Exploring multi-objective exercise recommendations in online education systems

Zhenya Huang, Qi Liu, Chengxiang Zhai, Yu Yin, Enhong Chen, Weibo Gao, Guoping Hu

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

Abstract

Recommending suitable exercises to students in an online education system is highly useful. Existing approaches usually rely on machine learning techniques to mine large amounts of student interaction log data accumulated in the systems to select the most suitable exercises for each student. Generally, they mainly aim to optimize a single objective, i.e., recommending non-mastered exercises to address the immediate weakness of students. While this is a reasonable objective, there exist more beneficial multiple objectives in the long-term learning process that need to be addressed including Review & Explore, Smoothness of difficulty level and Engagement. In this paper, we propose a novel Deep Reinforcement learning framework, namely DRE, for adaptively recommending Exercises to students with optimization of above three objectives. In the framework, we propose two different Exercise Q-Networks for the agent, i.e., EQNM and EQNR, to generate recommendations following Markov property and Recurrent manner, respectively. We also propose novel reward functions to formally quantify those three objectives so that DRE could update and optimize its recommendation strategy by interactively receiving students' performance feedbacks (e.g., score). We conduct extensive experiments on two real-world datasets. Experimental results clearly show that the proposed DRE can effectively learn from the student interaction data to optimize multiple objectives in a single unified framework and adaptively recommend suitable exercises to students.

Original languageEnglish (US)
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1261-1270
Number of pages10
ISBN (Electronic)9781450369763
DOIs
StatePublished - Nov 3 2019
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: Nov 3 2019Nov 7 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
CountryChina
CityBeijing
Period11/3/1911/7/19

Keywords

  • Deep reinforcement learning
  • Multiple objectives
  • Recommendation

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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  • Cite this

    Huang, Z., Liu, Q., Zhai, C., Yin, Y., Chen, E., Gao, W., & Hu, G. (2019). Exploring multi-objective exercise recommendations in online education systems. In CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 1261-1270). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3357384.3357995