Domain Adaptation for Real-Time Student Performance Prediction
Increasingly fast development and update cycle of online course contents, and diverse demographics of students in each online classroom, make student performance prediction in real-time (before the course finishes) and/or on curriculum without specific historical performance data available interesti...
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Zusammenfassung: | Increasingly fast development and update cycle of online course contents, and
diverse demographics of students in each online classroom, make student
performance prediction in real-time (before the course finishes) and/or on
curriculum without specific historical performance data available interesting
topics for both industrial research and practical needs. In this research, we
tackle the problem of real-time student performance prediction with on-going
courses in a domain adaptation framework, which is a system trained on
students' labeled outcome from one set of previous coursework but is meant to
be deployed on another. In particular, we first introduce recently-developed
GritNet architecture which is the current state of the art for student
performance prediction problem, and develop a new \emph{unsupervised} domain
adaptation method to transfer a GritNet trained on a past course to a new
course without any (students' outcome) label. Our results for real Udacity
students' graduation predictions show that the GritNet not only
\emph{generalizes} well from one course to another across different Nanodegree
programs, but enhances real-time predictions explicitly in the first few weeks
when accurate predictions are most challenging. |
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DOI: | 10.48550/arxiv.1809.06686 |