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Inferring concept prerequisite relations from online educational resources

AUTHORs:

Meghana Madhyastha,

Sheril Lawrence,

Vaibhav RajanAuthors Info & Claims

AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

Article No.: 1183, Pages 9589 - 9594

https://doi.org/10.1609/aaai.v33i01.33019589

Published: 27 January 2019 Publication History

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Abstract

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.

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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
Sun JHe YXu YSun JSun GSerra ESpezzano F(2024)A Learning-path based Supervised Method for Concept Prerequisite Relations Extraction in Educational DataProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679597(2168-2177)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679597
Layoun JZouaq ADesmarais MHong JPark J(2024)Contextual Embeddings and Graph Convolutional Networks for Concept Prerequisite LearningProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3636062(81-90)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3636062
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Inferring concept prerequisite relations from online educational resources

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AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

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AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

January 2019

10088 pages

ISBN:978-1-57735-809-1

Copyright © 2019 Association for the Advancement of Artificial Intelligence.

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Association for the Advancement of Artificial Intelligence

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AAAI Press

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Published: 27 January 2019

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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
Sun JHe YXu YSun JSun GSerra ESpezzano F(2024)A Learning-path based Supervised Method for Concept Prerequisite Relations Extraction in Educational DataProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679597(2168-2177)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679597
Layoun JZouaq ADesmarais MHong JPark J(2024)Contextual Embeddings and Graph Convolutional Networks for Concept Prerequisite LearningProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3636062(81-90)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3636062
Wang ZShao YPeng BLi BLi YWang QLi N(2024)Multi-view Transformer-Based Network for Prerequisite Learning in Concept GraphsThe Semantic Web – ISWC 202410.1007/978-3-031-77844-5_4(67-86)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-77844-5_4
Xiao KLi KZhang YChen XLou Y(2023)WikiCPRL: A Weakly Supervised Approach for Wikipedia Concept Prerequisite Relation LearningWeb and Big Data10.1007/978-981-97-2390-4_13(177-192)Online publication date: 6-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-97-2390-4_13
Xiao KBai YZhang YOria VSapino MSatoh SKerhervé BCheng WIde ISingh V(2022)Extracting Precedence Relations between Video Lectures in MOOCsProceedings of the 2022 International Conference on Multimedia Retrieval10.1145/3512527.3531414(608-614)Online publication date: 27-Jun-2022
https://dl.acm.org/doi/10.1145/3512527.3531414
Smith CUrgo KArguello JCapra RElsweiler DKruschwitz ULudwig B(2022)Learner, Assignment, and Domain: Contextualizing Search for ComprehensionProceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505819(191-201)Online publication date: 14-Mar-2022
https://dl.acm.org/doi/10.1145/3498366.3505819
Zhang JLin NZhang XSong WYang XPeng ZSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Learning Concept Prerequisite Relations from Educational Data via Multi-Head Attention Variational Graph Auto-EncodersProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498434(1377-1385)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498434
Tang THou JGuo TBai XTian XNoori Hoshyar A(2021)KIDNet: A Knowledge-Aware Neural Network Model for Academic Performance PredictionIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3498851.3498927(37-44)Online publication date: 14-Dec-2021
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Zhang YAn RCui JShang XScheffel MDowell NJoksimovic SSiemens G(2021)Undergraduate Grade Prediction in Chinese Higher Education Using Convolutional Neural NetworksLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448184(462-468)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.1145/3448139.3448184

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