Année : 2018
Lieu de publication de l'article :

Résumé de l'article

Learning analytics has the potential to detect and explain character-istics of learning strategies through analysis of trace data and com-municate 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 re-search aims to examine the sequential and temporal characteristics of learning strategies and investigate their association with feed-back. 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 pro-cess mining were used to detect and interpret learning tactics and strategies. Inferential statistics were used to examine the associa-tion of feedback with the learning performance and the detected learning strategies. The results suggest a positive association be-tween the personalised feedback and the effective strategies. CCS CONCEPTS • Applied computing~Computer-assisted instruction KEYWORDS Learning Analytics, Learning Strategies, Learning Tactics, Data Mining, Self-regulated Learning, Feedback ACM Reference Format: W. Matcha, D. Gašević , N. Ahmad Uzir, J. Jovanović and A. Pardo. 2019 Analytics of Learning Strategies: Associations with Academic Performance and Feedback. In The 9th International Learning Analytics and Knowledge Conference (LAK19), March, 2019, Tempe, AZ, USA. ACM, New York, NY, USA. 10 pages. https://doi.org/10.1145/3303772.3303787

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