Early Prediction of Student Dropout and Performance in MOOCSs Using Higher Granularity Temporal Information

Autor/inn/en	Ye, Cheng; Biswas, Gautam
Titel	Early Prediction of Student Dropout and Performance in MOOCSs Using Higher Granularity Temporal Information
Quelle	In: Journal of Learning Analytics, 1 (2014) 3, S.169-172 (4 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Large Group Instruction; Online Courses; Educational Technology; Technology Uses in Education; Predictor Variables; Dropouts; Graduation Rate; Data Collection; Data Analysis; Educational Research; College Students; Tennessee + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Prädiktor; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Data capture; Datensammlung; Auswertung; Bildungsforschung; Pädagogische Forschung; Collegestudent
Abstract	Our project is motivated by the early dropout and low completion rate problem in MOOCs. We have extended traditional features for MOOC analysis with richer and higher granularity information to make more accurate predictions of dropout and performance. The results show that finer-grained temporal information increases the predictive power in the early phases of the Pattern-Oriented Software Architectures (POSA) MOOC offered in summer 2013 by Vanderbilt University. As a next step, we plan to develop unsupervised learning methods with our extended feature set to define profiles that can be used for effective scaffolding and feedback. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01