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DC Field | Value | Language |
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dc.contributor.author | Maldonado Mahauad, Jorge Javier | - |
dc.contributor.author | Perez Sanagustín, Mar | - |
dc.contributor.author | Kizilcec, Rene | - |
dc.contributor.author | Muñoz Gama, Jorge | - |
dc.date.accessioned | 2018-10-19T18:03:47Z | - |
dc.date.available | 2018-10-19T18:03:47Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0747-5632 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85034415787&origin=inward | - |
dc.description | 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 … | - |
dc.description.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 … | - |
dc.language.iso | es_ES | - |
dc.source | Computers in Human Behavior | - |
dc.subject | Learning Strategies | - |
dc.subject | Massive Open Online Courses | - |
dc.subject | Process Mining | - |
dc.subject | Self-Regulated Learning | - |
dc.title | Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses | - |
dc.type | ARTÍCULO | - |
dc.ucuenca.idautor | 1102959051 | - |
dc.ucuenca.idautor | 0000-0001-9854-9963 | - |
dc.ucuenca.idautor | 0000-0001-6283-5546 | - |
dc.ucuenca.idautor | 0000-0002-6908-3911 | - |
dc.identifier.doi | 10.1016/j.chb.2017.11.011 | - |
dc.ucuenca.version | Versión publicada | - |
dc.ucuenca.areaconocimientounescoamplio | 06 - Información y Comunicación (TIC) | - |
dc.ucuenca.afiliacion | Maldonado, J., Universidad de Cuenca, Departamento de Ciencias de la Computación, Cuenca, Ecuador; Maldonado, J., Pontificia Universidad Catolica de Chile, Santiago, Chile | - |
dc.ucuenca.afiliacion | Perez, M., Pontificia Universidad Catolica de Chile, Santiago, Chile | - |
dc.ucuenca.afiliacion | Kizilcec, R., Stanford University, Palo Alto, Estados unidos | - |
dc.ucuenca.afiliacion | Muñoz, J., Pontificia Universidad Catolica de Chile, Santiago, Chile | - |
dc.ucuenca.correspondencia | Maldonado Mahauad, Jorge Javier, jjmaldonado@uc.cl | - |
dc.ucuenca.volumen | volumen 80, número 0 | - |
dc.ucuenca.indicebibliografico | SCOPUS | - |
dc.ucuenca.factorimpacto | 1.555 | - |
dc.ucuenca.cuartil | Q1 | - |
dc.ucuenca.numerocitaciones | 1 | - |
dc.ucuenca.areaconocimientofrascatiamplio | 1. Ciencias Naturales y Exactas | - |
dc.ucuenca.areaconocimientofrascatiespecifico | 1.2 Informática y Ciencias de la Información | - |
dc.ucuenca.areaconocimientofrascatidetallado | 1.2.1 Ciencias de la Computación | - |
dc.ucuenca.areaconocimientounescoespecifico | 061 - Información y Comunicación (TIC) | - |
dc.ucuenca.areaconocimientounescodetallado | 0612 - Base de Datos, Diseno y Administración de Redes | - |
dc.ucuenca.urifuente | http://www.sciencedirect.com/science/journal/07475632 | - |
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documento.pdf | document | 2.12 MB | Adobe PDF | View/Open |
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