Dissecting learning tactics in MOOC using ordered network analysis. (30th August 2022)

• Record Type: Journal Article
• Authors: Fan, Yizhou; Tan, Yuanru; Raković, Mladen; Wang, Yeyu; Cai, Zhiqiang; Shaffer, David Williamson; Gašević, Dragan
• Is Part Of: Journal of computer assisted learning. Volume 39:Number 1(2023)
• Page Start: 154
• Page End: 166
• Publication Date: 2022-08-30
• Subjects: learning analytics -- learning tactics -- MOOCs -- ordered network analysis -- process mining; Computer-assisted instruction -- Periodicals; 371.334
• DOI: 10.1111/jcal.12735
• Languages: English
• ISSNs: 0266-4909

Abstract

Abstract: Background: Select and enact appropriate learning tactics that advance learning has been considered a critical set of skills to successfully complete highly flexible online courses, such as Massive open online courses (MOOCs). However, limited by analytic methods that have been used in the past, such as frequency distribution, sequence mining and process mining, we lack a deep, complete and detailed understanding of the learning tactics used by MOOC learners. Objectives: In the present study, we proposed four major dimensions to better interpret and understand learning tactics, which are frequency, continuity, sequentiality and role of learning actions within tactics. The aim of this study was to examine to what extent can a new analytic technique, the ordered network analysis (ONA), deepen the understanding of MOOC learning tactics compared to using other methods. Methods: In particular, we performed a fine‐grained analysis of learning tactics detected from more than 4 million learning events in the behavioural trace data of 8788 learners who participated in a large‐scale MOOC 'Flipped Classroom'. Results and Conclusions: We detected eight learning tactics, and then chose one typical tactic as an example to demonstrate how the ONA technique revealed all four dimensions and provided deeper insights into this MOOC learning tactic. Most importantly, based on the comparison with different methods such as process mining, we found that the ONA method provided a unique opportunity and novel insight into the roles of different learning actions in tactics which was neglected in the past. Takeaway: In summary, we conclude that ONA is a promising technique that can benefit the research on learning tactics, and ultimately benefit MOOC learners by strengthening the strategic support. Lay Description: What is already known about this topic?: Since external support from instructors or peers are usually limited in MOOCs, the ability to self‐regulate learning (SRL), that is, select and enact goal‐oriented learning tactics that advance learning comes to be a critical set of skills to successfully complete coursework. Researchers have utilised analytic techniques that go beyond frequency‐based approaches, for example, process mining and sequence mining, to detect and understand learning tactics. However, limited by previous analytic methods that have been used in the past, we still lack a deep, complete and detailed understanding of the learning tactics used by MOOC learners. What this paper adds?: We proposed four major dimensions to better interpret and understand learning tactics, which are frequency, continuity, sequentiality and role of learning actions within tactics. We examined to what extent can a new analytic technique, the ordered network analysis (ONA), deepen the understanding of learning tactics compared to other methods. We found that the ONA method provided a unique opportunity and novel insight into the roles of different learning actions in tactics which was neglected in the past. Implications for practice and/or policy: Researchers who study learning tactics unpacked using the ONA technique can gain a deeper insight into a theorised learning processes that interplay within and across learning tactics and, in that way, improve their understanding of how self‐regulated learners enact and monitor learning tactics. The analytics on learning tactics use in a form of ONA graphs has the potential to provide learners with a detailed overview of their learning engagement over the selected learning period in MOOCs. The ONA graphs of learning tactics, combined with the information about learning performance and course requirements, instructors or learners (prompted by instructors) can evaluate whether the way learners have studied in a MOOC was beneficial to learners' learning success.