Progressive Prediction of Student Performance in College Programs


  • Jie Xu University of Miami
  • Yuli Han Tsinghua University
  • Daniel Marcu University of Southern California
  • Mihaela van der Schaar University of California, Los Angeles



Machine learning for education


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.




How to Cite

Xu, J., Han, Y., Marcu, D., & van der Schaar, M. (2017). Progressive Prediction of Student Performance in College Programs. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1).



Main Track: Machine Learning Applications