learning process as well as the assessment of objective tests. Regarding the relationship between learning outcomes assessment and learning goals achievement, some researchers have argued that learners' self-evaluation of their learning stages has a positive impact on their learning outcomes. In addition, the ability to plan, monitor, and manage learning affects the speed at which learning objectives are achieved, and by monitoring their learning behavior, learners can not only select relevant learning resources based on their individual needs, abilities, and interests, but also can be significantly and positively motivated to move closer to their goals (Le, 2019; Yan, 2020 ; Chang, 2022). As stated by Zhao, R. ( 2022 ), regarding the relationship between learning strategy design and learning outcomes assessment, recent research has confirmed the effectiveness of learning outcomes assessment across populations and settings (Zhao & Ling, 2022 ). In summary, the study concluded that learning outcome assessment has a mediating effect in the role of learning strategies design on learning goals achievement. Feedback Regulation Mechanisms as a Mediator It has been proved that feedback-regulated learning is related to the achievement of learning goals, and effective feedback affects the motivation to achieve learning goals (Liu, Draper, & Dawson, 2022 ). In addition, students with high levels of feedback regulation are able to flexibly use their skills to achieve higher learning goals, therefore, when students are to engage in feedback-regulated learning, they need to have the ability to highly regulate their learning (Cai & Kannan., 2020). However, studies have shown that many learners find it difficult to provide feedback regulation in the information technology environment. Specifically, when students lack feedback regulation support, coupled with a lack of learning motivation or limited learning resources, they often overestimate their learning effectiveness and stop learning before grasping the knowledge, resulting in negative feedback and hindering the achievement of learning goals(Fyfe, 2020 ). Therefore, Rawad, C. & Maria, A. I. (2021) argued that the importance of learners for obtaining feedback regulation support in the information technology environment (Rawad & Maria, 2021 ). Learning Outcomes Assessment and Feedback Regulation Mechanisms as Sequential Mediators Previous research suggests that there is a significant positive correlation between learning outcomes assessment and feedback regulation mechanisms may be due to the fact that internal feedback has a positive impact on learners' learning (Wisniewski, Zierer, & Hattie J, 2020 ). Combined with the adaptive control learning, learners need to constantly monitor the learning outcomes during the learning process and self-regulate based on the feedback, and then make decisions to ultimately achieve the desired learning goals. Therefore, the learning outcomes assessment and the feedback regulation mechanisms based on the difference in outcomes are important links to ensure the effectiveness of adaptive control and to achieve learning goals. The Hypothesized Model Although previous studies have reviewed the interconnection of the four key constructs in fields such as education and psychology, no research has explored and demonstrated the complex relationships between them. Learners as the main body of learning how much influence the learning process, through the design of learning strategies, assessment of learning outcomes, and feedback to adjust learning, and ultimately achieve the learning goals achievement, and ultimately contributing to engagement deserve more empirical tests. To bridge this gap, SEM (see Fig. 2 ) was utilized to elucidate the interconnections among those constructs. This study has the following hypotheses: Hypothesis 1 Learning strategies design positively predict learning goals achievement. Hypothesis 2 Learning outcomes assessment mediates the relationship between Learning strategies design and learning goals achievement. Hypothesis 3 Feedback regulation mechanisms mediate the relationship between Learning strategies design and learning goals achievement. Hypothesis 4 Learning strategies design predicts learning goals achievement through the sequential mediating roles of learning outcomes assessment and feedback regulation mechanisms. Methods Instruments According to the theoretical model and hypothesis, the core elements of this study include learning goal achievement, learning strategies design, learning outcomes assessment, and feedback regulation mechanism. Combining the literature review and established scales, we developed “The Questionnaire on Factors Influencing University Students' Learning in the Digital Age” and listed some of the measurement items in Table 1 . Table 1 Sample questionnaire on factors influencing university students' learning in the digital age First-level indicators Second-level indicators Sample Questions Learning goals achievement (LGA) (Elliot & McGregor, 2001 ) Talent cultivation objectives (TCO) University studies are designed to cultivate basic theories, specialized knowledge, and comprehensive abilities to solve complex problems in one's major field of study. Learning achievement objectives (LAO) I worry about getting poor grades in my studies, and this often motivates me to keep studying hard. Learning mastery objectives (LMO) I hope to master everything I need to learn. Learning strategies design (LSD) (Weinstein, 1982 ) Learning modality choice (LMC) During the learning process, I actively seek out and learn knowledge related to the subject as much as possible. Learning Context Construction (LCC) I obtain resources useful for my studies through books, websites, and software. Learning outcomes assessment (LOA) (Dejone, 1991 ) Self-evaluation of behavior (SEB) I can organize my study plan reasonably to make it more systematic. External objective evaluation (EOE) During the learning process, I will frequently communicate with teachers. Feedback regulation mechanisms (FRM) (Kaplan, et al, 2017 ) Feedback regulation ability (FRA) Through repeated learning, I have been able to develop my own learning model and system. Feedback regulation support (FRS) When I encounter limitations in learning this course, I can use online resources and technical tools to continue learning. This questionnaire with two parts, the first part involves ten questions referring to students’ demographic characteristics (e.g., gender, age, grade, and hometown) and their use of the information technology. The second section contains 46 items of students’ perception of the above five core elements under the fusion of information technology learning. The measurement tools used the Likert five-point scale to evaluate students’ perception with the markers, from “ Strongly disagree ”, “Disagree”, “Neutral”, “Agree”, and “Strongly Agree”. Participants We recruited participants from 14 universities in China using a convenience sampling method. A total of 1200 university students voluntarily participated in the study. Then, we received 1190 responses and retained 977 usable responses based on the result of data filtering, leading to a response rate of 82.1%. Of these available samples, the numbers of female and male students were 550 (56.29%) and 427 (43.7%), respectively. Participants were recruited via social media and e-mail. They were informed about who the researchers were, the study’s focus on how university students adaptive control their learning in the digital age, and its estimated duration of 15 min. In addition to the relatively small number of 127 (13.00%) in the fourth grade of the university, the remaining three grades are relatively average, 304 (31.12%) in the first grade, 281 (28.76%) in the second grade, and 265 (27.12%) in the third grade. Respondents from different disciplines, with 206 (21.08%) from humanities, 205 (20.98%) from social sciences, 362 (37.05%) from engineering, 143 (14.64%) from science, and 61 (6.25%) from medicine and others. Data Analysis The data from the questionnaires were processed with SPSS 25.0 and Smart-PLS 4.0. First, descriptive statistics and correlation analysis were carried out on the variables; Second, the structural equation model was used to determine the correlation of factors and the mediating effect. Results Reliability and validity of instruments The analysis results show that the factor loading of all items was more than 0.708, the Cronbach's Alpha and the combined reliability (CR) were above 0.7 (see Table 2 ), and the convergence validity (AVE) was more than 0.5. According to Sekaran and Bougie ( 2003 ), the Cronbach’s Alpha coefficient above 0.70 is considered acceptable and above 0.80 is good. Therefore, the items in the questionnaire were regarded to be highly reliable. Table 2 The reliability and validity of each indicator's questionnaire items First-level ndicators Second-level indicators Cronbach’s alpha CR AVE Items Learning goals achievement (LGA) Talent cultivation objectives (TCO) 0.926 0.951 0.692 4 Learning achievement objectives (LAO) 0.843 0.898 0.765 4 Learning mastery objectives (LMO) 0.909 0.951 0.694 6 Learning strategies design (LSD) Learning modality choice (LMC) 0.950 0.855 0.775 10 Learning Context Construction (LCC) 0.897 0.920 0.807 4 Learning outcomes assessment (LOA) Self-evaluation of behavior (SEB) 0.951 0.855 0.873 9 External objective evaluation (EOE) 0.855 0.926 0.819 3 Feedback regulation mechanisms (FRM) Feedback regulation ability (FRA) 0.820 0.845 0.679 4 Feedback regulation support (FRS) 0.855 0.910 0.688 2 We tested the structural validity of the questionnaire by KMO and Bartlett’s testing (see Table 3 ). The value of KMO with 0.978 (> 0.7) means that the structural validity of the questionnaire was well (Kaiser, 1974 ). Table 3 The result of KMO and Bartlett’s test. Kaiser–Meyer–Oklin measure of sampling adequacy 0.978 Bartlett’s test of sphericity Approximately Chi-square 45166.591 df 1035 Significance 0.000 Descriptive statistics and correlation analysis of the survey According to the mean value of the variables in Table 4 , respondents have the highest recognition of the talents training objective in the dimension of learning goal achievement (M = 4.38, SD = 0.750), followed by the learning mastery objectives (M = 4.21, SD = 0.792), and the learning achievement objectives is the lowest (M = 3.94, SD = 0.913). Table 4 The mean, standard deviation, and correlation matrix of each variable M SD correlation 1 2 3 4 5 6 7 8 9 Talent cultivation objectives (TCO) 4.21 0.761 - Learning achievement objectives (LAO) 4.23 0.746 0.887 - Learning mastery objectives (LMO) 4.01 0.844 0.838 0.784 - Learning modality choice (LMC) 4.01 0.848 0.796 0.887 0.857 - Learning Context Construction (LCC) 4.20 0.752 0.863 0.827 0.843 0.807 - Self-evaluation of behavior (SEB) 4.18 0.760 0.808 0.784 0.789 0.777 0.849 - External objective evaluation (EOE) 4.38 0.750 0.629 0.584 0.541 0.519 0.580 0.537 - Feedback regulation ability (FRA) 3.94 0.913 0.661 0.606 0.675 0.638 0.641 0.593 0.524 - Feedback regulation support (FRS) 4.21 0.792 0.790 0.728 0.675 0.624 0.711 0.664 0.620 0.690 - Meanwhile, respondents pay more attention to Learning modality choice (M = 4.21, SD = 0.761) and the Learning context construction(M = 4.23, SD = 0.746) in the learning strategies design. The attention to self-behavior assessment (M = 4.01, SD = 0.844) and external objective evaluation (M = 4.01, SD = 0.848) of learning outcomes assessment was general. Then, university students have a good understanding of the effectiveness of the feedback regulation mechanism in the learning process(M = 4.20, SD = 0.752), but also show a willingness to seek the support of feedback regulation (M = 4.18, SD = 0.760). SEM Analysis In this study, PLS-SEM operation in Smart-PLS 4.0 software was used to test the structural model according to the model collinearity test, R 2 test, and Q 2 test. The results show that the inner VIF values of all measured variables were between 1.000 and 5.979, and the outer VIF values were between 1.688 and 4.096, both of which were less than 10; the R 2 values of all endogenous latent variables were greater than 0.67, and the minimum was 0.671. The Q 2 explanatory power of the endogenous latent variables in the structural model was more than 0, and the minimum is 0.394. The SRMR of the model fitting parameter test was 0.056, reaching the standard of less than 0.08. Based on the above parameter estimation results, it can be clearly stated that the structural model of this study had good fitting, there is no collinearity problem, the predictive is high prediction correlation, and the explanatory power of the model is good. Through the Bootstrapping operation in Smart-PLS 4.0 software, the path coefficient analysis of the structural model is shown in Fig. 3 . Among them, the T statistic is greater than 1.96, and the p-value is less than 0.05, indicating that it has a significant positive impact. The results show that university students' learning strategies design has a considerable positive impact on learning goals achievement (T = 45.898, p < 0.001), which supports Hypothesis H1 (Fig. 3 ). The mediating effect test results of this study are shown in Table 5 . In addition to the direct impact of learning strategies design on learning goals achievement, the indirect effect coefficient of learning outcomes assessment between learning strategies design and learning goals achievement is 0.086 (T = 2.173, p < 0.05), so it is assumed that H2 is established. The indirect effect coefficient of the feedback regulation mechanism between learning strategies design and learning goals achievement is 0.063 (T = 2.105, p < 0.05), so it is assumed that H3 is established. The chain mediating effect of learning strategies design on learning goals achievement through learning outcomes assessment and feedback assessment mechanisms is 0.044 (T = 2.229, p < 0.05). It can be seen the chain mediating hypothesis H4 is established, and the above indirect effect hypothesis confidence interval does not contain 0. Table 5 Unstandardized and standardized path coefficients Effect categories B SE T p Bias-corrected 95% CI Lower Upper Direct effect 0.627 0.055 11.483 0.000*** 0.519 0.734 Total indirect effect 0.194 0.055 3.534 0.000*** 0.085 0.302 Total effect 0.821 0.018 45.898 0.000*** 0.784 0.853 Specific indirect effects Learning strategies design→ Learning outcomes assessment→ Learning goals achievement 0.086 0.040 2.173 0.030* 0.009 0.163 Learning strategies design→ Feedback regulation mechanisms→ Learning goals achievement 0.063 0.030 2.105 0.035* 0.008 0.125 Learning strategies design→ Learning outcomes assessment→ Feedback regulation mechanisms→ Learning goals achievement 0.044 0.020 2.229 0.026* 0.006 0.084 Note: * indicates a significant P-value at the 0.05 level; **indicates a significant P-value at the 0.01 level; *** indicates a significant at the 0.001 level. Furthermore, the data results show that in the chain mediating model of the influence of learning strategies design on learning goals achievement, the total effect value is 0.194 (T = 3.534, p < 0.001), and the direct effect of this path is significant, indicating that this model is a partial mediating effect model. Among the three specific indirect paths, the relative proportion of the specific indirect effects of this path mediated by learning outcomes assessment is higher than that of the other two specific indirect effects. This indicates that learning strategies design does indeed affect the achievement of learning goals through learning outcomes assessment and feedback regulation mechanisms, and the mediating effect of learning outcome assessment is significant. Discussion This study aims to provide an in-depth analysis of the key elements of adaptive control learning. The direct positive influence of learning strategies design in relation to learning goals achievement was examined. In addition, the study also explored the mediating role of learning outcomes assessment and feedback regulation mechanisms between learning strategies design and learning goals achievement. Analysis of Key Elements of Adaptive Control Learning Expected learning goals, learning strategies design, learning outcomes assessment, and feedback regulation mechanisms are four key elements of adaptive control learning. As can be seen from the data in Table 3 , first of all, in terms of the expected learning goals achievement, data shows that Chinese undergraduate learners have the highest recognition of talent cultivation goals, while the lowest recognition of academic performance goals. This indicates that academic performance goals are more guided by test scores, and lacks dynamic evaluation of students’ innovation ability and problem solving ability. In contrast, the assessment of the achievement of talent cultivation goals based on long-term tracking, and this combination of"process oriented + outcome oriented" approach is more likely to gain learners’ recognition (Yang, 2024 ). Secondly, in terms of learning strategies design, the respondents' scores on the choice of learning methods and the design of the learning environments are both at a high level. This indicates that technology empowerment has provided learners with a diversified choice of learning modes, which indirectly reflects the core competitiveness of Chinese Undergraduate Learners as "digital natives", whose learning strategy design has progressed from the selection of a single method to the systematic construction of "tool-scenario-goal" in the technology-permeated educational ecology (Lv & Shi, 2025 ). Thirdly, in terms of the learning outcomes assessment, the scores of its sub items are all at a relatively low level, which is consistent with the evidence from various research evidence showing that learners’ learning outcomes assessment is generally at a low level. This is due to two reasons: on the one hand, the theoretical exploration has not effectively supported the implementation of the assessment of learners' learning outcomes. On the other hand, in practical exploration, the concept of learners' self-assessment is insufficient and the tools of learners' assessment lack scientificity (Liu, 2017 ). Therefore, in the digital era, we should focus on the learners' real learning process, enhance their awareness of learning outcome evaluation, explore scientific and effective assessment tools with the help of information technology, and build a sustainable assessment paradigm, so that, as the main body of learning, learners pay more attention to improving their skills, thereby realizing their self-growth and self-development (Zhang & Zhang, 2023 ). Finally, the overall score of the feedback assessment mechanisms is high, indicating that in the digital era, Chinese Undergraduate Learners have gradually realized that undergraduate learning needs to change from "want me to learn" and "I was taught to learn" to "I want to learn" and "I can learn", and they are able to adjust their learning strategies in time according to feedback (Chen, et al, 2022 ). In addition, it also clearly demonstrates that university students are able to effectively receive feedback from various sources during the learning process and adjust their learning methods, plans, and attitudes accordingly(Zhang & Xing, 2023 ). This ability is crucial for improving learning efficiency and effectiveness (Qi, et al, 2024 ). The Direct Impact of Learning Strategies Design on Learning Goals Achievement. Bloom's findings confirm that there is a significant correlation between the mastery of learning strategies and the achievement of learning goals (Lenchuk & Ahmed, 2021 ), and structural equation modeling further validates the hypothesis that strategic choice of learning methods and design of the learning environments can significantly positively influence the learning goals achievement. According to adaptive control theory, learning strategies design is the behavioral choices that make during the learning process in response to the influence of the information technology environment and in accordance with their own learning styles in order to achieve their learning goals (Putri, et al, 2021 ). The design of learning strategies is not only influenced by the individual behavior of the learners, but is also related to the learning environment in which they are situated. Adaptive control learning requires learners to adapt to the influence of external environment and the constant change of internal individual factors during the learning process, while the external environment is still mainly through the internal self-regulation, which in turn plays a role in the effectiveness of learning (Ma, Jiang, & Liu, 2023 ). Therefore, the key to achieving higher learning goals for learners lies in improving self-motivation and mastering learning methods, especially being able to effectively control the learning process (Ma & Liu, 2024). On the one hand, strategic learning represents learners' higher-order thinking, critical reflection and problem-solving skills. In the ever-changing information technology environment, learners continue to internalize their knowledge and skills by improving the process of acquiring, organizing, or transforming information in order to achieve talent development goals and gain a sense of self-efficacy, which contributes to learners' level of adaptation to the integration of information technology learning (Zhu, Xu, & Ma, 2025 ). On the other hand, learning emotion management is particularly important for learning. Positive emotions can stimulate both intrinsic motivation and extrinsic motivation, and in most cases have a positive impact on academic performance, while negative emotions have the opposite effect (Wu & Jing, 2021 ).Therefore, developing a reasonable learning planning and strengthening collaborative learning can significantly improve academic performance and promote the comprehensive improvement of learners' individual knowledge, ability, and quality (Liu, et al). Mediating Roles of Learning Outcomes Assessment and Feedback Regulation Mechanisms The complexity of the educational and teaching environment, as well as the diversity and differences of undergraduate learners in the fusion of information technology, determine that the individual learner factor plays a non-negligible role in the learning process (Wu, 2020 ). This study tested the chain mediating role of learning strategies design in adaptive control learning that affects learning goals achievement through learning outcomes assessment and feedback regulation mechanisms, which helps to explain the intrinsic mechanisms by which learning strategies design affects the achievement of expected learning goals. Specifically, firstly, learning outcomes assessment plays a key mediating role between the learning strategies design and the learning goals achievement, as it is related to learners' cognitive and meta-cognitive processes and is a manifestation of learners' responsibility for constructing their learning processes. Therefore, in order to achieve learning goals successfully and efficiently, learners need to motivate themselves to use learning strategies appropriately and consciously assess their learning activities (Zhu & Dou, 2022 ). Secondly, the learners' learning strategies design affects the learning goals achievement through feedback regulation mechanisms. In other words, as a means for learners to control the learning process, the feedback regulation mechanism can give full play to their learning subjectivity and dynamically adjust their cognitive strategies in a timely manner, thus achieving the development of learners towards the expected learning goals that have been set (Sun & Zhou, 2025 ). Therefore, multiple factors such as learning styles, learning motivation, learning content, learning emotions and learning environment need to jointly influence the learning process, which in turn affects the achievement of learners' learning goals (Xie & Wang, 2025 ). Finally, this study confirms that learning outcomes assessment and feedback assessment mechanisms play a chain mediating role between learning strategies design and learning goal achievement. That is to say, learners choose learning methods according to their individual characteristics, construct learning contexts, and monitor their own learning behaviors at all times, thus providing feedback regulation of the learning process to achieve learning goals (Johannes, 2022 ). Furthermore, the significant direct effect of learning outcomes assessment on the feedback regulation mechanisms should not be underestimated. This suggests that in adaptive control learning, in addition to the learning strategies design, the feedback regulation mechanisms are also directly affected by the learning outcomes assessment. The reason for this effect is that learners are required to compare learning outcomes with learning standards or learning objectives before making regulatory decisions, and the resulting deviations are used as reference information for adjusting learners' learning strategies (Huang & Ou, 2021 ). Implications Regarding the findings and discussions, this study identifies the following research implications. In terms of theoretical construction, this study constructs a research hypothesis model based on adaptive control theory. By exploring the relationship among learning strategies design, learning outcomes assessment, feedback assessment mechanisms, and learning goals achievement ,we fully understand the positive role of adaptive control theory on university students’ learning in the digital era. In terms of practice, this study reveals the operating mechanism of adaptive control learning. It emphasizes the use of university students as learning subjects in the integration of information technology and exert their learning initiative in the process of continuous integration and interaction with the information technology environment (Deng & Li, 2023 ). First of all, in order to better adapt to learning in the digital age, university students should take learning strategies design as the core, pay attention to the role of elements such as learning content, learning methods, and learning environments in the process of meaning construction (Hui & Xuan,2024). At the same time, they should use interactive communication technology to create learning situations and build a good interactive communication platform (Zhao, Brun, & Boyer, 2020 ). Secondly, the findings demonstrate the importance of learning outcomes assessment in the digital era. The core function of learning outcomes assessment is not to measure, but also to promote students' learning, which is "assessment for learning". Therefore, it is necessary for students to monitor the deviation of learning outcomes from learning goals, in order to adjust their learning behaviors in the next stage to ensure that they ultimately achieve the expected learning goals and avoid being influenced by the external environment (Kismihók, et al, 2020 ). Finally, the feedback regulation mechanism is a key element in a learning cycle, which helps students form a constantly improving learning state, thereby stimulating their learning motivation. Cognitive theory suggests that iterative feedback learning, as an efficient learning method, can encourage individuals to continually optimize their learning strategies and achieve self-learning, narrowing the gap between their current level of performance and expected learning goals, thereby enabling students to better learn knowledge (He, 2017 ). University students' learning is a periodic feedback regulation process (Wang,2021), therefore, it is necessary to enhance students’ "feedback literacy", accurately grasp the appropriate timing of feedback, and integrate feedback regulation into different stages such as “learning preparation, learning process and learning completion” (Han, 2020 ).By realizing the transition from external control to self-control, students can further meet their subject needs and enhance their self-efficacy (Wang & Chen, 2022 ). Limitations and Future Research Directions The current study has several limitations that should be taken into account when interpreting the results. Firstly, the participants in this study were all from one region of China and the sample size was also small, which reduces the generality of the results. Future studies should include participants from different countries to obtain more consistent results from a larger sample. Secondly, the complete reliance on self-report questionnaires in the current study may also lead to potential bias, although they have been verified to have high reliability and validity. Future research could explore the use of measures such as some qualitative interviews and controlled trials to better understand the relationship between elements of adaptive control learning in the digital era through a mixed research approach. Thirdly, the data collection focused on the collection at one point in time and did not take into account the chronological processes, so it was not possible to show how university students' learning in the digital era changed over time. A longitudinal study was included in the follow-up study to reveal the dynamic role of the relationship between the elements of adaptive control learning. Conclusion This study constructs an adaptive control learning model for university students in the digital era, which includes four links: expected learning goals, learning strategies design, learning outcomes assessment, and feedback regulation mechanisms. In addition, the study also found that learning in the digital era can be enhanced by fully coupling social environmental factors, strengthening the monitoring of one's own learning behavior, and improving self-regulation learning ability, so as to strengthen the learning initiative of college students, play the role of the main body of college students' learning, and promote the iterative cycle of the learning process. The results of this study emphasize the importance of learning outcomes assessment and feedback regulation mechanisms in improving learning effectiveness, and the need for continuous learning improvement through monitoring, evaluation, feedback, and adjusting of one's learning status. Declarations Acknowledgement The researchers would like to express sincere thanks to Graduate School of Education, Dalian University of Technology, for the invaluable support and resources provided throughout the course of this research. Author’s Contributions Xiao Lu Guan wrote the main manuscript. Wei Nan Chen collected data and conducted the statistical analysis. Yanru Liu supervised the research design, contributed to methodology refinement, and critically reviewed the manuscript. Funding The research is supported by the China Postdoctoral Science Foundation (Grant No.2024M750331) Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval This study was reviewed and grand an exemption from requiring formal ethical approval by the Institutional Review Board(IRB) of Dalian University of Technology. The exemption was granted because the research involved anonymized analysis of existing, non-sensitive educational data, posed no psychological or physical harm to participants, and did not involve the collection of identifiable private information. This exemption is in full accordance with Article 32 of the Measures for Ethical Review of Human Life Science and Medical Research (issued by the National Health Commission of China, February 18, 2023), which stipulates the exemption conditions for such minimal-risk research. Furthermore, all research procedures were conducted in line with the ethical principles outlined in the Declaration of Helsinki (1975) and its subsequent revisions (2024). Consent to participate All participants provided informed consent to take part in the study. Conflict of interest No potential conflict of interest was reported by the authors. Consent for Publication Not applicable. Clinical trial number Not applicable. References Bonk, C. J., & Doo, M.Y. (2020). Self-directed learning in MOOCs: exploring the relationships among motivation, self-monitoring, and self-management. Educ. Tech. Res, (68), 073-2093. Cai, Y., & Kunnan, A. J. (2023). Mapping the fluctuating effect of strategy use ability on English reading performance for nursing students: a multi-layered moderation analysis approach. Language Testing, 37(2), 280-304. Chang, Y., Li, J., & Huang, A. D. (2022). A Personalized Learning Service Compatible with Moodle E-Learning Management System. Applied sciences, 3. Chen, K. Q., Han, X. L., & Zheng, Z. F. (2022). Scene Construction and Application Model Analysis of Intelligent Education under Human-Machine Collaboration: A Review of Domestic and International Research on Human-Machine Collaboration Education over the Past Decade. Journal of Distance Education, 40(02), 3-14. Deng, M. J., & Li, Y. (2023) . Towards Technological Embodiment: Re-examining Learner Agency in the Information Technology Age. Research on Electrified Education, 44(08), 26-32. Dejone, K. (1991). Inventory Regulation Strategies. Educational leadership, (45), 108-116. Elliot, A. J., & McGregor, H. A. (2001). A 2*2 achievement goal frame-work. Journal of Personality and Social Psychology, (80), 501-519. Fyfe, E. R. (2020). This is easy, you can do it! Feedback during mathematics problem solving is more beneficial when students expect to succeed. Instructional Science. Brown SA, 48(1), 23-44. García-Pérez, D., Fraile, J., & Panadero, E. (2021). Learning strategies and self-regulation in context: How higher education students approach different courses, assessments, and challenges. Eur J Psychol. Educ, (36), 533–550. Guterman, O., & Neuman, A. (2021). Not all paths lead to success: Learning strategies and achievement among undergraduate students. Furth. High. Educ, (46), 115-127. Händel, M., Harder, B., & Dresel, M. (2020). Enhanced monitoring accuracy and test performance: Incremental effects of judgment training over and above repeated testing. Learning and Instruction, (65), 101245-101249. Han, Y., & Yueting, X. (2020). The development of student feedback literacy:the influences of teacher feedback on peer feedback. Assessment & Evaluation in Higher Education, 45(5), 680-696. Han, Z. Z., Chen, P. N. & Chen, S. Z. (2011). Adaptive Control. Basic Theory of Adaptive Control (pp.34-37). Beijing: Tsinghua University Press. He, K. K. (2017). A deep understanding of the essence of feedback and the planning and design of effective feedback—the second highlight that deeply inspired us from the American ‘Handbook of Educational Communication and Technology Research (4th Edition)’. China Educational Technology, (5), 1, 7-14. Hsieh, H. (2022). Effect of Deep Learning Approach on Career Self-Efficacy: Using Off-Campus Internships of Hospitality university Students as an Example. Sustainability, (14), 7594. Huang, Y., & Ou, J. (2021). The Relationship between Self-Directed Learning and Problem-Solving Ability: The Mediating Role of Academic Self-Efficacy and Self-Regulated Learning among Nursing Students.Int.Environ. Res. Public Health, 18. Hui, W. J., & Xuan, X. H. (2024). Transcending Breakthroughs: Exploring the Relationship Between Education and New Productivity. Educational research, 45(12), 71-83. Johannes, C. (2022). The impact of feedback mode on learning gain and self-efficacy: A quasi-experimental study. Learning in higher education, 10. Kadioglu-Akbulut, C.; & Uzuntiryaki-Kondakci, E. (2021). Implementation of self-regulatory instruction to promote students’ achievement and learning strategies in the high school chemistry classroom. Chem. Educ. Res. Pract, 22, 12-29. Kaiser, H. F. (1974). ‘An index of factorial simplicity’. Psychometrika, 39(1), 31-36. Kaplan, J., Montalembert, M., Laurent, P., & Fenouillet, F. (2017). ERICA–an instrument to measure individual and collective regulation of learning. European Review of Applied Psychology, 67(2), 79-89. Kautish, P., Hameed, S., Kour, P., Walia, S. (2021). Career beliefs, self-efficacy and VUCA skills: A study among generation Z female students of tourism and hospitality. Hosp. Leis. Sport Tour. Educ, 30, 100340. Kismihók, G., Zhao, C., & Schippers, M. (2020). Translating the concept of goal setting into practice: What 'else' does it require than a goal setting tool? In H. C. Lane, S. Zvacek, & J. Uhomoibhi (Eds.), 388-395. Lee, D.,Watson, S. L., & Watson, W. R. (2019). Systematic literature review on self-regulated learning in massive open online courses. Australasian Journal of Educational Technology, 35(1). Lee, M., & Na, H., Kim, B. (2021). Mediating effects of achievement emotions between peer support and learning satisfaction in graduate nursing students. Nurse Educ. Pract, 52. Lenchuk, I., & Ahmed. (2021) Tapping into Bloom Taxonomy's Higher-Order Cognitive Processes: The Case for Multiple Choice Questions as a Valid Assessment Tool in the ESP Classroom. Arab World English Journal, 160-171. Liao, X., Zhang, X., & Wang, Y. (2024). Design and implementation of an AI-enabled visual report tool as formative assessment to promote learning achievement and self-regulated learning:an experimental study. British journal of educational technology, 55, 1253-1276. Li, T. Z. (2024). Interaction and Relationship Reshaping Between University Teachers and Students in the Digital Education Era. China Educational Technology, (09), 110-115. Li, J., Wong, S.C., Yang, X., & Bell, A. (2020) Using Feedback to Promote Student Participation in Online Learning Programs: Evidence from a Quasi-Experimental Study. Educ. Technol. Res. Dev., 68, 485–510. Liu, H. Y., Zhu, Q., & Nie, Q. (2025). From ‘dependence’ to ‘independence’: The impact of in-depth counselling on the development of gifted college students with difficulties. Research on Higher Education in China, (02), 19, 26-35. Liu, S. T. (2017). Reflections on Promoting Reforms in Student Learning Outcomes Assessment in the ‘Double First-Class’ Initiative. University Education Science, (6), 38-41. Liu, Y., & Draper, J., & Dawson, M. (2022). The Relationship between Work Experience and Career Expectations: Career Decision-Making Self-efficacy as Mediator. Hosp. Tour. Educ, 4. Lv, G. Z., & Shi, M. (2025). Generative AI Empowers Digital Transformation in Higher Education: An Analysis Based on Stanford University. Research on Higher Engineering Education, (02), 176, 181-188. Ma, C. J., & Liu, P. J. (2025). Withdrawal from the Classroom: The Mechanisms Behind University Truancy and Rational Reflection. Chongqing Higher Education Research, 13(03), 117-127. Ma, J., Jiang, M., & Liu, X. X. (2023). How does an academic atmosphere promote graduate students' commitment to research? The role of cognitive demands and self-control. Tsinghua University Educational Research, 44(6), 93-102. Matcha, W. (2019). Analytics of Learning Strategies: Associations with Academic Performance and Feedback. In The 9th International Learning Analytics and Knowledge Conference (LAK19), USA: New York, 2019. Meina, Z., & Min, Y. D. (2022). The relationship among motivation, self‑monitoring, self‑management, and learning strategies of MOOC learners.Journal of Computing in Higher Education, (34), 321–342. Putri, K.Y.S., Bin, A. Z., & Nugrahaeni, T. (2021). Learning Management Strategy of Communication Studies through Blended Learning in Higher Education. International Journal of Interactive Mobile Technologies, 14(16), 117-132. Qi, J., Xu, Y. R., & Liu, J. A. (2024). The Impact of Generative Artificial Intelligence Tools on Critical Thinking and Independent Learning Abilities Among University Students. Research on Electrified Education, 45(12), 67-74. Rawad, C., & Maria, A. I. (2021). The moderating effect of social capital on co-regulated learning for MOOC achievement, Education and Information Technologies, (26), 899-919. Shi, D., Irwin, D., & Du, P. (2022). Languaging dynamics in interactive lecturing: Exploring an embodied approach to deep learning in L2 higher education contexts. Classr. Discourse, 9. Sekaran, U. & Bougie, R. (2003). Research methods for business. A skill building approach, Chichester:Wiley. Sun, L. H, & Zhou, L. (2025). Generative Artificial Intelligence Literacy: Conceptual Evolution, Framework Construction, and Pathways for Improvement. Modern distance education, (01), 11-21. Xie, X. Y., & Wang, J. (2025). The Experience and Implications of the Transformation of ‘the Learning Paradigm’ in American Community Colleges. Jiangsu Higher Education, (03), 119-124. Wang, C., & Hsieh, H. (2022). Effect of Deep Learning Approach on Career Self-Efficacy: Using Off-Campus Internships of Hospitality College Students as an Example. Sustainability, 14, 7594. Wang, C. J. (2021). Should I Stay or Should I Go? Linking Career Decision-Making Self-Efficacy to Intention to Stay in the Hospitality Industry Based on Internship Experience. Sustainability, 13, 10571. Wang, M. D., & Chen, K. D. (2022). Student Feedback Literacy from a Learning Science Perspective: Structural Model and Operational Mechanisms. China Educational Technology, (7), 104-114. Wang, Q. L. (2014). Research on Adaptive Career Development Management System for Core Employees in Enterprises. Theoretical basis (pp.42-63). Beijing: North China Electric Power University. Wang, Y. & Xie, Y. G. (2017). Theoretical Foundations for Research on the Elements and Structure of Precise Learner Models in Smart Learning Environments. Open Education Research, (6), 104-111. Weinstein. (1982). LASSI User`s Manual. Learning and Study Strategies Inventory. http://www.hhpublishing.com/_onlinecourses/study_strategies/BSL/admin/usersmanual. html. Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, (10), 3087. Wu, C. C., & Jing, B. (2021). Student’s Learning Strategies and Academic Emotions: Their Influence on Learning Satisfaction During the COVID-19 Pandemic. Frontiers inPsychology, 7. Wu, D. G. (2020) A Review and Reflection on the Evolution of Educational Technology: A Perspective from Online Teaching in Higher Education Institutions During the COVID-19 Pandemic. Research on Higher Education in China, (4). Yan, C. H. (2024). Promoting high-quality development of higher education in western China through digitalisation. Research on Higher Education in China, (1), 11, 14-22. Yan, J. Z. & Zhang, J. J. (2023). Learning the interpretation and construction path of the metaverse. Journal of Distance Education, 41(02), 36-47. Yan, Z. (2020). Self-assessment in the process of self-regulated learning and its relationship with academic achievement. Assess. Eval. High. Educ, (45), 224-238. Yang, D. (2024). Disembedding and Embedding: The Current Dilemma and Advancement Strategies of Smart Engineering Education-Based on the Theory of Technology Embedding. Higher Education Administration, 18(01), 33, 46-69. Yang, Y. D. (2020). Establishing a Normalised Model of Blended Online and Offline Teaching in Higher Vocational Colleges in the Context of Deep Learning. Jiangsu Higher Education, (06), 77-82. Yu, S. K., Wang, Q. L., & Zhang, O. (2012). Research on Employee Development Pathways and Their Adaptability. Research on Technology, Economics and Management, (08), 52-56. Zhao, R., & Ling, T. (2022). Latent profile analysis of university students’ self-management and self-monitoring in the links among motivation, engagement, and wellbeing. Front. Psychol, 13. Zhao, Z., Brun, A., & Boyer, A. (2020) New Measures for Offline Evaluation of Learning Path Recommenders. Addressing Global Challenges and Quality Education. 15th European Conference on Technology Enhanced Learning, 259-273. Zhang, X. X., & Xing, L. (2023). Building a Profile of University Students Aimed at High-Quality Learning: A Case Study of Shanghai Jiao Tong University. Research on Educational Development, 43(17), 76-84. Zhang, Y. Q., & Zhang, Y. L. (2023). The Historical Evolution and Basic Characteristics of Talent Cultivation Goals in China's Undergraduate Education: An Analysis Based on Policy Documents. Jiangsu Higher Education, (10), 13-21. Zhong, B. L., & Wang, X. F. (2019). The Popularisation of Higher Education in China: Opportunities, Challenges and Prospects. Research on Higher Education in China, (8), 7-13. Zhu, J. H., Xu, L. Y., & Ma, J. Y. (2025). How Generative Artificial Intelligence Empowers Student Learning: An Empirical Study Based on Self-Regulated Learning Behaviour Among University Students. Research on Higher Engineering Education, (02), 66-72. Zhu, M. N., Dou, M. Y. (2022). The relationship among motivation, self‑monitoring, self‑management, and learning strategies of MOOC learners. Journal of Computing in Higher Education, (34), 321-342. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8402334","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591257652,"identity":"79371561-ff8b-4b8d-97e5-532e361d437d","order_by":0,"name":"Xiao Lu Guan","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"Lu","lastName":"Guan","suffix":""},{"id":591257653,"identity":"68330d32-59b7-46d5-9d7e-374e06202d11","order_by":1,"name":"Wei Nan Chen","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"Nan","lastName":"Chen","suffix":""},{"id":591257654,"identity":"74df58c6-f522-4af7-9d32-742628c3b2fb","order_by":2,"name":"Yanru Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYPACCx4GZuaDDz5UyIG5Bx4Q1iLBw8Delmw444wxREsCEVoYGHjOqEnztkG0MODTYnD87LHPPAwSMgY3chgkZ84zkDO4dvgh0BY7Od0GHFrO5CXPBmrhMbiRe8Dg4zYDY8nZaQZALcnGZgdwaDmQY8wM0ZKXkDhz25/EfukEkJYDidtwaTn/BqYlx+Aw7xyDxDbp9A/4tdyA2XLmjGEzb4MB0JYc/LZI3nhjzDjHQIJH8nhbMuOMYyC/5BQcSDDA7Re+8znGDG8qbOz5DjMf//GhBhhit9M3f/hQYSeHS4sCWNwA08HYlYOAfANuuVEwCkbBKBgFEAAAybdd+lrFKTUAAAAASUVORK5CYII=","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yanru","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-12-19 08:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8402334/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8402334/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102866151,"identity":"802afead-93c7-42f8-94b2-f81ecae2d559","added_by":"auto","created_at":"2026-02-17 16:56:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130004,"visible":true,"origin":"","legend":"\u003cp\u003eAdaptive system model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8402334/v1/a07b837c07550947b16e63d1.png"},{"id":102866149,"identity":"5818e234-0402-4a1a-b767-36041730485a","added_by":"auto","created_at":"2026-02-17 16:56:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112516,"visible":true,"origin":"","legend":"\u003cp\u003eThe hypothesized model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8402334/v1/530e6ef028d9ea8890b859d5.png"},{"id":102964289,"identity":"14697ce4-74e2-451d-8261-7c65e5edadec","added_by":"auto","created_at":"2026-02-19 04:22:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":221327,"visible":true,"origin":"","legend":"\u003cp\u003eThe final mediation model\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8402334/v1/d63123baf6e6a3bc7125110b.png"},{"id":106994361,"identity":"c150135a-ce3b-4a55-9a05-6d0181e33114","added_by":"auto","created_at":"2026-04-15 15:07:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1624309,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8402334/v1/ad7ecead-b6a6-453f-a475-6558876edd13.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Learning Control Mechanisms of University Students in the Digital Age","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the digital era, as \"Internet Plus\", \"big data\" and \"artificial intelligence technology\" enter the field of higher education, the deep integration of information technology and educational methods has become an inevitable trend of future higher education (Yan, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In particular, how university students, as highly individualized cognitive subjects, can adapt to the changes in information technology, constantly update learning patterns, obtain better learning results, and meet their diverse learning needs has become a critical issue of academic research (Zhong \u0026amp; Wang, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Previous studies indicate that the key to changing the learning mode influenced by information technology is the transformation of learning subjects from a \"passive recipient of knowledge\" to an \"active constructor of knowledge\" (Yang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The more intuitive impact of learning in the digital age is the change in the channels and ways in which learners acquire knowledge, while the more profound and implicit change is the empowerment of learning subjects by information technology, which inspires learners to actively update their learning concepts and build learning systems (Yan \u0026amp; Zhang, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, Previous studies have generally focused on the construction of adaptive learning systems at the technological level and have not sufficiently explored learners' autonomous learning behaviors, especially the lack of exploration of the relationship between students' learning goals, learning processes, and learning outcomes in the context of information technology (Kautish, 2021; Garc\u0026iacute;a-P\u0026eacute;rez, 2021). Therefore, by placing learners at the center of the learning process and considering their unique characteristics, enhancing their subjective initiative, updating learning concepts and behavior patterns, transforming the learning process and knowledge processing paradigms, exploring a learning approach suitable for the digital age has considerable theoretical value and practical significance. This study has applied the theory of adaptive control to the field of higher education, defined the concept of adaptive control learning, and constructed a theoretical model of adaptive control learning. It deeply analyzed the key elements and their role relationship of adaptive control learning, focused on exploring the adaptive change process of Chinese undergraduate students in the context of information technology integration learning, and proposed a learning mode that conforms to learners' learning styles and meets their learning needs.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003e \u003cb\u003eAdaptive Control Theory: A Novel Perspective on University Students' Learning in the Context of Information Technology Integration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAdaptive control theory, which aims to overcome or reduce the deterioration of the system control effect caused by external disturbances, is derived from the automatic control theory of the control engineering field. The theoretical assumption is that any system faces uncertainties from within or outside the system during its actual operation. These objective uncertainties will reduce the effect of system operation. Thus, adaptive control is a feedback control system that can automatically adjust its characteristics according to environmental changes. The system will continuously adjust the parameters of the controller based on the actual control effect, so that it can better adapt to the changes in the controlled object, to make the system operate in an optimal state according to some set standards (Yu \u0026amp; Wang, 2012). Specifically, the adaptive control system usually consists of two loops, with the inner loop consisting of the regulator and the controlled object as an adjustable system, and the outer loop consisting of the reference model and the adaptive mechanism (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The characteristics of the reference model are set to the desired state of the control system, in effect an ideal control system whose output represents the desired performance. The adjustable system consists of a controlled object, a feedforward controller and a feedback controller. When the output of the reference model differs from that of the actual controlled object, the adaptive mechanism must make a decision to change the parameters of the regulators (including the feedforward controller and feedback controller ) or generate auxiliary inputs to eliminate errors and make the process output consistent with the output of the reference model (Han, 2011). Wang, Q. L. (2012), introduced adaptive control theory into the research of adaptive career development management systems for enterprise core employees. Based on the actual management of enterprises and focusing on the current enterprise career development management problems and challenges, starting from the career development demands of core employees, she proposed the concept of adaptive career development management and constructed an adaptive career development management system for core employees in enterprises (p. 5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study introduces the theory of adaptive control into the study of learners' learning behavior in the digital era, based on the actual learning situation of learners, focusing on the need to face the changes in their characteristics and the external environment when they learn and develop, and proposes the concept of adaptive control of learning from the learners' existing cognition and learning styles. Adaptive control learning refers to the constructive process of selecting appropriate learning strategies and methods in the learning environment of information technology, and providing continuous feedback to control learning to achieve the optimal state according to some set standards, so as to achieve individual specific learning goals in terms of knowledge, competence and quality (Wang \u0026amp; Xie, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). From the perspective of adaptive control theory, the learning of adaptive control includes four key elements: expected learning goals, learning strategies design, learning outcomes assessment, and feedback regulation mechanisms. Among these, firstly, the expected learning goals are what learners will get at the end of learning a certain content, and the effectiveness of learning depends on whether the learners have achieved the learning goals. In other words, the expected learning goals are both the starting point of adaptive control learning and the end point of the learners' learning activities, and by setting learning goals, it provides reference standards for subsequent learning and ensures that learning is fully controlled. Previous studies have shown that, under the driving force of learning objectives, the basis for organizing and implementing the learning process, the standards for monitoring and evaluating the learning quality, and the feedback adjustment orientation are important indicators of learners' active learning (Hsieh, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Secondly, learning strategies design, is a fundamental step in ensuring the successful implementation of adaptive control learning. Nowadays, learners have gained more ability to control learning, including the choice of personalized learning methods, learning time and place, Relevant studies have shown that learners organize and construct their own learning contexts by using resource management strategies to manage their learning environment and external resources, which has a positive impact on the achievement of learning goals (Lee, 2021). Thirdly, the learning outcomes assessment is the intermediate link between learning strategies design and feedback assessment mechanisms. Continuous monitoring through the assessment of the learner's self-learning behavior and objective test evaluation, real-time monitoring of the degree of match between learning achievements and learning objectives, and then adjust the starting point for the next round of learning after feedback, is an important link to promote learners to reach the best level of learning state under the integration of information technology, to achieve the expected learning outcomes, and to continue iterative and cyclic learning (Meina \u0026amp; Min, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While, the role of learning monitoring and evaluation, which refers to the behavior of individuals who actively monitor their own learning processes and evaluate learning outcomes, in mediating between the learning strategies design and the learning goals achievement is unclear. Finally, the feedback assessment mechanisms refer to the process of regulating and controlling the cognitive activities of learning through the use of various strategies when learners realize that the learning outcomes do not meet the set learning objectives, which is an important part of ensuring adaptive control of learning outcomes and achieving learning objectives (Kadioglu-Akbulut \u0026amp; Uzuntiryaki-Kondakci, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The feedback assessment mechanism is influenced by many factors, such as the management of the learning environment and external resources, particularly time management, the learning environment, and seeking help externally. Furthermore, the most important factor is the learner's ability to engage in feedback regulation as the learning subject, including the ability to think, act, adjust, monitor, and control (Li, Wong, Yang, \u0026amp; Bell, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Related studies have confirmed that effective evaluative feedback enhances learning performance and the level of self-regulated learning (Liao, et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the mediating role of the feedback regulation mechanisms between learning strategies design and learning goals achievement is still unclear, and few studies have explored the chain of mediating roles in learning outcomes assessment and feedback regulation mechanisms.\u003c/p\u003e \u003cp\u003eIn summary, this study takes as its theme \"How do university students learning in the digital era\" uses adaptive control theory as its analytical framework, and explores the impact of learning strategies design on the learning goals achievement through the intermediation of learning outcomes assessment and feedback regulation mechanism.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLearning Strategies Design and Learning Goals Achievement\u003c/h2\u003e \u003cp\u003eLearning strategies design includes the choice of learning methods and the design of learning environments. Previous studies have mostly focused on the relationship between learning strategies and learning outcomes, and researches have shown that university students' learning strategies have a significant positive impact on learning outcomes (Guterman \u0026amp; Neuman, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Research has confirmed that students' motivation, learning methods, and types of learning content can affect learning outcomes, and that students' motivation and engagement in learning are crucial to their achievement (Shi \u0026amp; Du, 2022). In addition, studies have also confirmed that there is a significant positive correlation between learning strategies and students' satisfaction with learning (Matcha, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Meanwhile, students who flexibly use learning strategies have been shown to have better control over the learning process, which affects their self-efficacy, academic emotions and learning outcomes (Wang \u0026amp; Hsieh, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As Händel, M. (2020) confirms, when learners have the ability to initiate metacognitive, cognitive, affective, motivational, and behavioral processes in order to take action to achieve their learning goals and persevere until they succeed and do not give up easily when they encounter setbacks, because their learning satisfaction will increase when they are fully engaged in their learning activities (Händel, Harder, \u0026amp; Drese, 2020).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLearning Outcomes Assessment as a Mediator\u003c/h3\u003e\n\u003cp\u003eConstructivist theory suggests that the sudden shift in control of learning from teachers to students presents a challenge to the learner and requires them to take more responsibility for constructing the individual learning process (Bonk \u0026amp; Doo, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Learning outcomes assessment in this study is related to learners' cognitive and meta-cognitive processes, and is an assessment of their cognitive, emotional, and behavioral inputs into the learning process as well as the assessment of objective tests. Regarding the relationship between learning outcomes assessment and learning goals achievement, some researchers have argued that learners' self-evaluation of their learning stages has a positive impact on their learning outcomes. In addition, the ability to plan, monitor, and manage learning affects the speed at which learning objectives are achieved, and by monitoring their learning behavior, learners can not only select relevant learning resources based on their individual needs, abilities, and interests, but also can be significantly and positively motivated to move closer to their goals (Le, 2019; Yan, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chang, 2022). As stated by Zhao, R. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), regarding the relationship between learning strategy design and learning outcomes assessment, recent research has confirmed the effectiveness of learning outcomes assessment across populations and settings (Zhao \u0026amp; Ling, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In summary, the study concluded that learning outcome assessment has a mediating effect in the role of learning strategies design on learning goals achievement.\u003c/p\u003e\n\u003ch3\u003eFeedback Regulation Mechanisms as a Mediator\u003c/h3\u003e\n\u003cp\u003eIt has been proved that feedback-regulated learning is related to the achievement of learning goals, and effective feedback affects the motivation to achieve learning goals (Liu, Draper, \u0026amp; Dawson, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, students with high levels of feedback regulation are able to flexibly use their skills to achieve higher learning goals, therefore, when students are to engage in feedback-regulated learning, they need to have the ability to highly regulate their learning (Cai \u0026amp; Kannan., 2020). However, studies have shown that many learners find it difficult to provide feedback regulation in the information technology environment. Specifically, when students lack feedback regulation support, coupled with a lack of learning motivation or limited learning resources, they often overestimate their learning effectiveness and stop learning before grasping the knowledge, resulting in negative feedback and hindering the achievement of learning goals(Fyfe, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, Rawad, C. \u0026amp; Maria, A. I. (2021) argued that the importance of learners for obtaining feedback regulation support in the information technology environment (Rawad \u0026amp; Maria, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLearning Outcomes Assessment and Feedback Regulation Mechanisms as Sequential Mediators\u003c/h3\u003e\n\u003cp\u003ePrevious research suggests that there is a significant positive correlation between learning outcomes assessment and feedback regulation mechanisms may be due to the fact that internal feedback has a positive impact on learners' learning (Wisniewski, Zierer, \u0026amp; Hattie J, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Combined with the adaptive control learning, learners need to constantly monitor the learning outcomes during the learning process and self-regulate based on the feedback, and then make decisions to ultimately achieve the desired learning goals. Therefore, the learning outcomes assessment and the feedback regulation mechanisms based on the difference in outcomes are important links to ensure the effectiveness of adaptive control and to achieve learning goals.\u003c/p\u003e\n\u003ch3\u003eThe Hypothesized Model\u003c/h3\u003e\n\u003cp\u003eAlthough previous studies have reviewed the interconnection of the four key constructs in fields such as education and psychology, no research has explored and demonstrated the complex relationships between them. Learners as the main body of learning how much influence the learning process, through the design of learning strategies, assessment of learning outcomes, and feedback to adjust learning, and ultimately achieve the learning goals achievement, and ultimately contributing to engagement deserve more empirical tests. To bridge this gap, SEM (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was utilized to elucidate the interconnections among those constructs. This study has the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eLearning strategies design positively predict learning goals achievement.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eLearning outcomes assessment mediates the relationship between Learning strategies design and learning goals achievement.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eFeedback regulation mechanisms mediate the relationship between Learning strategies design and learning goals achievement.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eLearning strategies design predicts learning goals achievement through the sequential mediating roles of learning outcomes assessment and feedback regulation mechanisms.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n "},{"header":"Methods","content":"\u003ch2\u003eInstruments\u003c/h2\u003e\u003cp\u003eAccording to the theoretical model and hypothesis, the core elements of this study include learning goal achievement, learning strategies design, learning outcomes assessment, and feedback regulation mechanism. Combining the literature review and established scales, we developed “The Questionnaire on Factors Influencing University Students' Learning in the Digital Age” and listed some of the measurement items in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample questionnaire on factors influencing university students' learning in the digital age\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e \u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cem\u003eFirst-level indicators\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cem\u003eSecond-level indicators\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cem\u003eSample Questions\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colname=\"c1\" morerows=\"2\" rowspan=\"3\" style=\"text-align: left;\"\u003e \u003cp\u003eLearning goals achievement (LGA) (Elliot \u0026amp; McGregor, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eTalent cultivation objectives (TCO)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eUniversity studies are designed to cultivate basic theories, specialized knowledge, and comprehensive abilities to solve complex problems in one's major field of study.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eLearning achievement objectives (LAO)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eI worry about getting poor grades in my studies, and this often motivates me to keep studying hard.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eLearning mastery objectives (LMO)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eI hope to master everything I need to learn.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" morerows=\"1\" rowspan=\"2\" style=\"text-align: left;\"\u003e \u003cp\u003eLearning strategies design (LSD) (Weinstein, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1982\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eLearning modality choice (LMC)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eDuring the learning process, I actively seek out and learn knowledge related to the subject as much as possible.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eLearning Context Construction (LCC)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eI obtain resources useful for my studies through books, websites, and software.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" morerows=\"1\" rowspan=\"2\" style=\"text-align: left;\"\u003e \u003cp\u003eLearning outcomes assessment (LOA) (Dejone, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eSelf-evaluation of behavior (SEB)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eI can organize my study plan reasonably to make it more systematic.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eExternal objective evaluation (EOE)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eDuring the learning process, I will frequently communicate with teachers.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" morerows=\"1\" rowspan=\"2\" style=\"text-align: left;\"\u003e \u003cp\u003eFeedback regulation mechanisms (FRM) (Kaplan, et al, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eFeedback regulation ability (FRA)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eThrough repeated learning, I have been able to develop my own learning model and system.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eFeedback regulation support (FRS)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eWhen I encounter limitations in learning this course, I can use online resources and technical tools to continue learning.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis questionnaire with two parts, the first part involves ten questions referring to students’ demographic characteristics (e.g., gender, age, grade, and hometown) and their use of the information technology. The second section contains 46 items of students’ perception of the above five core elements under the fusion of information technology learning. The measurement tools used the Likert five-point scale to evaluate students’ perception with the markers, from “ Strongly disagree ”, “Disagree”, “Neutral”, “Agree”, and “Strongly Agree”.\u003c/p\u003e\u003ch3\u003eParticipants\u003c/h3\u003e\u003cp\u003eWe recruited participants from 14 universities in China using a convenience sampling method. A total of 1200 university students voluntarily participated in the study. Then, we received 1190 responses and retained 977 usable responses based on the result of data filtering, leading to a response rate of 82.1%. Of these available samples, the numbers of female and male students were 550 (56.29%) and 427 (43.7%), respectively.\u003c/p\u003e\u003cp\u003eParticipants were recruited via social media and e-mail. They were informed about who the researchers were, the study’s focus on how university students adaptive control their learning in the digital age, and its estimated duration of 15 min. In addition to the relatively small number of 127 (13.00%) in the fourth grade of the university, the remaining three grades are relatively average, 304 (31.12%) in the first grade, 281 (28.76%) in the second grade, and 265 (27.12%) in the third grade. Respondents from different disciplines, with 206 (21.08%) from humanities, 205 (20.98%) from social sciences, 362 (37.05%) from engineering, 143 (14.64%) from science, and 61 (6.25%) from medicine and others.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eThe data from the questionnaires were processed with SPSS 25.0 and Smart-PLS 4.0. First, descriptive statistics and correlation analysis were carried out on the variables; Second, the structural equation model was used to determine the correlation of factors and the mediating effect.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReliability and validity of instruments\u003c/h2\u003e \u003cp\u003eThe analysis results show that the factor loading of all items was more than 0.708, the Cronbach's Alpha and the combined reliability (CR) were above 0.7 (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and the convergence validity (AVE) was more than 0.5. According to Sekaran and Bougie (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), the Cronbach\u0026rsquo;s Alpha coefficient above 0.70 is considered acceptable and above 0.80 is good. Therefore, the items in the questionnaire were regarded to be highly reliable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe reliability and validity of each indicator's questionnaire items\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirst-level ndicators\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSecond-level indicators\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCronbach\u0026rsquo;s alpha\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAVE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eItems\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLearning goals achievement (LGA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTalent cultivation objectives (TCO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning achievement objectives (LAO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning mastery objectives (LMO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLearning strategies design (LSD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning modality choice (LMC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning Context Construction (LCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLearning outcomes assessment (LOA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-evaluation of behavior (SEB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExternal objective evaluation (EOE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFeedback regulation mechanisms (FRM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeedback regulation ability (FRA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeedback regulation support (FRS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe tested the structural validity of the questionnaire by KMO and Bartlett\u0026rsquo;s testing (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The value of KMO with 0.978 (\u0026gt;\u0026thinsp;0.7) means that the structural validity of the questionnaire was well (Kaiser, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1974\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe result of KMO and Bartlett\u0026rsquo;s test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaiser\u0026ndash;Meyer\u0026ndash;Oklin measure of sampling adequacy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartlett\u0026rsquo;s test of sphericity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproximately Chi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45166.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics and correlation analysis of the survey\u003c/h2\u003e \u003cp\u003eAccording to the mean value of the variables in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, respondents have the highest recognition of the talents training objective in the dimension of learning goal achievement (M\u0026thinsp;=\u0026thinsp;4.38, SD\u0026thinsp;=\u0026thinsp;0.750), followed by the learning mastery objectives (M\u0026thinsp;=\u0026thinsp;4.21, SD\u0026thinsp;=\u0026thinsp;0.792), and the learning achievement objectives is the lowest (M\u0026thinsp;=\u0026thinsp;3.94, SD\u0026thinsp;=\u0026thinsp;0.913).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mean, standard deviation, and correlation matrix of each variable\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c12\" namest=\"c4\"\u003e \u003cp\u003ecorrelation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTalent cultivation objectives (TCO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning achievement objectives (LAO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning mastery objectives (LMO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning modality choice (LMC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning Context Construction (LCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-evaluation of behavior (SEB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal objective evaluation (EOE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeedback regulation ability (FRA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeedback regulation support (FRS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMeanwhile, respondents pay more attention to Learning modality choice (M\u0026thinsp;=\u0026thinsp;4.21, SD\u0026thinsp;=\u0026thinsp;0.761) and the Learning context construction(M\u0026thinsp;=\u0026thinsp;4.23, SD\u0026thinsp;=\u0026thinsp;0.746) in the learning strategies design. The attention to self-behavior assessment (M\u0026thinsp;=\u0026thinsp;4.01, SD\u0026thinsp;=\u0026thinsp;0.844) and external objective evaluation (M\u0026thinsp;=\u0026thinsp;4.01, SD\u0026thinsp;=\u0026thinsp;0.848) of learning outcomes assessment was general. Then, university students have a good understanding of the effectiveness of the feedback regulation mechanism in the learning process(M\u0026thinsp;=\u0026thinsp;4.20, SD\u0026thinsp;=\u0026thinsp;0.752), but also show a willingness to seek the support of feedback regulation (M\u0026thinsp;=\u0026thinsp;4.18, SD\u0026thinsp;=\u0026thinsp;0.760).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSEM Analysis\u003c/h2\u003e \u003cp\u003eIn this study, PLS-SEM operation in Smart-PLS 4.0 software was used to test the structural model according to the model collinearity test, R\u003csup\u003e2\u003c/sup\u003e test, and Q\u003csup\u003e2\u003c/sup\u003e test. The results show that the inner VIF values of all measured variables were between 1.000 and 5.979, and the outer VIF values were between 1.688 and 4.096, both of which were less than 10; the R\u003csup\u003e2\u003c/sup\u003e values of all endogenous latent variables were greater than 0.67, and the minimum was 0.671. The Q\u003csup\u003e2\u003c/sup\u003e explanatory power of the endogenous latent variables in the structural model was more than 0, and the minimum is 0.394. The SRMR of the model fitting parameter test was 0.056, reaching the standard of less than 0.08. Based on the above parameter estimation results, it can be clearly stated that the structural model of this study had good fitting, there is no collinearity problem, the predictive is high prediction correlation, and the explanatory power of the model is good.\u003c/p\u003e \u003cp\u003eThrough the Bootstrapping operation in Smart-PLS 4.0 software, the path coefficient analysis of the structural model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among them, the T statistic is greater than 1.96, and the p-value is less than 0.05, indicating that it has a significant positive impact. The results show that university students' learning strategies design has a considerable positive impact on learning goals achievement (T\u0026thinsp;=\u0026thinsp;45.898, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which supports Hypothesis H1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mediating effect test results of this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In addition to the direct impact of learning strategies design on learning goals achievement, the indirect effect coefficient of learning outcomes assessment between learning strategies design and learning goals achievement is 0.086 (T\u0026thinsp;=\u0026thinsp;2.173, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), so it is assumed that H2 is established. The indirect effect coefficient of the feedback regulation mechanism between learning strategies design and learning goals achievement is 0.063 (T\u0026thinsp;=\u0026thinsp;2.105, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), so it is assumed that H3 is established. The chain mediating effect of learning strategies design on learning goals achievement through learning outcomes assessment and feedback assessment mechanisms is 0.044 (T\u0026thinsp;=\u0026thinsp;2.229, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It can be seen the chain mediating hypothesis H4 is established, and the above indirect effect hypothesis confidence interval does not contain 0.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnstandardized and standardized path coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEffect categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eBias-corrected 95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDirect effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal indirect effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpecific indirect effects\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLearning strategies design\u0026rarr; Learning outcomes assessment\u0026rarr; Learning goals achievement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLearning strategies design\u0026rarr; Feedback regulation mechanisms\u0026rarr; Learning goals achievement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLearning strategies design\u0026rarr; Learning outcomes assessment\u0026rarr; Feedback regulation mechanisms\u0026rarr; Learning goals achievement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: * indicates a significant P-value at the 0.05 level; **indicates a significant P-value at the 0.01 level; *** indicates a significant at the 0.001 level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the data results show that in the chain mediating model of the influence of learning strategies design on learning goals achievement, the total effect value is 0.194 (T\u0026thinsp;=\u0026thinsp;3.534, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the direct effect of this path is significant, indicating that this model is a partial mediating effect model. Among the three specific indirect paths, the relative proportion of the specific indirect effects of this path mediated by learning outcomes assessment is higher than that of the other two specific indirect effects. This indicates that learning strategies design does indeed affect the achievement of learning goals through learning outcomes assessment and feedback regulation mechanisms, and the mediating effect of learning outcome assessment is significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aims to provide an in-depth analysis of the key elements of adaptive control learning. The direct positive influence of learning strategies design in relation to learning goals achievement was examined. In addition, the study also explored the mediating role of learning outcomes assessment and feedback regulation mechanisms between learning strategies design and learning goals achievement.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Key Elements of Adaptive Control Learning\u003c/h2\u003e \u003cp\u003eExpected learning goals, learning strategies design, learning outcomes assessment, and feedback regulation mechanisms are four key elements of adaptive control learning. As can be seen from the data in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, first of all, in terms of the expected learning goals achievement, data shows that Chinese undergraduate learners have the highest recognition of talent cultivation goals, while the lowest recognition of academic performance goals. This indicates that academic performance goals are more guided by test scores, and lacks dynamic evaluation of students\u0026rsquo; innovation ability and problem solving ability. In contrast, the assessment of the achievement of talent cultivation goals based on long-term tracking, and this combination of\"process oriented\u0026thinsp;+\u0026thinsp;outcome oriented\" approach is more likely to gain learners\u0026rsquo; recognition (Yang, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Secondly, in terms of learning strategies design, the respondents' scores on the choice of learning methods and the design of the learning environments are both at a high level. This indicates that technology empowerment has provided learners with a diversified choice of learning modes, which indirectly reflects the core competitiveness of Chinese Undergraduate Learners as \"digital natives\", whose learning strategy design has progressed from the selection of a single method to the systematic construction of \"tool-scenario-goal\" in the technology-permeated educational ecology (Lv \u0026amp; Shi, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thirdly, in terms of the learning outcomes assessment, the scores of its sub items are all at a relatively low level, which is consistent with the evidence from various research evidence showing that learners\u0026rsquo; learning outcomes assessment is generally at a low level. This is due to two reasons: on the one hand, the theoretical exploration has not effectively supported the implementation of the assessment of learners' learning outcomes. On the other hand, in practical exploration, the concept of learners' self-assessment is insufficient and the tools of learners' assessment lack scientificity (Liu, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, in the digital era, we should focus on the learners' real learning process, enhance their awareness of learning outcome evaluation, explore scientific and effective assessment tools with the help of information technology, and build a sustainable assessment paradigm, so that, as the main body of learning, learners pay more attention to improving their skills, thereby realizing their self-growth and self-development (Zhang \u0026amp; Zhang, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finally, the overall score of the feedback assessment mechanisms is high, indicating that in the digital era, Chinese Undergraduate Learners have gradually realized that undergraduate learning needs to change from \"want me to learn\" and \"I was taught to learn\" to \"I want to learn\" and \"I can learn\", and they are able to adjust their learning strategies in time according to feedback (Chen, et al, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, it also clearly demonstrates that university students are able to effectively receive feedback from various sources during the learning process and adjust their learning methods, plans, and attitudes accordingly(Zhang \u0026amp; Xing, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This ability is crucial for improving learning efficiency and effectiveness (Qi, et al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe Direct Impact of Learning Strategies Design on Learning Goals Achievement.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBloom's findings confirm that there is a significant correlation between the mastery of learning strategies and the achievement of learning goals (Lenchuk \u0026amp; Ahmed, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and structural equation modeling further validates the hypothesis that strategic choice of learning methods and design of the learning environments can significantly positively influence the learning goals achievement. According to adaptive control theory, learning strategies design is the behavioral choices that make during the learning process in response to the influence of the information technology environment and in accordance with their own learning styles in order to achieve their learning goals (Putri, et al, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The design of learning strategies is not only influenced by the individual behavior of the learners, but is also related to the learning environment in which they are situated. Adaptive control learning requires learners to adapt to the influence of external environment and the constant change of internal individual factors during the learning process, while the external environment is still mainly through the internal self-regulation, which in turn plays a role in the effectiveness of learning (Ma, Jiang, \u0026amp; Liu, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the key to achieving higher learning goals for learners lies in improving self-motivation and mastering learning methods, especially being able to effectively control the learning process (Ma \u0026amp; Liu, 2024). On the one hand, strategic learning represents learners' higher-order thinking, critical reflection and problem-solving skills. In the ever-changing information technology environment, learners continue to internalize their knowledge and skills by improving the process of acquiring, organizing, or transforming information in order to achieve talent development goals and gain a sense of self-efficacy, which contributes to learners' level of adaptation to the integration of information technology learning (Zhu, Xu, \u0026amp; Ma, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). On the other hand, learning emotion management is particularly important for learning. Positive emotions can stimulate both intrinsic motivation and extrinsic motivation, and in most cases have a positive impact on academic performance, while negative emotions have the opposite effect (Wu \u0026amp; Jing, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).Therefore, developing a reasonable learning planning and strengthening collaborative learning can significantly improve academic performance and promote the comprehensive improvement of learners' individual knowledge, ability, and quality (Liu, et al).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMediating Roles of Learning Outcomes Assessment and Feedback Regulation Mechanisms\u003c/h2\u003e \u003cp\u003eThe complexity of the educational and teaching environment, as well as the diversity and differences of undergraduate learners in the fusion of information technology, determine that the individual learner factor plays a non-negligible role in the learning process (Wu, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study tested the chain mediating role of learning strategies design in adaptive control learning that affects learning goals achievement through learning outcomes assessment and feedback regulation mechanisms, which helps to explain the intrinsic mechanisms by which learning strategies design affects the achievement of expected learning goals. Specifically, firstly, learning outcomes assessment plays a key mediating role between the learning strategies design and the learning goals achievement, as it is related to learners' cognitive and meta-cognitive processes and is a manifestation of learners' responsibility for constructing their learning processes. Therefore, in order to achieve learning goals successfully and efficiently, learners need to motivate themselves to use learning strategies appropriately and consciously assess their learning activities (Zhu \u0026amp; Dou, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecondly, the learners' learning strategies design affects the learning goals achievement through feedback regulation mechanisms. In other words, as a means for learners to control the learning process, the feedback regulation mechanism can give full play to their learning subjectivity and dynamically adjust their cognitive strategies in a timely manner, thus achieving the development of learners towards the expected learning goals that have been set (Sun \u0026amp; Zhou, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, multiple factors such as learning styles, learning motivation, learning content, learning emotions and learning environment need to jointly influence the learning process, which in turn affects the achievement of learners' learning goals (Xie \u0026amp; Wang, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, this study confirms that learning outcomes assessment and feedback assessment mechanisms play a chain mediating role between learning strategies design and learning goal achievement. That is to say, learners choose learning methods according to their individual characteristics, construct learning contexts, and monitor their own learning behaviors at all times, thus providing feedback regulation of the learning process to achieve learning goals (Johannes, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, the significant direct effect of learning outcomes assessment on the feedback regulation mechanisms should not be underestimated. This suggests that in adaptive control learning, in addition to the learning strategies design, the feedback regulation mechanisms are also directly affected by the learning outcomes assessment. The reason for this effect is that learners are required to compare learning outcomes with learning standards or learning objectives before making regulatory decisions, and the resulting deviations are used as reference information for adjusting learners' learning strategies (Huang \u0026amp; Ou, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eRegarding the findings and discussions, this study identifies the following research implications. In terms of theoretical construction, this study constructs a research hypothesis model based on adaptive control theory. By exploring the relationship among learning strategies design, learning outcomes assessment, feedback assessment mechanisms, and learning goals achievement ,we fully understand the positive role of adaptive control theory on university students\u0026rsquo; learning in the digital era.\u003c/p\u003e \u003cp\u003eIn terms of practice, this study reveals the operating mechanism of adaptive control learning. It emphasizes the use of university students as learning subjects in the integration of information technology and exert their learning initiative in the process of continuous integration and interaction with the information technology environment (Deng \u0026amp; Li, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). First of all, in order to better adapt to learning in the digital age, university students should take learning strategies design as the core, pay attention to the role of elements such as learning content, learning methods, and learning environments in the process of meaning construction (Hui \u0026amp; Xuan,2024). At the same time, they should use interactive communication technology to create learning situations and build a good interactive communication platform (Zhao, Brun, \u0026amp; Boyer, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Secondly, the findings demonstrate the importance of learning outcomes assessment in the digital era. The core function of learning outcomes assessment is not to measure, but also to promote students' learning, which is \"assessment for learning\". Therefore, it is necessary for students to monitor the deviation of learning outcomes from learning goals, in order to adjust their learning behaviors in the next stage to ensure that they ultimately achieve the expected learning goals and avoid being influenced by the external environment (Kismih\u0026oacute;k, et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, the feedback regulation mechanism is a key element in a learning cycle, which helps students form a constantly improving learning state, thereby stimulating their learning motivation. Cognitive theory suggests that iterative feedback learning, as an efficient learning method, can encourage individuals to continually optimize their learning strategies and achieve self-learning, narrowing the gap between their current level of performance and expected learning goals, thereby enabling students to better learn knowledge (He, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). University students' learning is a periodic feedback regulation process (Wang,2021), therefore, it is necessary to enhance students\u0026rsquo; \"feedback literacy\", accurately grasp the appropriate timing of feedback, and integrate feedback regulation into different stages such as \u0026ldquo;learning preparation, learning process and learning completion\u0026rdquo; (Han, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).By realizing the transition from external control to self-control, students can further meet their subject needs and enhance their self-efficacy (Wang \u0026amp; Chen, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Research Directions\u003c/h2\u003e \u003cp\u003eThe current study has several limitations that should be taken into account when interpreting the results. Firstly, the participants in this study were all from one region of China and the sample size was also small, which reduces the generality of the results. Future studies should include participants from different countries to obtain more consistent results from a larger sample. Secondly, the complete reliance on self-report questionnaires in the current study may also lead to potential bias, although they have been verified to have high reliability and validity. Future research could explore the use of measures such as some qualitative interviews and controlled trials to better understand the relationship between elements of adaptive control learning in the digital era through a mixed research approach. Thirdly, the data collection focused on the collection at one point in time and did not take into account the chronological processes, so it was not possible to show how university students' learning in the digital era changed over time. A longitudinal study was included in the follow-up study to reveal the dynamic role of the relationship between the elements of adaptive control learning.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study constructs an adaptive control learning model for university students in the digital era, which includes four links: expected learning goals, learning strategies design, learning outcomes assessment, and feedback regulation mechanisms. In addition, the study also found that learning in the digital era can be enhanced by fully coupling social environmental factors, strengthening the monitoring of one's own learning behavior, and improving self-regulation learning ability, so as to strengthen the learning initiative of college students, play the role of the main body of college students' learning, and promote the iterative cycle of the learning process. The results of this study emphasize the importance of learning outcomes assessment and feedback regulation mechanisms in improving learning effectiveness, and the need for continuous learning improvement through monitoring, evaluation, feedback, and adjusting of one's learning status.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researchers would like to express sincere thanks to Graduate School of Education, Dalian University of Technology, for the invaluable support and resources provided throughout the course of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiao Lu Guan wrote the main manuscript. Wei Nan Chen collected data and conducted the statistical analysis. Yanru Liu supervised the research design, contributed to methodology refinement, and critically reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp; The research is supported by the China Postdoctoral Science Foundation (Grant No.2024M750331)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and grand an exemption from requiring formal ethical approval by the Institutional Review Board(IRB) of Dalian University of Technology. The exemption was granted because the research involved anonymized analysis of existing, non-sensitive educational data, posed no psychological or physical harm to participants, and did not involve the collection of identifiable private information. This exemption is in full accordance with Article 32 of the Measures for Ethical Review of Human Life Science and Medical Research (issued by the National Health Commission of China, February 18, 2023), which stipulates the exemption conditions for such minimal-risk research. Furthermore, all research procedures were conducted in line with the ethical principles outlined in the Declaration of Helsinki (1975) and its subsequent revisions (2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent to take part in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBonk, C. J., \u0026amp; Doo, M.Y. (2020). Self-directed learning in MOOCs: exploring the relationships among motivation, self-monitoring, and self-management. Educ. Tech. Res, (68), 073-2093.\u003c/li\u003e\n\u003cli\u003eCai, Y., \u0026amp; Kunnan, A. J. (2023). Mapping the fluctuating effect of strategy use ability on English reading performance for nursing students: a multi-layered moderation analysis approach. Language Testing, 37(2), 280-304. \u003c/li\u003e\n\u003cli\u003eChang, Y., Li, J., \u0026amp; Huang, A. D. (2022). A Personalized Learning Service Compatible with Moodle E-Learning Management System. Applied sciences, 3.\u003c/li\u003e\n\u003cli\u003eChen, K. Q., Han, X. L., \u0026amp; Zheng, Z. F. (2022). Scene Construction and Application Model Analysis of Intelligent Education under Human-Machine Collaboration: A Review of Domestic and International Research on Human-Machine Collaboration Education over the Past Decade. Journal of Distance Education, 40(02), 3-14.\u003c/li\u003e\n\u003cli\u003eDeng, M. J., \u0026amp; Li, Y. (2023) . Towards Technological Embodiment: Re-examining Learner Agency in the Information Technology Age. Research on Electrified Education, 44(08), 26-32.\u003c/li\u003e\n\u003cli\u003eDejone, K. (1991). Inventory Regulation Strategies. Educational leadership, (45), 108-116.\u003c/li\u003e\n\u003cli\u003eElliot, A. J., \u0026amp; McGregor, H. A. (2001). A 2*2 achievement goal frame-work. Journal of Personality and Social Psychology, (80), 501-519.\u003c/li\u003e\n\u003cli\u003eFyfe, E. R. (2020). This is easy, you can do it! Feedback during mathematics problem solving is more beneficial when students expect to succeed. Instructional Science. Brown SA, 48(1), 23-44.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-P\u0026eacute;rez, D., Fraile, J., \u0026amp; Panadero, E. (2021). Learning strategies and self-regulation in context: How higher education students approach different courses, assessments, and challenges. Eur J Psychol. Educ, (36), 533\u0026ndash;550.\u003c/li\u003e\n\u003cli\u003eGuterman, O., \u0026amp; Neuman, A. (2021). Not all paths lead to success: Learning strategies and achievement among undergraduate students. Furth. High. Educ, (46), 115-127.\u003c/li\u003e\n\u003cli\u003eH\u0026auml;ndel, M., Harder, B., \u0026amp; Dresel, M. (2020). Enhanced monitoring accuracy and test performance: Incremental effects of judgment training over and above repeated testing. Learning and Instruction, (65), 101245-101249.\u003c/li\u003e\n\u003cli\u003eHan, Y., \u0026amp; Yueting, X. (2020). The development of student feedback literacy:the influences of teacher feedback on peer feedback. Assessment \u0026amp; Evaluation in Higher Education, 45(5), 680-696.\u003c/li\u003e\n\u003cli\u003eHan, Z. Z., Chen, P. N. \u0026amp; Chen, S. Z. (2011). Adaptive Control. Basic Theory of Adaptive Control (pp.34-37). Beijing: Tsinghua University Press.\u003c/li\u003e\n\u003cli\u003eHe, K. K. (2017). A deep understanding of the essence of feedback and the planning and design of effective feedback\u0026mdash;the second highlight that deeply inspired us from the American \u0026lsquo;Handbook of Educational Communication and Technology Research (4th Edition)\u0026rsquo;. China Educational Technology, (5), 1, 7-14.\u003c/li\u003e\n\u003cli\u003eHsieh, H. (2022). Effect of Deep Learning Approach on Career Self-Efficacy: Using Off-Campus Internships of Hospitality university Students as an Example. Sustainability, (14), 7594. \u003c/li\u003e\n\u003cli\u003eHuang, Y., \u0026amp; Ou, J. (2021). The Relationship between Self-Directed Learning and Problem-Solving Ability: The Mediating Role of Academic Self-Efficacy and Self-Regulated Learning among Nursing Students.Int.Environ. Res. Public Health, 18.\u003c/li\u003e\n\u003cli\u003eHui, W. J., \u0026amp; Xuan, X. H. (2024). Transcending Breakthroughs: Exploring the Relationship Between Education and New Productivity. Educational research, 45(12), 71-83.\u003c/li\u003e\n\u003cli\u003eJohannes, C. (2022). The impact of feedback mode on learning gain and self-efficacy: A quasi-experimental study. Learning in higher education, 10.\u003c/li\u003e\n\u003cli\u003eKadioglu-Akbulut, C.; \u0026amp; Uzuntiryaki-Kondakci, E. (2021). Implementation of self-regulatory instruction to promote students\u0026rsquo; achievement and learning strategies in the high school chemistry classroom. Chem. Educ. Res. Pract, 22, 12-29.\u003c/li\u003e\n\u003cli\u003eKaiser, H. F. (1974). \u0026lsquo;An index of factorial simplicity\u0026rsquo;. \u003cem\u003ePsychometrika,\u003c/em\u003e 39(1), 31-36.\u003c/li\u003e\n\u003cli\u003eKaplan, J., Montalembert, M., Laurent, P., \u0026amp; Fenouillet, F. (2017). ERICA\u0026ndash;an instrument to measure individual and collective regulation of learning. European Review of Applied Psychology, 67(2), 79-89.\u003c/li\u003e\n\u003cli\u003eKautish, P., Hameed, S., Kour, P., Walia, S. (2021). Career beliefs, self-efficacy and VUCA skills: A study among generation Z female students of tourism and hospitality. Hosp. Leis. Sport Tour. Educ, 30, 100340.\u003c/li\u003e\n\u003cli\u003eKismih\u0026oacute;k, G., Zhao, C., \u0026amp; Schippers, M. (2020). Translating the concept of goal setting into practice: What \u0026apos;else\u0026apos; does it require than a goal setting tool? In H. C. Lane, S. Zvacek, \u0026amp; J. Uhomoibhi (Eds.), 388-395.\u003c/li\u003e\n\u003cli\u003eLee, D.,Watson, S. L., \u0026amp; Watson, W. R. (2019). Systematic literature review on self-regulated learning in massive open online courses. Australasian Journal of Educational Technology, 35(1).\u003c/li\u003e\n\u003cli\u003eLee, M., \u0026amp; Na, H., Kim, B. (2021). Mediating effects of achievement emotions between peer support and learning satisfaction in graduate nursing students. Nurse Educ. Pract, 52. \u003c/li\u003e\n\u003cli\u003eLenchuk, I., \u0026amp; Ahmed. (2021) Tapping into Bloom Taxonomy\u0026apos;s Higher-Order Cognitive Processes: The Case for Multiple Choice Questions as a Valid Assessment Tool in the ESP Classroom. Arab World English Journal, 160-171.\u003c/li\u003e\n\u003cli\u003eLiao, X., Zhang, X., \u0026amp; Wang, Y. (2024). Design and implementation of an AI-enabled visual report tool as formative assessment to promote learning achievement and self-regulated learning:an experimental study. British journal of educational technology, 55, 1253-1276.\u003c/li\u003e\n\u003cli\u003eLi, T. Z. (2024). Interaction and Relationship Reshaping Between University Teachers and Students in the Digital Education Era. China Educational Technology, (09), 110-115.\u003c/li\u003e\n\u003cli\u003eLi, J., Wong, S.C., Yang, X., \u0026amp; Bell, A. (2020) Using Feedback to Promote Student Participation in Online Learning Programs: Evidence from a Quasi-Experimental Study. Educ. Technol. Res. Dev., 68, 485\u0026ndash;510. \u003c/li\u003e\n\u003cli\u003eLiu, H. Y., Zhu, Q., \u0026amp; Nie, Q. (2025). From \u0026lsquo;dependence\u0026rsquo; to \u0026lsquo;independence\u0026rsquo;: The impact of in-depth counselling on the development of gifted college students with difficulties. Research on Higher Education in China, (02), 19, 26-35.\u003c/li\u003e\n\u003cli\u003eLiu, S. T. (2017). Reflections on Promoting Reforms in Student Learning Outcomes Assessment in the \u0026lsquo;Double First-Class\u0026rsquo; Initiative. University Education Science, (6), 38-41.\u003c/li\u003e\n\u003cli\u003eLiu, Y., \u0026amp; Draper, J., \u0026amp; Dawson, M. (2022). The Relationship between Work Experience and Career Expectations: Career Decision-Making Self-efficacy as Mediator. Hosp. Tour. Educ, 4.\u003c/li\u003e\n\u003cli\u003eLv, G. Z., \u0026amp; Shi, M. (2025). Generative AI Empowers Digital Transformation in Higher Education: An Analysis Based on Stanford University. Research on Higher Engineering Education, (02), 176, 181-188.\u003c/li\u003e\n\u003cli\u003eMa, C. J., \u0026amp; Liu, P. J. (2025). Withdrawal from the Classroom: The Mechanisms Behind University Truancy and Rational Reflection. Chongqing Higher Education Research, 13(03), 117-127.\u003c/li\u003e\n\u003cli\u003eMa, J., Jiang, M., \u0026amp; Liu, X. X. (2023). How does an academic atmosphere promote graduate students\u0026apos; commitment to research? The role of cognitive demands and self-control. Tsinghua University Educational Research, 44(6), 93-102. \u003c/li\u003e\n\u003cli\u003eMatcha, W. (2019). Analytics of Learning Strategies: Associations with Academic Performance and Feedback. In The 9th International Learning Analytics and Knowledge Conference (LAK19), USA: New York, 2019.\u003c/li\u003e\n\u003cli\u003eMeina, Z., \u0026amp; Min, Y. D. (2022). The relationship among motivation, self‑monitoring, self‑management, and learning strategies of MOOC learners.Journal of Computing in Higher Education, (34), 321\u0026ndash;342.\u003c/li\u003e\n\u003cli\u003ePutri, K.Y.S., Bin, A. Z., \u0026amp; Nugrahaeni, T. (2021). Learning Management Strategy of Communication Studies through Blended Learning in Higher Education. International Journal of Interactive Mobile Technologies, 14(16), 117-132.\u003c/li\u003e\n\u003cli\u003eQi, J., Xu, Y. R., \u0026amp; Liu, J. A. (2024). The Impact of Generative Artificial Intelligence Tools on Critical Thinking and Independent Learning Abilities Among University Students. Research on Electrified Education, 45(12), 67-74.\u003c/li\u003e\n\u003cli\u003eRawad, C., \u0026amp; Maria, A. I. (2021). The moderating effect of social capital on co-regulated learning for MOOC achievement, Education and Information Technologies, (26), 899-919.\u003c/li\u003e\n\u003cli\u003eShi, D., Irwin, D., \u0026amp; Du, P. (2022). Languaging dynamics in interactive lecturing: Exploring an embodied approach to deep learning in L2 higher education contexts. Classr. Discourse, 9.\u003c/li\u003e\n\u003cli\u003eSekaran, U. \u0026amp; Bougie, R. (2003). Research methods for business. A skill building approach, Chichester:Wiley.\u003c/li\u003e\n\u003cli\u003eSun, L. H, \u0026amp; Zhou, L. (2025). Generative Artificial Intelligence Literacy: Conceptual Evolution, Framework Construction, and Pathways for Improvement. Modern distance education, (01), 11-21.\u003c/li\u003e\n\u003cli\u003eXie, X. Y., \u0026amp; Wang, J. (2025). The Experience and Implications of the Transformation of \u0026lsquo;the Learning Paradigm\u0026rsquo; in American Community Colleges. Jiangsu Higher Education, (03), 119-124.\u003c/li\u003e\n\u003cli\u003eWang, C., \u0026amp; Hsieh, H. (2022). Effect of Deep Learning Approach on Career Self-Efficacy: Using Off-Campus Internships of Hospitality College Students as an Example. Sustainability, 14, 7594.\u003c/li\u003e\n\u003cli\u003eWang, C. J. (2021). Should I Stay or Should I Go? Linking Career Decision-Making Self-Efficacy to Intention to Stay in the Hospitality Industry Based on Internship Experience. Sustainability, 13, 10571.\u003c/li\u003e\n\u003cli\u003eWang, M. D., \u0026amp; Chen, K. D. (2022). Student Feedback Literacy from a Learning Science Perspective: Structural Model and Operational Mechanisms. \u003cem\u003eChina Educational \u003c/em\u003e\u003cem\u003eTechnology, \u003c/em\u003e(7), 104-114.\u003c/li\u003e\n\u003cli\u003eWang, Q. L. (2014). Research on Adaptive Career Development Management System for Core Employees in Enterprises. Theoretical basis (pp.42-63). Beijing: North China Electric Power University.\u003c/li\u003e\n\u003cli\u003eWang, Y. \u0026amp; Xie, Y. G. (2017). Theoretical Foundations for Research on the Elements and Structure of Precise Learner Models in Smart Learning Environments. Open Education Research, (6), 104-111.\u003c/li\u003e\n\u003cli\u003eWeinstein. (1982). LASSI User`s Manual. Learning and Study Strategies Inventory. http://www.hhpublishing.com/_onlinecourses/study_strategies/BSL/admin/usersmanual. html.\u003c/li\u003e\n\u003cli\u003eWisniewski, B., Zierer, K., \u0026amp; Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, (10), 3087.\u003c/li\u003e\n\u003cli\u003eWu, C. C., \u0026amp; Jing, B. (2021). Student\u0026rsquo;s Learning Strategies and Academic Emotions: Their Influence on Learning Satisfaction During the COVID-19 Pandemic. Frontiers inPsychology, 7.\u003c/li\u003e\n\u003cli\u003eWu, D. G. (2020) A Review and Reflection on the Evolution of Educational Technology: A Perspective from Online Teaching in Higher Education Institutions During the COVID-19 Pandemic. Research on Higher Education in China, (4).\u003c/li\u003e\n\u003cli\u003eYan, C. H. (2024). Promoting high-quality development of higher education in western China through digitalisation. Research on Higher Education in China, (1), 11, 14-22.\u003c/li\u003e\n\u003cli\u003eYan, J. Z. \u0026amp; Zhang, J. J. (2023). Learning the interpretation and construction path of the metaverse. Journal of Distance Education, 41(02), 36-47.\u003c/li\u003e\n\u003cli\u003eYan, Z. (2020). Self-assessment in the process of self-regulated learning and its relationship with academic achievement. Assess. Eval. High. Educ, (45), 224-238.\u003c/li\u003e\n\u003cli\u003eYang, D. (2024). Disembedding and Embedding: The Current Dilemma and Advancement Strategies of Smart Engineering Education-Based on the Theory of Technology Embedding. Higher Education Administration, 18(01), 33, 46-69. \u003c/li\u003e\n\u003cli\u003eYang, Y. D. (2020). Establishing a Normalised Model of Blended Online and Offline Teaching in Higher Vocational Colleges in the Context of Deep Learning. Jiangsu Higher Education, (06), 77-82.\u003c/li\u003e\n\u003cli\u003eYu, S. K., Wang, Q. L., \u0026amp; Zhang, O. (2012). Research on Employee Development Pathways and Their Adaptability. Research on Technology, Economics and Management, (08), 52-56.\u003c/li\u003e\n\u003cli\u003eZhao, R., \u0026amp; Ling, T. (2022). Latent profile analysis of university students\u0026rsquo; self-management and self-monitoring in the links among motivation, engagement, and wellbeing. Front. Psychol, 13.\u003c/li\u003e\n\u003cli\u003eZhao, Z., Brun, A., \u0026amp; Boyer, A. (2020) New Measures for Offline Evaluation of Learning Path Recommenders. Addressing Global Challenges and Quality Education. 15th European Conference on Technology Enhanced Learning, 259-273.\u003c/li\u003e\n\u003cli\u003eZhang, X. X., \u0026amp; Xing, L. (2023). Building a Profile of University Students Aimed at High-Quality Learning: A Case Study of Shanghai Jiao Tong University. Research on Educational Development, 43(17), 76-84. \u003c/li\u003e\n\u003cli\u003eZhang, Y. Q., \u0026amp; Zhang, Y. L. (2023). The Historical Evolution and Basic Characteristics of Talent Cultivation Goals in China\u0026apos;s Undergraduate Education: An Analysis Based on Policy Documents. Jiangsu Higher Education, (10), 13-21.\u003c/li\u003e\n\u003cli\u003eZhong, B. L., \u0026amp; Wang, X. F. (2019). The Popularisation of Higher Education in China: Opportunities, Challenges and Prospects. Research on Higher Education in China, (8), 7-13.\u003c/li\u003e\n\u003cli\u003eZhu, J. H., Xu, L. Y., \u0026amp; Ma, J. Y. (2025). How Generative Artificial Intelligence Empowers Student Learning: An Empirical Study Based on Self-Regulated Learning Behaviour Among University Students. Research on Higher Engineering Education, (02), 66-72.\u003c/li\u003e\n\u003cli\u003eZhu, M. N., Dou, M. Y. (2022). The relationship among motivation, self‑monitoring, self‑management, and learning strategies of MOOC learners. Journal of Computing in Higher Education, (34), 321-342.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adaptive control theory, University students learning, Learning strategies design, Learning outcomes assessment, Feedback regulation mechanisms","lastPublishedDoi":"10.21203/rs.3.rs-8402334/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8402334/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the digital era, the development of information technology has brought profound changes to the self-regulated learning mode of university students. However, how students engage in adaptive control learning under the integration of information technology has yet to be empirically investigated. This study introduces adaptive control theory into the field of university students\u0026rsquo; learning, defines the concept of students\u0026rsquo; adaptive control learning, and proposes four key elements of adaptive control learning: expected learning goals, learning strategies design, learning outcomes assessment, and feedback regulation mechanisms. The confirmatory factor analysis (CFA) and structural equation model (SEM) were conducted to assess the current status of students\u0026rsquo; adaptive control learning in one university of China. The results showed that (1) The overall level of Chinese undergraduate learners' learning outcomes assessment is generally low; (2) Learning strategies design could directly and significantly positively predict the achievement of expected learning goals; (3) Learning strategies design indirectly affects the achievement of expected learning goals through the sequential mediating role of learning outcomes assessment and feedback regulation mechanisms. The research reveals the operation mechanism of adaptive control learning, and tests the positive effects of adaptive control theory on learning in the digital era, providing insights into the change of learning pattern in the digital era.\u003c/p\u003e","manuscriptTitle":"Adaptive Learning Control Mechanisms of University Students in the Digital Age","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 16:56:20","doi":"10.21203/rs.3.rs-8402334/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dc304c40-8117-4f3c-bc76-465062211b08","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T10:23:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 16:56:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8402334","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8402334","identity":"rs-8402334","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}