Prediction in MOOCs: A Review and Future Research Directions

Autor/inn/en	Moreno-Marcos, Pedro Manuel; Alario-Hoyos, Carlos; Munoz-Merino, Pedro J.; Kloos, Carlos Delgado
Titel	Prediction in MOOCs: A Review and Future Research Directions
Quelle	In: IEEE Transactions on Learning Technologies, 12 (2019) 3, S.384-401 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Moreno-Marcos, Pedro Manuel) ORCID (Alario-Hoyos, Carlos) ORCID (Munoz-Merino, Pedro J.) ORCID (Kloos, Carlos Delgado)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2018.2856808
Schlagwörter	Prediction; Large Group Instruction; Online Courses; Educational Research; Educational Trends; Distance Education; Electronic Learning; Models; Outcomes of Education; Research Methodology + Suchen Sie Ihr Suchwort? Vorhersage; Online course; Online-Kurs; Bildungsforschung; Pädagogische Forschung; Bildungsentwicklung; Distance study; Distance learning; Fernunterricht; Analogiemodell; Lernleistung; Schulerfolg; Research method; Forschungsmethode
Abstract	This paper surveys the state of the art on prediction in MOOCs through a systematic literature review (SLR). The main objectives are: first, to identify the characteristics of the MOOCs used for prediction, second, to describe the prediction outcomes, third, to classify the prediction features, fourth, to determine the techniques used to predict the variables, and, fifth, to identify the metrics used to evaluate the predictive models. Results show there is strong interest in predicting dropouts in MOOCs. A variety of predictive models are used, though regression and support vector machines stand out. There is also wide variety in the choice of prediction features, but clickstream data about platform use stands out. Future research should focus on developing and applying predictive models that can be used in more heterogeneous contexts (in terms of platforms, thematic areas, and course durations), on predicting new outcomes and making connections among them (e.g., predicting learners' expectancies), on enhancing the predictive power of current models by improving algorithms or adding novel higher-order features (e.g., efficiency, constancy, etc.). (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01