The Factors Influencing the Transfer of Learning Outcomes in College Students' Mental Health Courses Using Data Mining

Abstract

This work aims to utilize data mining techniques to analyze the factors influencing the transfer of learning outcomes in college students' mental health courses. It explores the role of self-interpretation in indirectly promoting the transfer of learning outcomes to daily life situations by influencing course satisfaction. Based on social cognitive theory and self-determination theory, a theoretical model is developed to examine the relationships between self-interpretation, course satisfaction, and the transfer of learning outcomes. The work utilizes a questionnaire survey method, randomly selecting 250 university students from multiple domestic universities as the sample. Data are collected using a self-interpretation scale, a course satisfaction questionnaire, and a learning outcomes transfer assessment tool. Statistical software is used for data analysis, including descriptive statistics, correlation analysis, and path analysis to test the hypothesized relationships. The results show that self-interpretation is significantly positively correlated with both course satisfaction and the transfer of learning outcomes. Furthermore, self-interpretation plays a significant mediating role between course satisfaction and the transfer of learning outcomes. Course satisfaction promotes the transfer of learning outcomes through both direct and indirect pathways (by enhancing self-interpretation). Additionally, factors such as gender, age, and academic background are found to moderate these relationships to some extent. This work provides empirical evidence for the design and teaching strategies of mental health courses, emphasizing the importance of cultivating positive self-interpretation skills. The work also offers strategic guidance for educators to enhance course satisfaction and promote knowledge transfer through data mining techniques.