Analyzing the consistency in within-activity learning patterns in blended learning

Abstract

Performance and consistency play a large role in learning. This study analyzes the relation between consistency in students' online work habits and academic performance in a blended course. We utilize the data from logs recorded by a learning management system (LMS) in two information technology courses. The two courses required the completion of monthly asynchronous online discussion tasks and weekly assignments, respectively. We measure consistency by using Data Time Warping (DTW) distance for two successive tasks (assignments or discussions), as an appropriate measure to assess similarity of time series, over 11-day timeline starting 10 days before and up to the submission deadline. We found meaningful clusters of students exhibiting similar behavior and we use these to identify three distinct consistency patterns: highly consistent, incrementally consistent, and inconsistent users. We also found evidence of significant associations between these patterns and learner's academic performance.