Abstract
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 the past 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.
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Boyer, S., Veeramachaneni, K. (2015). Transfer Learning for Predictive Models in Massive Open Online Courses. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_6
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DOI: https://doi.org/10.1007/978-3-319-19773-9_6
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