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Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic
| Citations: | 8 - 2 self |
BibTeX
@MISC{Ramesh_modelinglearner,
author = {Arti Ramesh and Dan Goldwasser and Bert Huang and Hal Daume ́ Iii and Lise Getoor},
title = {Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic},
year = {}
}
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Abstract
Massive open online courses (MOOCs) attract a large number of student registra-tions, but recent studies have shown that only a small fraction of these students complete their courses. Student dropouts are thus a major deterrent for the growth and success of MOOCs. We believe that understanding student engagement as a course progresses is essential for minimizing dropout rates. Formally defining student engagement in an online setting is challenging. In this paper, we leverage activity (such as posting in discussion forums, timely submission of assignments, etc.), linguistic features from forum content and structural features from forum interaction to identify two different forms of student engagement (passive and ac-tive) in MOOCs. We use probabilistic soft logic (PSL) to model student engage-ment by capturing domain knowledge about student interactions and performance. We test our models on MOOC data from Coursera and demonstrate that modeling engagement is helpful in predicting student performance. 1
Keyphrases
probabilistic soft logic student engagement learner engagement small fraction large number student registra-tions major deterrent discussion forum student performance mooc data linguistic feature forum content massive open online course different form online setting student interaction domain knowledge recent study student dropout dropout rate timely submission forum interaction structural feature
