Literaturnachweis - Detailanzeige
Autor/inn/en | Ye, Cheng; Biswas, Gautam |
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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 |
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 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 |