DiAD: Domain adaptation for learning at scale

Ziheng Zeng, Suma Bhat, Snigdha Chaturvedi, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings of the 9th International Conference on Learning Analytics and Knowledge
Subtitle of host publicationLearning Analytics to Promote Inclusion and Success, LAK 2019
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450362566
StatePublished - Mar 4 2019
Externally publishedYes
Event9th International Conference on Learning Analytics and Knowledge, LAK 2019 - Tempe, United States
Duration: Mar 4 2019Mar 8 2019

Publication series

NameACM International Conference Proceeding Series


Conference9th International Conference on Learning Analytics and Knowledge, LAK 2019
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


  • Confusion detection
  • Domain adaptation
  • Learning at scale


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