Exploring the Effectiveness of Student Behavior in Prerequisite Relation Discovery for Concepts
Authors: 
Jifan Yu, Hanming Li, Gan Luo, Yankai Lin, Peng Li, Jianjun Xu, Lei Hou, Bin XuAuthors Info & Claims
Pages 359 - 374
Abstract
What knowledge should a student grasp before beginning a new MOOC course? To answer this question, it is essential to automatically discover prerequisite relations among course concepts. Although researchers have devoted intensive efforts to detecting such relations by analyzing various types of information, there are few explorations of utilizing student behaviors in this task. In this paper, we investigate the effectiveness of student behaviors in prerequisite relation discovery. Specifically, we first construct a novel dataset to support the study, and then formally define four typical student behavior patterns based on analysis of the dataset. Moreover, We explore how this behavior information can be utilized to enhance existing methods, including feature-based and graph-based, via extensive experiments. Experimental results demonstrate that proper modeling of student behaviors can significantly improve the performance of the methods on this task. We hope our study could call for more attention and efforts to explore student behavior for prerequisite relation discovery.
References
[1]
Agrawal, R., Golshan, B., Papalexakis, E.: Datadriven synthesis of study plans. Data Insights Laboratories (2015)
[2]
Alzetta, C., et al.: Prerequisite or Not Prerequisite? That’s the Problem! An NLP-Based Approach for Concept Prerequisites Learning (2019)
[3]
Ausubel: Educational Psychology: A Cognitive View (1968)
[4]
Bergan JR and Jeska P An examination of prerequisite relations, positive transfer among learning tasks, and variations in instruction for a seriation hierarchy Contemp. Educ. Psychol. 1980 5 203-215
[5]
Brunskill, E.: Estimating Prerequisite Structure from Noisy Data (2011)
[6]
Gagne RM and Briggs LJ Principles of Instructional Design 1974 Austin Holt, Rinehart & Winston
[7]
Gordon, J., Zhu, L., Galstyan, A., Natarajan, P., Burns, G.: Modeling concept dependencies in a scientific corpus. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 866–875 (2016)
[8]
Huang, X., Yang, K., Lawrence, V.B.: An efficient data mining approach to concept map generation for adaptive learning. In: Industrial Conference on Data Mining (2015)
[9]
Jia, C., Shen, Y., Tang, Y., Sun, L., Lu, W.: Heterogeneous graph neural networks for concept prerequisite relation learning in educational data. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2036–2047 (2021)
[10]
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)
[11]
Labutov, I., Huang, Y., Brusilovsky, P., He, D.: Semi-supervised techniques for mining learning outcomes and prerequisites. In: SIGKDD. ACM (2017)
[12]
Li, I., Fabbri, A.R., Tung, R.R., Radev, D.R.: What should i learn first: introducing lecturebank for NLP education and prerequisite chain learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6674–6681 (2019)
[13]
Li, I., Fabbri, A.R., Hingmire, S., Radev, D.: R-VGAE: relational-variational graph autoencoder for unsupervised prerequisite chain learning. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1147–1157 (2020)
[14]
Liang, C., Wu, Z., Huang, W., Giles, C.L.: Measuring prerequisite relations among concepts. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1668–1674 (2015)
[15]
Liang, C., Ye, J., Wang, S., Pursel, B., Giles, C.L.: Investigating active learning for concept prerequisite learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
[16]
Liang, C., Ye, J., Wu, Z., Pursel, B., Giles, C.L.: Recovering concept prerequisite relations from university course dependencies. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
[17]
Pan, L., Li, C., Li, J., Tang, J.: Prerequisite relation learning for concepts in MOOCs. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 1447–1456 (2017)
[18]
Pan, L., Wang, X., Li, C., Li, J., Tang, J.: Course concept extraction in MOOCs via embedding-based graph propagation. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing, vol. 1: Long Papers, pp. 875–884 (2017)
[19]
Parkay FW and Hass G Curriculum Planning: A Contemporary Approach 1999 Boston Allyn & Bacon Incorporated
[20]
Romero C and Ventura S Educational data mining: a survey from 1995 to 2005 Expert Syst. Appl. 2007 33 135-146
[21]
Roy, S., Madhyastha, M., Lawrence, S., Rajan, V.: Inferring concept prerequisite relations from online educational resources. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9589–9594 (2019)
[22]
Sayyadiharikandeh, M., Gordon, J., Ambite, J.L., Lerman, K.: Finding prerequisite relations using the wikipedia clickstream. In: Companion Proceedings of the 2019 World Wide Web Conference, pp. 1240–1247 (2019)
[23]
Scheines, R., Silver, E., Goldin, I.M.: Discovering prerequisite relationships among knowledge components. In: EDM, pp. 355–356 (2014)
[24]
Talukdar, P., Cohen, W.: Crowdsourced comprehension: predicting prerequisite structure in wikipedia. In: Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pp. 307–315 (2012)
[25]
Vuong, A., Nixon, T., Towle, B.: A method for finding prerequisites within a curriculum. In: EDM (2011)
[26]
Yu, J., et al.: Course concept expansion in MOOCs with external knowledge and interactive game. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4292–4302 (2019)
[27]
Zhu, Y., Zamani, H.: Predicting prerequisite relations for unseen concepts. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 8542–8548 (2022)
Recommendations
Mining Students' Learning Behavior in Moodle System
In the last few years, Educational Data Mining has become an interesting area exploited to discover and extract hidden knowledge of students from educational environment data. During the establishment of this work an attempt was made to manage the ...
Multi-Task Modeling of Student Knowledge and Behavior
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge ManagementKnowledge Tracing (KT) and Behavior Modeling (BM) are essential mining and discovery problems in education. KT models student knowledge based on prior performance with learning materials, while BM focuses on patterns such as student preferences, ...
Quality Evaluation for Document Relation Discovery Using Citation Information
Assessment of discovered patterns is an important issue in the field of knowledge discovery. This paper presents an evaluation method that utilizes citation (reference) information to assess the quality of discovered document relations. With the concept ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Oct 2023
538 pages
ISBN:978-981-97-2420-8
DOI:10.1007/978-981-97-2421-5
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 12 May 2024
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Dec 2024