Background: Based on the control-value theory (CVT), learning strategies and academic emotions are closely related to learning achievement, and have been considered as important factors influencing student's learning satisfaction and learning performance in the online learning context. However, only a few studies have focused on the influence of learning strategies on academic emotions and the interaction of learning strategies with behavioral engagement and social interaction on learning satisfaction.Methods: The participants were 363 pre-service teachers in China, and we used structural equation modeling (SEM) to analyze the mediating and moderating effects of the data.Results: The main findings of the current study showed that learning strategies influence students' online learning satisfaction through academic emotions. The interaction between learning strategies and behavioral engagement was also an important factor influencing online learning satisfaction.Conclusions: We explored the internal mechanism and boundary conditions of how learning strategies influenced learning satisfaction to provide intellectual guarantee and theoretical support for the online teaching design and online learning platform. This study provides theoretical contributions to the CVT and practical value for massive open online courses (MOOCs), flipped classrooms and blended learning in the future.

Background: Constructivism theory has suggested that constructing students’ own meaning is essential to successful learning. The erroneous example can easily trigger learners’ confusion and metacognition, which may “force” students to process the learning material and construct meaning deeply. However, some learners exhibit a low level of elaboration activity and spend little time on each example. Providing instructional scaffolding and elaboration training may be an efficient method for addressing this issue. The current study conducted a randomized controlled trial to examine the effectiveness of erroneous example elaboration training on learning outcomes and the mediating effects of metacognitive load for Chinese students in medical statistics during the COVID-19 pandemic. Methods: Ninety-one third-year undergraduate medical students were randomly assigned to the training group (n = 47) and the control group (n = 44). Prerequisite course performance and learning motivation were collected as covariates. The mid-term exam and final exam were viewed as posttest and delayed-test to make sure the robustness of the training effect. The metacognitive load was measured as a mediating variable to explain the relationship between the training and academic performance. Results: The training significantly improved both posttest and delayed-test performance compared with no training (F_posttest = 26.65, p < 0.001, Partial η² = 0.23; F_{delayed test} = 38.03, p < 0.001, Partial η² = 0.30). The variation trend in metacognitive load in the two groups was significantly different (F = 2.24, p < 0.05, partial η² = 0.20), but metacognitive load could not explain the positive association between the treatment and academic performance (β = − 0.06, se = 0.24, 95% CI − 0.57 to 0.43). Conclusions: Erroneous example learning and metacognitive demonstrations are effective for academic performance in the domain of medical statistics, but their underlying mechanism merits further study.

Nowadays, most courses in massive open online course (MOOC) platforms are xMOOCs, which are based on the traditional instruction-driven principle. Course lecture is still the key component of the course. Thus, analyzing lectures of the instructors of xMOOCs would be helpful to evaluate the course quality and provide feedback to instructors and researchers. The current study aimed to portray the lecture styles of instructors in MOOCs from the perspective of natural language processing. Specifically, 129 course transcripts were downloaded from two major MOOC platforms. Two semantic analysis tools (linguistic inquiry and word count and Coh-Metrix) were used to extract semantic features including self-reference, tone, effect, cognitive words, cohesion, complex words, and sentence length. On the basis of the comments of students, course video review, and the results of cluster analysis, we found four different lecture styles: “perfect,” “communicative,” “balanced,” and “serious.” Significant differences were found between the different lecture styles within different disciplines for notes taking, discussion posts, and overall course satisfaction. Future studies could use fine-grained log data to verify the results of our study and explore how to use the results of natural language processing to improve the lecture of instructors in both MOOCs and traditional classes.

Mental health problems are prevalent among Chinese college students, with gender differences in symptom presentation. Network analysis provides a novel approach to investigate the complex interactions between symptoms and identify gender differences in the structure and dynamics of mental health problems. Psychological assessment data were collected from 18,629 freshmen at a university in Chengdu, China, between 2020 and 2023. Gaussian Graphical Models and centrality indices were used to estimate and visualize symptom networks. Network comparison tests, accuracy and stability tests, and community detection were performed using R packages to examine gender differences. Mental health symptom networks differed across psychological distress levels. In the severe distress group, male and female students’ networks exhibited significant differences in 10 edges and overall strength. Inferiority, depression, and anxiety emerged as central symptoms, and revealed by community detection. The single-university setting may limit the generalizability of the findings to other populations or cultural contexts. The cross-sectional design precludes causal inferences about symptom relationships. Network analysis offers valuable insights into the complex interactions of mental health symptoms among Chinese college students, highlighting gender differences in the severe distress group. The findings reveal central symptoms and distinct symptom clusters, underscoring the importance of developing targeted, personalized interventions that address these specific patterns of psychological distress. By illuminating the intricate structure of mental health networks, this research provides a foundation for more effective, tailored approaches to support student well-being in higher education settings.

Background: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children’s Depression Inventory (CDI) is widely utilized in China, it lacks a localized revision or simplified version. With its 27 items requiring professional administration, the original CDI proves to be a time-consuming method for predicting children and adolescents with high depression risk. Hence, this study aimed to develop a shortened version of the CDI to predict high depression risk, thereby enhancing the efficiency of prediction and intervention. Methods: Initially, backward elimination is conducted to identify various version of the short-form scales (e.g., three-item and five-item versions). Subsequently, the performance of five machine learning (ML) algorithms on these versions is evaluated using the area under the ROC curve (AUC) to determine the best algorithm. The chosen algorithm is then utilized to model the short-form scales, facilitating the identification of the optimal short-form scale based on predefined evaluation metrics. Following this, evaluation metrics are computed for all potential decision thresholds of the optimal short-form scale, and the threshold value is determined. Finally, the reliability and validity of the optimal short-form scale are assessed using a new sample. Results: The study identified a five-item short-form CDI with a decision threshold of 4 as the most appropriate scale considering all assessment indicators. The scale had 81.48% fewer items than the original version, indicating good predictive performance (AUC = 0.81, Accuracy = 0.83, Recall = 0.76, Precision = 0.71). Based on the test of 315 middle school students, the results showed that the five-item CDI had good measurement indexes (Cronbach’s alpha = 0.72, criterion-related validity = 0.77). Conclusions: This five-item short-form CDI is the first shortened and revised version of the CDI in China based on large local data samples.