Graduate students often experience difficulties in learning statistics, particularly those who have limited mathematical backgrounds. In recent years, Learning Management Systems (LMS) and Problem-Based Learning (PBL) have been widely adopted to support instruction, yet little research has explored how these tools relate to learning outcomes using mixed methods design. Limited studies have employed machine learning methods such as clustering analysis in Learning Analytics (LA) to explore different behavior of clusters based on students log data. This study followed an explanatory sequential mixed methods design to examine student engagement patterns on Canvas and learning outcomes of students in a graduate-level statistics course. LMS log data and surveys were collected from 31 students, followed by interviews with 19 participants. K-means clustering revealed two groups: a high-performing group with lower LMS engagement and a low-performing group with higher LMS engagement. Six themes emerged from a thematic analysis of interview transcripts: behavioral differences in engagement, the role of assessment, emotional struggle, self-efficacy, knowledge or skill gain, and structured instructional support. Results indicated that low-performing students engaged more frequently and benefited from structured guidance and repeated exposure. High-performing students showed more proactive and consistent engagement habits. These findings highlight the importance of intentional course design that combines PBL with LMS features to support diverse learners.

Addiction is a complex and pervasive condition which affects physical, social, and mental health. Research consistently shows that social support and social networks are key to the addiction recovery process (defined as a process of change through which individuals improve their health and wellness, live a self-directed life, and strive to reach their full potential) and recovery communities available outside of or in conjunction to formal treatment are effective in providing such support. This study investigated social networks and psychological distress among members of The Phoenix, a sober active community that incorporates group-based exercise (e.g., CrossFit) into the recovery process. Using Social Network Analysis (SNA), we analyzed relationships within The Phoenix CrossFit programs in Denver, Colorado (N = 35) and Wichita, Kansas (N = 42). Linear Network Autocorrelation Models (LNAMs) assessed whether social network positions and connections related to psychological distress among members, and Exponential Random Graph Models (ERGMs) explored factors that explained the presence of supportive relationships between Phoenix members. Network centrality, such as being identified as a source of support (i.e., in-degree), was associated with lower psychological distress, while peripheral positions correlated with higher psychological distress in both networks. Additionally, individuals experiencing higher psychological distress tended to seek more supportive connections, whereas those with lower distress were more frequently nominated as supportive figures. These results highlight the potential of community-based recovery resources like The Phoenix to foster social networks that promote mental well-being.

Introduction: This study aims to explore the relationships among learning engagement, academic achievement, and employment quality for vocational school students in China. Learning engagement, defined by cognitive, emotional, and behavioral dimensions, plays a crucial role in academic achievement and employment quality. Method: Data from 624 graduates of vocational schools in Hunan Province in China were analyzed using structural equation modeling (SEM). Results: Findings revealed that cognitive, emotional, and behavioral engagement significantly predicted academic achievement, while only cognitive engagement had a direct positive impact on employment quality. Notably, academic achievement was found to have no significant direct effect on employment quality. Urban students reported higher employment quality than their rural peers, emphasizing disparities in educational and economic opportunities. Many graduates lacked formal labor contracts and alignment between their jobs and training, reflecting structural barriers to quality employment. Discussion: Recommendations include fostering learning engagement through targeted teaching strategies, career preparation education, and policies to reduce urban–rural disparities. Enhancing vocational education quality and promoting access to higher education can improve outcomes for vocational school students. The findings highlight the need for systemic reforms in vocational education to bridge gaps in learning engagement, academic achievement, and employment quality.