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We examine the process of engineering features for developing models that improve our understanding of learners' online behavior in MOOCs.
Predicting student retention in massive open online courses using hidden markov models
Balakrishnan, Girish and Coetzee, Derrick · 2013
Earlier work this paper cites.
Deconstructing disengagement: analyzing learner subpopulations in massive open online courses
Kizilcec, René F, Piech, Chris, and Schneider, Emily · 2013
Earlier work this paper cites.
Modeling learner engagement in moocs using probabilistic soft logic
Ramesh, Arti, Goldwasser, Dan, Huang, Bert, Daumé III, Hal, and Getoor, Lise · 2013
Earlier work this paper cites.
Moocdb: Developing standards and systems for mooc data science
Veeramachaneni, Kalyan, Halawa, Sherif, Dernoncourt, Franck, Taylor, Colin, and O’Reilly, Una-May · 2013
Cited alongside, same era.
Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses
Yang, Diyi, Sinha, Tanmay, Adamson, David, and Rosé, Carolyn Penstein · 2013
Cited alongside, same era.
Dropout prediction in moocs using learner activity features
Halawa, Sherif, Greene, Daniel, and Mitchell, John · 2014
Closest in time.
Learning latent engagement patterns of students in online courses
Ramesh, Arti, Goldwasser, Dan, Huang, Bert, Daume III, Hal, and Getoor, Lise · 2014
Closest in time.
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