Analytics of Learning Strategies

Abstract

Learning analytics has the potential to detect and explain characteristics of learning strategies through analysis of trace data and communicate the findings via feedback. However, the role of learning analytics-based feedback in selection and regulation of learning strategies is still insufficiently explored and understood. This research aims to examine the sequential and temporal characteristics of learning strategies and investigate their association with feedback. Three years of trace data were collected from online pre-class activities of a flipped classroom, where different types of feedback were employed in each year. Clustering, sequence mining, and process mining were used to detect and interpret learning tactics and strategies. Inferential statistics were used to examine the association of feedback with the learning performance and the detected learning strategies. The results suggest a positive association between the personalised feedback and the effective strategies.