Progressive prediction of student performance in college programs

Jie Xu, Yuli Han, Daniel Marcu, Mihaela Van Der Schaar

Research output: Contribution to conferencePaperpeer-review

11 Scopus citations


Accurately predicting students' future performance based on their tracked academic records in college programs is crucial for effectively carrying out necessary pedagogical interventions to ensure students' on-time graduation. Although there is a rich literature on predicting student performance in solving problems and studying courses using data-driven approaches, predicting student performance in completing college programs is much less studied and faces new challenges, mainly due to the diversity of courses selected by students and the requirement of continuous tracking and incorporation of students' evolving progresses. In this paper, we develop a novel algorithm that enables progressive prediction of students' performance by adapting ensemble learning techniques and utilizing education-specific domain knowledge. We prove its prediction performance guarantee and show its performance improvement against benchmark algorithms on a real-world student dataset from UCLA.

Original languageEnglish (US)
Number of pages7
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: Feb 4 2017Feb 10 2017


Other31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/TerritoryUnited States
CitySan Francisco

ASJC Scopus subject areas

  • Artificial Intelligence


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