Learner behavioral feature refinement and augmentation using GANs

Da Cao, Andrew S. Lan, Weiyu Chen, Christopher G. Brinton, Mung Chiang

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

2 Scopus citations


Learner behavioral data (e.g., clickstream activity logs) collected by online education platforms contains rich information about learners and content, but is often highly redundant. In this paper, we study the problem of learning low-dimensional, interpretable features from this type of raw, high-dimensional behavioral data. Based on the premise of generative adversarial networks (GANs), our method refines a small set of human-crafted features while also generating a set of additional, complementary features that better summarize the raw data. Through experimental validation on a real-world dataset that we collected from an online course, we demonstrate that our method leads to features that are both predictive of learner quiz scores and closely related to human-crafted features.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
EditorsRose Luckin, Kaska Porayska-Pomsta, Benedict du Boulay, Manolis Mavrikis, Carolyn Penstein Rosé, Bruce McLaren, Roberto Martinez-Maldonado, H. Ulrich Hoppe
PublisherSpringer Verlag
Number of pages6
ISBN (Print)9783319938455
StatePublished - 2018
Event19th International Conference on Artificial Intelligence in Education, AIED 2018 - London, United Kingdom
Duration: Jun 27 2018Jun 30 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10948 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Artificial Intelligence in Education, AIED 2018
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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