research-article

Guess you like: course recommendation in MOOCs

Authors:

Jie TangAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 783 - 789

https://doi.org/10.1145/3106426.3106478

Published: 23 August 2017 Publication History

Abstract

Recommending courses to online students is a fundamental and also challenging issue in MOOCs. Not exactly like recommendation in traditional online systems, students who enrolled the same course may have very different purposes and with very different backgrounds. For example, one may want to study "data mining" after studying the course of "big data analytics" because the former is a prerequisite course of the latter, while some other may choose "data mining" simply because of curiosity.

Employing the complete data from XuetangX¹, one of the largest MOOCs in China, we conduct a systematic investigation on the problem of student behavior modeling for course recommendation. We design a content-aware algorithm framework using content based users' access behaviors to extract user-specific latent information to represent students' interest profile. We also leverage the demographics and course prerequisite relation to better reveal users' potential choice. Finally, we develop a course recommendation algorithm based on the user interest, demographic profiles and course prerequisite relation using collaborative filtering strategy. Experiment results demonstrate that the proposed algorithm performs much better than several baselines (over 2X by MRR). We have deployed the recommendation algorithm onto the platform XuetangX as a new feature, which significantly helps improve the course recommendation performance (+24.6% by click rate) comparing with the recommendation strategy previously used in the system.

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Cited By

Wei LLi YWang WZhu Y(2024)Enhancing Knowledge-Concept Recommendations with Heterogeneous Graph-Contrastive LearningMathematics10.3390/math1215232412:15(2324)Online publication date: 25-Jul-2024
https://doi.org/10.3390/math12152324
Chanaa AEl Faddouli N(2024)Prerequisites-based course recommendation: recommending learning objects using concept prerequisites and metadata matchingSmart Learning Environments10.1186/s40561-024-00301-011:1Online publication date: 11-May-2024
https://doi.org/10.1186/s40561-024-00301-0
Sun JMei SYuan KJiang YCao J(2024)Prerequisite-Enhanced Category-Aware Graph Neural Networks for Course RecommendationACM Transactions on Knowledge Discovery from Data10.1145/364364418:5(1-21)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3643644
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cover image ACM Conferences

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAI: ACM Special Interest Group on Artificial Intelligence
TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
Web Intelligence Consortium

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Wei LLi YWang WZhu Y(2024)Enhancing Knowledge-Concept Recommendations with Heterogeneous Graph-Contrastive LearningMathematics10.3390/math1215232412:15(2324)Online publication date: 25-Jul-2024
https://doi.org/10.3390/math12152324
Chanaa AEl Faddouli N(2024)Prerequisites-based course recommendation: recommending learning objects using concept prerequisites and metadata matchingSmart Learning Environments10.1186/s40561-024-00301-011:1Online publication date: 11-May-2024
https://doi.org/10.1186/s40561-024-00301-0
Sun JMei SYuan KJiang YCao J(2024)Prerequisite-Enhanced Category-Aware Graph Neural Networks for Course RecommendationACM Transactions on Knowledge Discovery from Data10.1145/364364418:5(1-21)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3643644
Wang YMa DMa JJin Q(2024)HGCR: A Heterogeneous Graph-Enhanced Interactive Course Recommendation Scheme for Online LearningIEEE Transactions on Learning Technologies10.1109/TLT.2023.331439917(364-374)Online publication date: 2024
https://doi.org/10.1109/TLT.2023.3314399
Ouyang YLong HGao RLiu J(2024)Course Recommendation Model Based on Layer Dropout Graph Differential Contrastive LearningIEEE Access10.1109/ACCESS.2024.335204312(7762-7774)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3352043
Ma WChen WLu LFan X(2024)Integrating learners’ knowledge background to improve course recommendation fairness: A multi-graph recommendation method based on contrastive learningInformation Processing & Management10.1016/j.ipm.2024.10375061:4(103750)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103750
Li SZhao YGuo LRen MLi JZhang LLi K(2024)Quantification and prediction of engagementInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10353661:1Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103536
Tian RCai JLi CWang J(2024)Self-Supervised pre-training model based on Multi-view for MOOC RecommendationExpert Systems with Applications10.1016/j.eswa.2024.124143252(124143)Online publication date: Oct-2024
https://doi.org/10.1016/j.eswa.2024.124143
Ma BYang TRen B(2024)A Survey on Explainable Course Recommendation SystemsDistributed, Ambient and Pervasive Interactions10.1007/978-3-031-60012-8_17(273-287)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-60012-8_17
Jia Y(2023)BTCBMA Online Education Course Recommendation Algorithm Based on Learners' Learning QualityInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.32410116:1(1-17)Online publication date: 9-Jun-2023
https://dl.acm.org/doi/10.4018/IJITSA.324101
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