Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach

Yun Wei Chu, Elizabeth Tenorio, Laura Cruz, Kerrie Douglas, Andrew S. Lan, Christopher G. Brinton

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

8 Scopus citations


We study the problem of predicting student knowledge acquisition in online courses from clickstream behavior. Motivated by the proliferation of eLearning lecture delivery, we specifically focus on student in-video activity in lectures videos, which consist of content and in-video quizzes. Our methodology for predicting in-video quiz performance is based on three key ideas we develop. First, we model students' clicking behavior via time-series learning architectures operating on raw event data, rather than defining hand-crafted features as in existing approaches that may lose important information embedded within the click sequences. Second, we develop a self-supervised clickstream pre-training to learn informative representations of clickstream events that can initialize the prediction model effectively. Third, we propose a clustering guided meta-learning-based training that optimizes the prediction model to exploit clusters of frequent patterns in student clickstream sequences. Through experiments on three real-world datasets, we demonstrate that our method obtains substantial improvements over two base-line models in predicting students' in-video quiz performance. Further, we validate the importance of the pre-training and meta-learning components of our framework through ablation studies. Finally, we show how our methodology reveals insights on video-watching behavior associated with knowledge acquisition for useful learning analytics.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781665439022
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: Dec 15 2021Dec 18 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021


Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems


  • clickstream data
  • clustering
  • eLearning
  • meta-learning
  • performance prediction


Dive into the research topics of 'Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach'. Together they form a unique fingerprint.

Cite this