Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses.
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 learn...
| Publicado en: | Computers in Human Behavior Vol. 80; pp. 179 - 197 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Mar2018
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=127099970&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 127099970 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07475632 JC4 jtl: Computers in Human Behavior issn: 07475632 maglogo: N pubinfo: dt: Mar2018 vid: 80 pid: 2410 pub: Elsevier B.V. artinfo: ui: 127099970 10.1016/j.chb.2017.11.011 ppf: 179 ppct: 18 formats: tig: atl: Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses. aug: au: Maldonado-Mahauad, Jorge Pérez-Sanagustín, Mar Kizilcec, René F. Morales, Nicolás Munoz-Gama, Jorge affil: Pontificia Universidad Católica de Chile, Department of Computer Science, Avda. Vicuña Mackenna 4860, Macul, Santiago, Chile Universidad de Cuenca, Department of Computer Science, Av. 12 Abril, Cuenca, Ecuador Stanford University, Graduate School of Education, 485 Lausen Mall, Stanford, CA 94305, USA su: Goal (Psychology) Self-evaluation Cognitive styles Data mining Massive open online courses sug: subj: Goal (Psychology) Self-evaluation Cognitive styles Data mining Massive open online courses keyword: Learning strategies Process mining Self-regulated learning Learning strategies Process mining Self-regulated learning ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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