Analytics of time management and learning strategies for effective online learning in blended environments

• Published in: ACM International Conference Proceeding Series
• Main Author: Uzir N.A.A.; Gaševic D.; Jovanovic J.; Matcha W.; Lim L.-A.; Fudge A.
• Format: Conference paper
• Language: English
• Published: Association for Computing Machinery 2020
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082385367&doi=10.1145%2f3375462.3375493&partnerID=40&md5=bbc6e041f77d5fa0c3137c729af6f990

This paper reports on the findings of a study that proposed a novel learning analytics methodology that combines three complimentary techniques - agglomerative hierarchical clustering, epistemic network analysis, and process mining. The methodology allows for identification and interpretation of self-regulated learning in terms of the use of learning strategies. The main advantage of the new technique over the existing ones is that it combines the time management and learning tactic dimensions of learning strategies, which are typically studied in isolation. The new technique allows for novel insights into learning strategies by studying the frequency of, strength of connections between, and ordering and time of execution of time management and learning tactics. The technique was validated in a study that was conducted on the trace data of first-year undergraduate students who were enrolled into two consecutive offerings (N2017 = 250 and N2018 = 232) of a course at an Australian university. The application of the proposed technique identified four strategy groups derived from three distinct time management tactics and five learning tactics. The tactics and strategies identified with the technique were correlated with academic performance and were interpreted according to the established theories and practices of self-regulated learning. © 2020 Association for Computing Machinery.