Behavior prediction in MOOCs using higher granularity temporal information

L@S 2015 - 2nd ACM Conference on Learning at Scale

Volumn , Issue , 2015, Pages 335-338

  Ye, Cheng ^a   Fisher, Douglas H ^a   Kinnebrew, John S ^a   Narasimham, Gayathri ^a   Biswas, Gautam ^a   Brady, Katherine A ^a   Evans, Brent J ^a  

^a VANDERBILT UNIVERSITY   (United States)

Author keywords

Behavior prediction; Machine learning; MOOCs

Indexed keywords

LEARNING SYSTEMS;

BEHAVIOR PREDICTION; MOOCS; PREDICTIVE POWER; TEMPORAL FEATURES; TEMPORAL INFORMATION;

FORECASTING;

EID: 84928037335     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2724660.2728687     Document Type: Conference Paper

References (4)

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2. Clow, D. MOOCs and the funnel of participation. In Proceedings of the Third International Conference on Learning Analytics and Knowledge, ACM (2013), 185-189.
3. Halawa, S., Greene, D., and Mitchell, J. Dropout prediction in MOOCs using learner activity features. In Proceedings of the European MOOC Summit (EMOOCs 2014) (Lausanne, Switzerland, 2014).
4. Kizilcec, R. F., Piech, C., and Schneider, E. Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In Proceedings of the third international Conference on learning analytics and knowledge, ACM (2013), 170-179.