Mingzhang Zuo

Time management is a critical competency for first-year college students, yet many struggle with limited self-regulation, and existing interventions are often short-term and weakly grounded in theory. This study explored how a design-based research (DBR) approach integrating experiential learning and digital tools could strengthen students’ time management skills. From 2021 to 2023, 238 first-year students at a research university in central China participated in a three-month hybrid Freshman Orientation Seminar, with data collected from daily submissions via a WeChat mini-program. Over three iterative DBR cycles, the intervention combined experiential learning theory with authentic time management practice, guided by quantitative and qualitative evidence to refine the pedagogical model. The process yielded six design principles and a supporting digital tool. In the final iteration, students demonstrated substantial gains, including improved planning, greater task completion, more accurate time allocation, and higher satisfaction with time use. These findings suggest that sustained, theory-guided experiential learning, when supported by digital tools, can significantly enhance time management competencies. The study contributes practical strategies for embedding self-regulated learning into higher education through technology-enhanced experiential approaches.

This study examined how the time management behavior of first-year college students predicted their academic performance. Data on 44 objective indicators of daily time management behaviors were collected from 110 first-year students via a WeChat Mini Program, through one month of consecutive daily tracking. To identify stable predictors, Least Absolute Shrinkage and Selection Operator (LASSO) regression with 5000 bootstrap resamples was conducted, and variables with high selection frequency were subsequently entered Elastic Net regression to examine explanatory relationships. Six key behavioral indicators were found to predict overall academic performance. Subject-specific models revealed varying associations: time management behaviors appeared more influential in subjects such as Physical Education and English, while their role was less evident in Mathematics. The number and nature of retained predictors also differed across disciplines.

Introduction: Achievement emotions have a profound impact on students' academic achievements and learning strategies, especially in online learning context where students may face great challenges in maintaining positive emotions due to physical separation and social isolation. While most studies focus on achievement emotions as predictors of academic performance, fewer have examined them as outcome variables. However, achievement emotions themselves can be seen as an important learning outcome, as experiencing positive achievement emotions is also a valuable learning goal.Methods: This study explored key factors influencing achievement emotions among Chinese elementary and middle school students in online mathematics learning, using control-value theory as a framework. Data were collected using stratified sampling, and backward regressions were used to analyze data from 2,940 students. In addition, mediation analyses were conducted to examine whether control and value appraisals mediated the relationship between technology efficacy and achievement emotions.Results: Results indicated that control and value appraisals were the strongest predictors of achievement emotions, positively predicting positive emotions and negatively predicting negative emotions. Notably, negative effort belief positively predicted all three negative emotions substantially, surpassing the predictive effects of control appraisal. This study also found that technology efficacy positively predicted all positive emotions and negatively predicted anxiety. Regarding demographic factors, grade level and school tier significantly influenced some achievement emotions, but gender was not a significant predictor. In addition, mediation analyses showed that control and value appraisals partially mediated the effects of technology efficacy on both positive and negative achievement emotions.Conclusion: This study emphasizes the need to optimize online learning by enhancing students' control and value appraisals, addressing negative effort beliefs. Special attention should be given to students in disadvantaged schools and boys in all schools during online mathematics learning.

The mental health of college students, particularly first-year students, deserves significant attention. As they transition from high school to college, they experience substantial changes not only in their learning behaviors but also in their daily behaviors, which may pose mental health challenges. However, existing studies exploring the factors influencing college students’ mental health still have limitations in the selection of predictor variables and behavior assessment. Building on this, our study adopted a cross-sectional design involving 110 first-year college students in China (M = 17.94 years; 47 males, 63 females) and analyzed the impact of daily behaviors on mental health. We collected daily behavioral data through a designed mini program, and assessed mental health using the GHQ-28. LASSO regression was applied to identify key behavioral predictors, and semi-structured interviews were conducted to further explore the impact of these factors. The results show that sleep quality, planned and implemented physical exercise, average self-evaluation scores, average daily number of planned tasks, and the completion rate of planned tasks have a positive impact on mental health. These findings contribute to enhancing the theoretical understanding of how daily behaviors shape college students’ mental health and offer practical guidance for universities to carry out mental health education.

How adolescents’ after-school behaviors, self-management, and parental involvement are associated with their academic success and personal development has drawn growing attention. This study systematically examined how these three factors predict academic achievement. Data were collected from 353 seventh-grade students through a self-designed daily behavior log sheet and self-management app, and multiple linear regression analysis was conducted to identify key predictors. The results indicated that academic achievement was positively related to sleep quality, homework duration, self-assessment, and parental involvement, while it was negatively associated with sleep duration, exercise duration, and the number of daily planned tasks. Despite these insights, the study has limitations, including reliance on self-reported data, which may introduce bias, and the use of a cross-sectional design, which precludes determining the temporal sequence of variables and inferring causal relationships. These findings provide valuable insights for educators, parents, and students; they also emphasize the importance of organizing students’ after-school activities effectively, enhancing their self-management competencies, and encouraging active parental involvement in fostering students’ academic success and overall development.

As online learning platforms become prevalent, online learning has been an important way for college students. Online learning engagement, as an evaluation of online learning quality, is crucial for enhancing learning quality and promoting higher education by investigating college students’ engagement and its influencing factors in the online learning environment. This paper aims to identify key factors affecting college students’ online learning behavioral engagement. Based on a literature review, the Delphi expert consultation method was used to build an assessment framework covering five dimensions (participation, concentration, interaction, challenge, and self-monitoring) with sixteen specific indicators. The Analytic Hierarchy Process (AHP) determined the weights of these factors. Then, data from 63 students using the “Ketangpai” online learning platform were collected and analyzed to explore the correlation and predictive relationships between online learning behavior indicators and academic achievements. The results showed a strong correlation between the frequency of accessing online learning resources and long-term online learning and academic performance, and a prediction model was established. The framework offers theoretical and methodological insights for designing online learning activities and evaluating learning quality. It supports intervening in and assessing college students’ online learning processes and improving learning quality. Also, exploring the relationship helps educators formulate personalized online teaching strategies, improving online education effectiveness and students’ learning experiences.

Cognitive engagement is a crucial factor that shapes successful learning outcomes, but our understanding of the factors that influence such engagement in the smart classroom context remains limited. This study aims to narrow this research gap by exploring the relationships among college students’ perceptions of the smart learning environment, perceived usefulness of mobile technology, achievement emotions, and cognitive engagement. A total of 1293 college students completed an online questionnaire survey, and 1076 valid responses were received. Structural equation modeling was used to analyze the interrelationships among these factors. The results revealed that students’ perceptions of the smart classroom environment and perceived usefulness of mobile technology as well as two achievement emotions (pride and anxiety) significantly impact cognitive engagement. Both pride and anxiety act as mediators in the relationships among perceptions of smart classroom environments, the perceived usefulness of mobile technology, and cognitive engagement, in which context the mediating effect of pride is stronger than that of anxiety. These findings have practical implications for instructors, who should focus on implementing strategies that promote positive achievement emotions when students use mobile technology in smart classrooms. Additionally, these findings can inform the design and construction of smart classroom environments. Moreover, our study has limitations due to reliance on online data collection and self-reported data, which may introduce biases and measurement errors. Future research should incorporate multimodal data and advanced technologies for a comprehensive assessment to better understand students’ engagement in smart learning environments, while also considering individual factors and the educational context to enhance the effectiveness of mobile technology in supporting students’ emotions and achievement.

The implementation of home-based learning for secondary school students faces challenges such as weakened supervision, a lack of prior online learning experience, and low self-regulated learning (SRL) skills. To address this, we propose an implementation mechanism to help teachers develop students’ SRL skills in home-based learning environments. After three iterations of design, implementation, and evaluation, following the educational-design research approach, the proposed implementation mechanism was empirically validated and refined. The results confirmed the feasibility and effectiveness of the proposed framework, one which integrates strategies of goal setting and planning, self-monitoring, and self-evaluation. We also demonstrated that the designed implementation mechanism, which comprises the four components of sequence, resource, activity, and incentive, helped students master SRL skills and improve nonacademic performance. Lastly, we identified seven design principles that can guide educators in the adoption of similar practices to develop students’ SRL skills, particularly for future flexible and smart learning scenarios. These principles emphasize the motivational, sequential, social, and instrumental aspects of instructional design, and call for parental involvement and a flexible mindset during implementation. The paper ends with a discussion of several limitations regarding sample representativeness and data diversity that should be noted when interpreting the study results.

Scaffolding is widely used in online learning; however, it is unclear whether scaffolding can effectively enhance students’ online learning performance. To evaluate its effectiveness, we conducted a meta-analysis that included 83 effects sizes from 32 articles published between 2011 and 2021. The findings demonstrated that scaffolding had a significant role in improving students’ online learning performance with an overall effect size of 0.53. In addition, we analyzed the effects of five moderator variables to gain insights into how scaffolding affects students’ online learning performance under different conditions. The discipline, learning outcome type, and scaffolding type were identified as significant moderators. It is expected that the research results will guide teachers to design online scaffolding strategies and improve students’ online learning performance.

A learning environment’s quality has crucial influence on a student’s engagement. In this study, we utilized a structural equation modeling approach to explore the structural relationships between students’ perceptions of an online learning environment and their online learning engagement during China’s COVID-19 school closure period by focusing on an online learning environment and the specific features that facilitate student engagement. The online learning environment was conceptualized as a multidimensional structure consisting of four elements: pedagogy, social interaction, technology, and the consideration of home learning conditions. Student engagement was conceptualized as a multifaceted construct comprising behavioral, emotional, and cognitive engagement. The results showed that teaching presence significantly predicted deep behavioral engagement (β = 0.246), emotional engagement (β = 0.110), and cognitive engagement (β = 0.180). Social presence significantly positively predicted cognitive engagement (β = 0.298) and emotional engagement (β = 0.480), whereas its effect on behavioral engagement was not significant. The perceived ease of technology use significantly predicted only emotional engagement (β = 0.324), and the family learning presence significantly predicted only behavioral engagement (β = 0.108). The results also indicated that emotional and cognitive engagement had indirect effects on the predictive power of the online learning environment for behavioral engagement. These findings provide valuable guidelines and effective strategies for teachers and parents to design suitable online learning environments to enhance K–12 student engagement.