Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic.

Arti Ramesh     Dan Goldwasser     Bert Huang     Hal Daumé III     Lise Getoor.    
NIPS Workshop on Data Driven Education, 2013


Massive open online courses (MOOCs) attract a large number of student registrations, 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 active) in MOOCs. We use probabilistic soft logic (PSL) to model student engagement 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.

Bib Entry

    author = "Arti Ramesh and Dan Goldwasser and Bert Huang and Hal Daumé III and Lise Getoor.",
    title = "Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic.",
    booktitle = "NIPS Workshop on Data Driven Education",
    year = "2013"