DiAd: Domain Adaptation for Learning at Scale

Abstract

Massive online courses occupy an important place in the educational landscape of today. We study an approach to scale predictive analytic models derived from online course discussion fora--specifically that of confusion detection--onto other courses. The primary challenge here is the lack of labeled examples in a new course and this calls for unsupervised domain adaptation (DA). As a first step in exploring DA in the education domain, we propose a simple algorithm, DiAd, which adapts a classifier trained on a course with labeled data by selectively choosing instances from a new course (with no labeled data) that are most dissimilar to the course with labeled data and on which the classifier is very confident of classification. Our algorithm is empirically validated on the confusion detection task across multiple online courses. We find that DiAd outperforms other methods on the target domain, while showing a comparable performance to a popular method that uses labeled data from the target domain.