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.

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.

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.

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.

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.