Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses. (March 2018)

• Record Type: Journal Article
• Authors: Maldonado-Mahauad, Jorge; Pérez-Sanagustín, Mar; Kizilcec, René F.; Morales, Nicolás; Munoz-Gama, Jorge
• Is Part Of: Computers in human behavior. Volume 80(2018)
• Page Start: 179
• Page End: 196
• Publication Date: 2018-03
• Subjects: Self-regulated learning -- Learning strategies -- Process mining -- Massive open online courses; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2017.11.011
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: Big data in education offers unprecedented opportunities to support learners and advance research in the learning sciences. Analysis of observed behaviour using computational methods can uncover patterns that reflect theoretically established processes, such as those involved in self-regulated learning (SRL). This research addresses the question of how to integrate this bottom-up approach of mining behavioural patterns with the traditional top-down approach of using validated self-reporting instruments. Using process mining, we extracted interaction sequences from fine-grained behavioural traces for 3458 learners across three Massive Open Online Courses. We identified six distinct interaction sequence patterns. We matched each interaction sequence pattern with one or more theory-based SRL strategies and identified three clusters of learners. First, Comprehensive Learners, who follow the sequential structure of the course materials, which sets them up for gaining a deeper understanding of the content. Second, Targeting Learners, who strategically engage with specific course content that will help them pass the assessments. Third, Sampling Learners, who exhibit more erratic and less goal-oriented behaviour, report lower SRL, and underperform relative to both Comprehensive and Targeting Learners. Challenges that arise in the process of extracting theory-based patterns from observed behaviour are discussed, including analytic issues and limitations of available trace data from learning platforms. Highlights: Self-regulated learning (SRL) is critical for learners in MOOCs to succeed. We integrate mining of behavioural patterns with self-reported instruments. Six distinct learner interaction sequences with course content were identified. Interaction sequences were related to SRL strategies described in the literature.