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ERIC Number: EJ1127084
Record Type: Journal
Publication Date: 2014
Pages: 4
Abstractor: As Provided
Reference Count: 11
ISBN: N/A
ISSN: EISSN-1929-7750
Early Prediction of Student Dropout and Performance in MOOCSs Using Higher Granularity Temporal Information
Ye, Cheng; Biswas, Gautam
Journal of Learning Analytics, v1 n3 p169-172 2014
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.
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/
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Tennessee