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DC Field | Value | Language |
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dc.contributor.author | Maldonado Mahauad, Jorge Javier | - |
dc.contributor.author | Pérez Sanagustín, Mar | - |
dc.contributor.author | Moreno Marcos, Pedro Manuel | - |
dc.contributor.author | Alario Hoyos, Carlos | - |
dc.contributor.author | Muñoz Merino, Pedro | - |
dc.contributor.author | Delgado Kloos, Carlos | - |
dc.date.accessioned | 2019-08-01T20:58:13Z | - |
dc.date.available | 2019-08-01T20:58:13Z | - |
dc.date.issued | 2018 | - |
dc.identifier.isbn | 978-331998571-8 | - |
dc.identifier.issn | 03029743 | - |
dc.identifier.uri | http://dspace.ucuenca.edu.ec/handle/123456789/33206 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85053215508&origin=inward | - |
dc.description | In the past years, predictive models in Massive Open Online Courses (MOOCs) have focused on forecasting learners’ success through their grades. The prediction of these grades is useful to identify problems that might lead to dropouts. However, most models in prior work predict categorical and continuous variables using low-level data. This paper contributes to extend current predictive models in the literature by considering coarse-grained variables related to Self-Regulated Learning (SRL). That is, using learners’ self-reported SRL strategies and MOOC activity sequence patterns as predictors. Lineal and logistic regression modelling were used as a first approach of prediction with data collected from N = 2,035 learners who took a self-paced MOOC in Coursera. We identified two groups of learners (1) Comprehensive, who follow the course path designed by the teacher; and (2) Targeting, who seek … | - |
dc.description.abstract | In the past years, predictive models in Massive Open Online Courses (MOOCs) have focused on forecasting learners’ success through their grades. The prediction of these grades is useful to identify problems that might lead to dropouts. However, most models in prior work predict categorical and continuous variables using low-level data. This paper contributes to extend current predictive models in the literature by considering coarse-grained variables related to Self-Regulated Learning (SRL). That is, using learners’ self-reported SRL strategies and MOOC activity sequence patterns as predictors. Lineal and logistic regression modelling were used as a first approach of prediction with data collected from N = 2,035 learners who took a self-paced MOOC in Coursera. We identified two groups of learners (1) Comprehensive, who follow the course path designed by the teacher; and (2) Targeting, who seek … | - |
dc.language.iso | es_ES | - |
dc.publisher | Springer Verlag | - |
dc.source | Lifelong Technology-Enhanced Learning | - |
dc.subject | Achievement | - |
dc.subject | Massive Open Online Courses | - |
dc.subject | Prediction | - |
dc.subject | Self-Regulated Learning | - |
dc.subject | Sequence Patterns | - |
dc.subject | Success | - |
dc.title | Predicting learners’ success in a self-paced MOOC through sequence patterns of self-regulated learning | - |
dc.type | ARTÍCULO DE CONFERENCIA | - |
dc.description.city | Leeds | - |
dc.ucuenca.idautor | 1102959051 | - |
dc.ucuenca.idautor | Sgrp-1548-2 | - |
dc.ucuenca.idautor | Sgrp-1548-3 | - |
dc.ucuenca.idautor | Sgrp-1548-4 | - |
dc.ucuenca.idautor | Sgrp-1548-5 | - |
dc.ucuenca.idautor | Sgrp-1548-6 | - |
dc.identifier.doi | 10.1007/978-3-319-98572-5_27 | - |
dc.ucuenca.embargoend | 2050-12-31 | - |
dc.ucuenca.version | Versión publicada | - |
dc.ucuenca.embargointerno | 2050-12-31 | - |
dc.ucuenca.areaconocimientounescoamplio | 06 - Información y Comunicación (TIC) | - |
dc.ucuenca.afiliacion | Maldonado, J., Pontifical Catholic University of Chile, Santiago, Chile; Maldonado, J., Universidad de Cuenca, Departamento de Ciencias de la Computación, Cuenca, Ecuador | - |
dc.ucuenca.afiliacion | Pérez, M., Pontifical Catholic University of Chile, Santiago, Chile | - |
dc.ucuenca.afiliacion | Moreno, P., Universidad Carlos III de Madrid, Leganés, España | - |
dc.ucuenca.afiliacion | Alario, C., Universidad Carlos III de Madrid, Leganés, España | - |
dc.ucuenca.afiliacion | Muñoz, P., Universidad Carlos III de Madrid, Leganés, España | - |
dc.ucuenca.afiliacion | Delgado, C., Universidad Carlos III de Madrid, Leganés, España | - |
dc.ucuenca.volumen | volumen 11082 LNCS | - |
dc.ucuenca.indicebibliografico | SCOPUS | - |
dc.ucuenca.factorimpacto | 0.295 | - |
dc.ucuenca.cuartil | Q2 | - |
dc.ucuenca.numerocitaciones | 0 | - |
dc.ucuenca.areaconocimientofrascatiamplio | 5. Ciencias Sociales | - |
dc.ucuenca.pais | REINO UNIDO | - |
dc.ucuenca.conferencia | EC-TEL 2018: 13th European Conference for Technology-Enhanced Learning | - |
dc.ucuenca.areaconocimientofrascatiespecifico | 5.9 Otras Ciencias Sociales | - |
dc.ucuenca.areaconocimientofrascatidetallado | 5.9.1 Ciencias Sociales Interdisciplinarias | - |
dc.ucuenca.areaconocimientounescoespecifico | 061 - Información y Comunicación (TIC) | - |
dc.ucuenca.areaconocimientounescodetallado | 0613 - Software y Desarrollo y Análisis de Aplicativos | - |
dc.ucuenca.fechainicioconferencia | 2018-09-03 | - |
dc.ucuenca.fechafinconferencia | 2018-09-06 | - |
dc.ucuenca.organizadorconferencia | University of Leeds | - |
dc.ucuenca.comiteorganizadorconferencia | Hendrik Drachsler, German Institute for International Educational Research, Goethe University Frankfurt am Main, Germany, Open University of the Netherlands | - |
dc.ucuenca.urifuente | https://link.springer.com/book/10.1007/978-3-319-98572-5 | - |
Appears in Collections: | Artículos |
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documento.pdf Until 2050-12-31 | document | 154.44 kB | Adobe PDF | View/Open Request a copy |
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