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AIED
2015
Springer

Transfer Learning for Predictive Models in Massive Open Online Courses

3 years 9 days ago
Transfer Learning for Predictive Models in Massive Open Online Courses
Data recorded while learners are interacting with Massive Open Online Courses (MOOC) platforms provide a unique opportunity to build predictive models that can help anticipate future behaviors and develop interventions. But since most of the useful predictive problems are defined for a real-time framework, using knowledge drawn from previous courses becomes crucial. To address this challenge, we designed a set of processes that take advantage of knowledge from both previous courses and previous weeks of the same course to make real time predictions on learners behavior. In particular, we evaluate multiple transfer learning methods. In this article, we present our results for the stopout prediction problem (predicting which learners are likely to stop engaging in the course). We believe this paper is a first step towards addressing the need of transferring knowledge across courses.
Sebastien Boyer, Kalyan Veeramachaneni
Added 14 Apr 2016
Updated 14 Apr 2016
Type Journal
Year 2015
Where AIED
Authors Sebastien Boyer, Kalyan Veeramachaneni
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