Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches

• Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Main Author: Matcha W.; Gašević D.; Ahmad Uzir N.; Jovanović J.; Pardo A.; Maldonado-Mahauad J.; Pérez-Sanagustín M.
• Format: Conference paper
• Language: English
• Published: Springer Verlag 2019
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072953731&doi=10.1007%2f978-3-030-29736-7_39&partnerID=40&md5=003ab8737ce5a9f829b0d2cb5462cba0

Research in learning analytics proposed different computational techniques to detect learning tactics and strategies adopted by learners in digital environments through the analysis of students’ trace data. While many promising insights have been produced, there has been much less understanding about how and to what extent different data analytic approaches influence results. This paper presents a comparison of three analytic approaches including process, sequence, and network approaches for detection of learning tactics and strategies. The analysis was performed on a dataset collected in a massive open online course on software programming. All three approaches produced four tactics and three strategy groups. The tactics detected by using the sequence analysis approach differed from those identified by the other two methods. The process and network analytic approaches had more than 66% of similarity in the detected tactics. Learning strategies detected by the three approaches proved to be highly similar. © 2019, Springer Nature Switzerland AG.