Automatically Learning Topics and Difficulty Levels of Problems in Online Judge Systems


Online Judge (OJ) systems have been widely used in many areas, including programming, mathematical problems solving, and job interviews. Unlike other online learning systems, such as Massive Open Online Course, most OJ systems are designed for self-directed learning without the intervention of teachers. Also, in most OJ systems, problems are simply listed in volumes and there is no clear organization of them by topics or difficulty levels. As such, problems in the same volume are mixed in terms of topics or difficulty levels. By analyzing large-scale users’ learning traces, we observe that there are two major learning modes (or patterns). Users either practice problems in a sequential manner from the same volume regardless of their topics or they attempt problems about the same topic, which may spread across multiple volumes. Our observation is consistent with the findings in classic educational psychology. Based on our observation, we propose a novel two-mode Markov topic model to automatically detect the topics of online problems by jointly characterizing the two learning modes. For further predicting the difficulty level of online problems, we propose a competition-based expertise model using the learned topic information. Extensive experiments on three large OJ datasets have demonstrated the effectiveness of our approach in three different tasks, including skill topic extraction, expertise competition prediction and problem recommendation.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
?? 50811700Jl ??
Additional Information: © ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Information Systems, VOL# 36, ISS# 3, (13 Mar 2018)
Uncontrolled Keywords: Topic models,expertise learning,online judge systems
Publication ISSN: 1558-2868
Last Modified: 19 Apr 2024 07:13
Date Deposited: 19 Dec 2017 14:20
Full Text Link:
Related URLs: https://dl.acm. ... 3146384.3158670 (Publisher URL)
PURE Output Type: Article
Published Date: 2018-03-13
Accepted Date: 2017-12-14
Authors: Zhao, Wayne Xin
Zhang, Wenhui
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Xie, Xing
Wen, Ji-Rong



Version: Published Version

Access Restriction: Restricted to Repository staff only


Version: Accepted Version

| Preview

Export / Share Citation


Additional statistics for this record