Recovering concept prerequisite relations from university course dependencies

Chen Liang, Jianbo Ye, Zhaohui Wu, Bart Pursel, C. Lee Giles

Research output: Contribution to conferencePaper

20 Scopus citations

Abstract

Prerequisite relations among concepts play an important role in many educational applications such as intelligent tutoring system and curriculum planning. With the increasing amount of educational data available, automatic discovery of concept prerequisite relations has become both an emerging research opportunity and an open challenge. Here, we investigate how to recover concept prerequisite relations from course dependencies and propose an optimization based framework to address the problem. We create the first real dataset for empirically studying this problem, which consists of the listings of computer science courses from 11 U.S. universities and their concept pairs with prerequisite labels. Experiment results on a synthetic dataset and the real course dataset both show that our method outperforms existing baselines.

Original languageEnglish (US)
Pages4786-4791
Number of pages6
StatePublished - Jan 1 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: Feb 4 2017Feb 10 2017

Other

Other31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUnited States
CitySan Francisco
Period2/4/172/10/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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    Liang, C., Ye, J., Wu, Z., Pursel, B., & Giles, C. L. (2017). Recovering concept prerequisite relations from university course dependencies. 4786-4791. Paper presented at 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, United States.