research-article

Learning Concept Prerequisite Relations from Educational Data via Multi-Head Attention Variational Graph Auto-Encoders

Authors:

Zhiyong PengAuthors Info & Claims

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Pages 1377 - 1385

https://doi.org/10.1145/3488560.3498434

Published: 15 February 2022 Publication History

Abstract

Recently, the topic of learning concept prerequisite relations has gained the attention of many researchers, which is crucial in the learning process for a learner to decide an optimal study order. However, the existing work still ignores three key factors. (1) People's cognitive differences could make a difference for annotating the prerequisite relation between resources (e.g., courses, textbooks) or concepts (e.g., binary tree). (2) The current vertex (resources or concepts) can be affected by the feature of the neighbor vertex in the resource or concept graph. (3) The feature information of the resource graph may affect the concept graph. To integrate the above factors, we propose an end-to-end graph network-based model called Multi-Head Attention Variational Graph Auto-Encoders (MHAVGAE ) to learn the prerequisite relation between concepts via a resource-concept graph. To address the first two problems, we introduce the multi-head attention mechanism to operate and compute the hidden representations of each vertex over the resource-concept graph. Then, we design a gated fusion mechanism to integrate the feature information of the resource and concept graphs to enrich concept content features. Finally, we conduct numerous experiments to demonstrate the effectiveness of the MHAVGAE across multiple widely used metrics compared with the state-of-the-art methods. The experimental results show that the performance of the MHAVGAE almost outperforms all the baseline methods.

Supplementary Material

MP4 File (WSDM22-338.mp4)

Presentation video of our paper (ID 338).

Download
17.13 MB

References

[1]

Fareedah Alsaad, Assma Boughoula, Chase Geigle, Hari Sundaram, and Chengxiang Zhai. 2018. Mining MOOC Lecture Transcripts to Construct Concept Dependency Graphs. In Proceedings of the 11th International Conference on Educational Data Mining .

[2]

Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018. Unsupervised Statistical Machine Translation. In Proceedings of the Proceedings of the Conference on Empirical Methods in Natural Language Processing . 3632--3642.

[3]

Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. In Proceedings of the 2nd International Conference on Learning Representations .

[4]

Sahar Changuel, Nicolas Labroche, and Bernadette Bouchon-Meunier. 2015. Resources Sequencing Using Automatic Prerequisite-Outcome Annotation. ACM Transactions on Intelligent Systems and Technology., Vol. 6, 1 (2015), 1--30.

Digital Library

[5]

Hongxu Chen, Hongzhi Yin, Xiangguo Sun, Tong Chen, Bogdan Gabrys, and Katarzyna Musial. 2020. Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction. In Proceedings of the The 26th ACM Conference on Knowledge Discovery and Data Mining. 1503--1511.

Digital Library

[6]

Penghe Chen, Yu Lu, Vincent W. Zheng, and Yang Pian. 2018. Prerequisite-Driven Deep Knowledge Tracing. In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17--20, 2018. 39--48.

[7]

Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, and Yanwen Guo. 2019. Multi-Label Image Recognition With Graph Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 5177--5186.

[8]

Zhiyong Cui, Kristian Henrickson, Ruimin Ke, and Yinhai Wang. 2020. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. IEEE Transactions on Intelligent Transportation Systems., Vol. 21, 11 (2020), 4883--4894.

[9]

Jibing Gong, Shen Wang, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, and Philip S. Yu. 2020. Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View. In Proceedings of the 43rd International ACM conference on research and development in Information Retrieval . 79--88.

[10]

Jonathan Gordon, Linhong Zhu, Aram Galstyan, Prem Natarajan, and Gully Burns. 2016. Modeling Concept Dependencies in a Scientific Corpus. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics . 866--875.

[11]

Mikael Henaff, Joan Bruna, and Yann LeCun. 2015. Deep Convolutional Networks on Graph-Structured Data. CoRR, Vol. abs/1506.05163 (2015).

[12]

Bin Shyan Jong, LuYung Wu, and Te Yi Chan. 2006. Dynamic Grouping Strategies Based on a Conceptual Graph for Cooperative Learning. IEEE Transactions on Knowledge and Data Engineering., Vol. 18, 6 (2006), 738--747.

Digital Library

[13]

Thomas N. Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. CoRR, Vol. abs/1611.07308 (2016).

[14]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations .

[15]

Stephen Laurence and Eric Margolis. 1999. Concepts and cognitive science. Concepts: core readings (1999), 3--81.

[16]

Irene Li, Alexander R. Fabbri, Swapnil Hingmire, and Dragomir R. Radev. 2020. R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning. In Proceedings of the 28th International Conference on Computational Linguistics. 1147--1157.

[17]

Irene Li, Alexander R. Fabbri, Robert R. Tung, and Dragomir R. Radev. 2019. What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence. 6674--6681.

[18]

Chen Liang, Zhaohui Wu, Wenyi Huang, and C. Lee Giles. 2015. Measuring Prerequisite Relations Among Concepts. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1668--1674.

[19]

Chen Liang, Jianbo Ye, Zhaohui Wu, Bart Pursel, and C. Lee Giles. 2017. Recovering Concept Prerequisite Relations from University Course Dependencies. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence . 4786--4791.

[20]

Hanxiao Liu, Wanli Ma, Yiming Yang, and Jaime G. Carbonell. 2016. Learning Concept Graphs from Online Educational Data. Journal of Artificial Intelligence Research., Vol. 55 (2016), 1059--1090.

Digital Library

[21]

Weiming Lu, Yangfan Zhou, Jiale Yu, and Chenhao Jia. 2019. Concept Extraction and Prerequisite Relation Learning from Educational Data. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence . 9678--9685.

Digital Library

[22]

Liangming Pan, Chengjiang Li, Juanzi Li, and Jie Tang. 2017. Prerequisite Relation Learning for Concepts in MOOCs. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics . 1447--1456.

[23]

Jean Michel Rouly, Huzefa Rangwala, and Aditya Johri. 2015. What Are We Teaching?: Automated Evaluation of CS Curricula Content Using Topic Modeling. In Proceedings of the eleventh annual International Conference on International Computing Education Research . 189--197.

Digital Library

[24]

Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, and Vaibhav Rajan. 2019. Inferring Concept Prerequisite Relations from Online Educational Resources. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence . 9589--9594.

Digital Library

[25]

Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling Relational Data with Graph Convolutional Networks. In Proceedings of the The Semantic Web - 15th International Conference. 593--607.

[26]

Partha P. Talukdar and William W. Cohen. 2012. Crowdsourced Comprehension: Predicting Prerequisite Structure in Wikipedia. In Proceedings of the Seventh Workshop on Building Educational Applications Using NLP . 307--315.

[27]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In Proceedings of the 6th International Conference on Learning Representations .

[28]

Sheng Wang, Zhifeng Bao, J. Shane Culpepper, Timos Sellis, and Xiaolin Qin. 2019 a. Fast Large-Scale Trajectory Clustering. Proc. VLDB Endow., Vol. 13, 1 (2019), 29--42.

Digital Library

[29]

Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. 2019 b. Heterogeneous Graph Attention Network. In Proceedings of the The World Wide Web Conference . 2022--2032.

[30]

Juntao Zhang, Biao Li, Wei Song, Nanzhou Lin, Xiandi Yang, and Zhiyong Peng. 2020. Learning Ability Community for Personalized Knowledge Tracing. In Web and Big Data - 4th International Joint Conference, APWeb-WAIM 2020, Tianjin, China, September 18--20, 2020, Proceedings, Part II. 176--192.

[31]

Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2018. Deep Learning on Graphs: A Survey. CoRR, Vol. abs/1812.04202 (2018).

[32]

Gianlucca L. Zuin, Luiz Chaimowicz, and Adriano Veloso. 2018. Learning Transferable Features For Open-Domain Question Answering. In Proceedings of the International Joint Conference on Neural Networks. 1--8.

Cited By

Bazouzi ALe Capitaine HMiklos ZFoursov M(2024)Precedability Prediction Between Open Educational ResourcesProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678686(386-393)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3677525.3678686
Zhang GZeng XWeng YWu Z(2024)Similarity Metrics and Visualization of Scholars Based on Variational Graph Normalized Auto-EncodersComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-9637-7_5(64-77)Online publication date: 5-Jan-2024
https://doi.org/10.1007/978-981-99-9637-7_5
Tang XLiu KXu HXiao WTan Z(2023)Continual Pre-Training of Language Models for Concept Prerequisite Learning with Graph Neural NetworksMathematics10.3390/math1112278011:12(2780)Online publication date: 20-Jun-2023
https://doi.org/10.3390/math11122780
Show More Cited By

Index Terms

Learning Concept Prerequisite Relations from Educational Data via Multi-Head Attention Variational Graph Auto-Encoders
1. Applied computing
  1. Education
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks

Recommendations

Weakly supervised setting for learning concept prerequisite relations using multi-head attention variational graph auto-encoders
Abstract
An increasing number of learners can benefit from educational resources in Massive Open Online Courses (MOOCs) through self-regulated learning. However, it is difficult for learners to organize a suitable learning path from educational resources ...
WikiCPRL: A Weakly Supervised Approach for Wikipedia Concept Prerequisite Relation Learning
Web and Big Data
Abstract
Concept prerequisite relations determine the order in which knowledge concept is learned. This kind of concept relations has been used in a variety of educational applications, such as curriculum planning, learning resource sequencing, and reading ...
Concept Graph Learning from Educational Data
WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

This paper addresses an open challenge in educational data mining, i.e., the problem of using observed prerequisite relations among courses to learn a directed universal concept graph, and using the induced graph to predict unobserved prerequisite ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

February 2022

1690 pages

ISBN:9781450391320

DOI:10.1145/3488560

General Chairs:
K. Selcuk Candan
Arizona State University, USA
,
Huan Liu
Arizona State University, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Xin Luna Dong
Meta Platforms, Inc. (former Facebook), USA
,
Jiliang Tang
Michigan State University, USA

Copyright © 2022 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 February 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

the National Natural Science Foundation of China; the National Natural Science Foundation of China; the National Key Research and Development Project of China; the Science and Technology Major Project of Hubei Province

Conference

WSDM '22

Sponsor:

WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining

February 21 - 25, 2022

AZ, Virtual Event, USA

Acceptance Rates

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
467
Total Downloads

Downloads (Last 12 months)112
Downloads (Last 6 weeks)3

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Bazouzi ALe Capitaine HMiklos ZFoursov M(2024)Precedability Prediction Between Open Educational ResourcesProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678686(386-393)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3677525.3678686
Zhang GZeng XWeng YWu Z(2024)Similarity Metrics and Visualization of Scholars Based on Variational Graph Normalized Auto-EncodersComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-9637-7_5(64-77)Online publication date: 5-Jan-2024
https://doi.org/10.1007/978-981-99-9637-7_5
Tang XLiu KXu HXiao WTan Z(2023)Continual Pre-Training of Language Models for Concept Prerequisite Learning with Graph Neural NetworksMathematics10.3390/math1112278011:12(2780)Online publication date: 20-Jun-2023
https://doi.org/10.3390/math11122780
Zhang MZhu XZhang CQian WPan FZhao HFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge ConceptsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614827(3236-3246)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614827
Mazumder DPaik JBasu AFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)A Graph Neural Network Model for Concept Prerequisite Relation ExtractionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614761(1787-1796)Online publication date: 21-Oct-2023
https://doi.org/10.1145/3583780.3614761
Zhang JLan HYang XZhang SSong WPeng Z(2022)Weakly supervised setting for learning concept prerequisite relations using multi-head attention variational graph auto-encodersKnowledge-Based Systems10.1016/j.knosys.2022.108689247:COnline publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108689

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents