Exploring the relationship between LMS interactions and academic performance: A Learning Cycle approach

Research on the relationship between the digital traces of students in Learning Management Systems (LMS) and their academic performance has traditionally been an area of interest in the field of learning analytics. Aiming at achieving high interpretability and generalizability, this study reviews past research, defines a new categorization scheme for interactions in LMS and investigates the relationships between clickstream data of students’ activity and course performance, measured as final grade. The results of the multiple regression analysis of Moodle log data collected from three courses of diverse nature using various classifications of interactions suggest that the new categorization, the CILC (Classification of Interactions based on the Learning Cycle), improves the explanatory power when compared to previous classifications. The analysis suggests that the predictive ability of the models could depend on the delivery mode, with predictions improving as the delivery mode transitions from face-to-face learning to online learning. This finding highlights the need for context-specific considerations about the learning process. Compared to previous research, the analysis also reveals nuances that suggest that the relationships may depend on the instructional design. Finally, the findings also seem to support the notion that increased data quantity and quality may improve predictive models. In summary, the study contributes with valuable insights into the interplay between LMS interactions, course delivery mode, instructional design, and academic performance, and advocates for a balanced exploration of white-box and black-box modeling approaches in learning analytics research. •We define a new categorization for LMS clickstream data based on the learning cycle.•The new categorization outperforms previous ones in explaining student performance.•The need for interpretability justifies white-box and black-box models’ coexistence.•The predictive power of LMS interaction data varies with course delivery mode.•The instructional design might affect the interaction-performance link.