GAT-LS: A Graph Attention Network for Learning Style Detection in Online Learning Environment

Wanying Suo

¹School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi, 710062, China

²School of Computer Science and Technology, Henan Institute of Science and Technology, Henan, 453003, China

³School of Information Science and Technology, Hainan Normal University, Haikou, 571158, China

*Corresponding author

In the context of the information age, the rapid growth and increasing diversity of learning resources underscore the urgency of personalized learning, while learning style is the most crucial factor to consider in personalized learning as it significantly influences students’ academic achievements and learning experiences. Traditional methods of assessing learning styles, such as completing questionnaires, have many drawbacks, including subjectivity and time costs. Therefore, in recent years, researchers have been exploring automatic methods to identify learning styles by analyzing students’ interactive behaviors. Motivated by these limitations, we propose a learning style detection method using a graph attention network (GAT), named GAT-LS. We originally constructed a bipartite graph between learners and learning materials, utilizing node features to represent the students’ behavior. Subsequently, we employ GAT to obtain hidden vectors for the graph nodes. These hidden vectors encapsulate both the overall graph information and the importance of neighboring nodes. We employ a multi-head attention network to process student nodes and combine a dropout mechanism with a single-layer attention network to process learning material nodes. Finally, we map the obtained hidden node features to the Felder-Silverman learning style model (FSLSM) and use K-means clustering to detect learning styles. The proposed method can be integrated into various types of educational systems or online learning platforms, providing a better educational experience and learning resource recommendations for both teachers and students. Experiments on the real-world dataset, KDD CUP 2015, demonstrated the superiority of our method. Our proposed approach achieved outstanding results with average values of 0.9647 accuracy, 0.9478 precision, 0.9171 recall, and 0.9346 F1 score.

Learning style, Graph learning, Graph attention network, Interactive learning environment, FSLSM

Wanying Suo, Bello Ahmad Muhammad, Zhichao Zhang, and Bo Liu (2024). GAT-LS: A Graph Attention Network for Learning Style Detection in Online Learning Environment. Journal of Networking and Network Applications, Volume 4, Issue 2, pp. 60–72. https://doi.org/10.33969/J-NaNA.2024.040202.

[1] M. Raleiras, A. H. Nabizadeh, and F. A. Costa, “Automatic learning styles prediction: A survey of the state-of-the-art (2006–2021),” Journal of Computers in Education, vol. 9, no. 4, pp. 587–679, 2022.

[2] B. A. Muhammad, B. Liu, H. K. Ahmad, M. Umar, and K. Qin, “Gnn-ls: A learning style prediction in online environments using graph neural networks,” Journal of Networking and Network Applications, vol. 2, no. 4, pp. 172–182, 2022.

[3] H. M. Truong, “Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities,” Computers in human behavior, vol. 55, pp. 1185–1193, 2016.

[4] J. Feldman, A. Monteserin, and A. Amandi, “Automatic detection of learning styles: state of the art,” Artificial Intelligence Review, vol. 44, no. 2, pp. 157–186, 2015.

[5] N. R. Ngatirin and Z. Zainol, “Automatic detection of learning styles: A decade review on data-driven approaches,” in Journal of Physics: Conference Series, vol. 1997, p. 012001, IOP Publishing, 2021.

[6] S. Wibirama, A. P. Sidhawara, G. L. Pritalia, and T. B. Adji, “A survey of learning style detection method using eye-tracking and machine learning in multimedia learning,” in 2020 International Symposium on Community-centric Systems (CcS), pp. 1–6, IEEE, 2020.

[7] S. Graf and P. Kinshuk, “An approach for detecting learning styles in learning management systems,” in Sixth IEEE International Conference on Advanced Learning Technologies (ICALT’06), pp. 161–163, IEEE, 2006.

[8] P. Q. Dung and A. M. Florea, “An approach for detecting learning styles in learning management systems based on learners’ behaviours,” in International Conference on Education and Management Innovation, vol. 30, pp. 171–177, 2012.

[9] M. Hasibuan, L. E. Nugroho, P. I. Santosa, and S. Kusumawardani, “A proposed model for detecting learning styles based on agent learning.,” International Journal of emerging technologies in learning, vol. 11, no. 10, 2016.

[10] S. V. Kolekar, R. M. Pai, and M. P. MM, “Rule based adaptive user interface for adaptive e-learning system,” Education and Information Technologies, vol. 24, pp. 613–641, 2019.

[11] O. El Aissaoui, Y. El Alami El Madani, L. Oughdir, and Y. El Allioui, “A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments,” Education and Information Technologies, vol. 24, pp. 1943–1959, 2019.

[12] B. Hmedna, A. El Mezouary, and O. Baz, “A predictive model for the identification of learning styles in mooc environments,” Cluster Computing, vol. 23, no. 2, pp. 1303–1328, 2020.

[13] F. A. Khan, A. Akbar, M. Altaf, S. A. K. Tanoli, and A. Ahmad, “Automatic student modelling for detection of learning styles and affective states in web based learning management systems,” IEEE Access, vol. 7, pp. 128242–128262, 2019.

[14] J. Yang, Z. X. Huang, Y. X. Gao, and H. T. Liu, “Dynamic learning style prediction method based on a pattern recognition technique,” IEEE Transactions on Learning Technologies, vol. 7, no. 2, pp. 165–177, 2014.

[15] S. Fatahi, H. Moradi, and E. Farmad, “Behavioral feature extraction to determine learning styles in e-learning environments.,” International Association for Development of the Information Society, 2015.

[16] R. M. Felder, L. K. Silverman, et al., “Learning and teaching styles in engineering education,” Engineering education, vol. 78, no. 7, pp. 674–681, 1988.

[17] Z. Othmane, A. DEROUICH, and A. TALBI, “A comparative study of the most influential learning styles used in adaptive educational environments,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 11, 2019.

[18] M. Hasibuan and R. A. Aziz, “Systematic literature review detection learning style,” in 2022 International Conference on Platform Technol-ogy and Service (PlatCon), pp. 29–33, IEEE, 2022.

[19] B. Xing, “A review of research on learning style,” Open Journal of Modern Linguistics, vol. 13, no. 2, pp. 263–275, 2023.

[20] S. Graf et al., Adaptivity in learning management systems focussing on learning styles. PhD thesis, Technische Universität Wien, 2007.

[21] J. Bernard, T.-W. Chang, E. Popescu, and S. Graf, “Learning style iden-tifier: Improving the precision of learning style identification through computational intelligence algorithms,” Expert Systems with Applica-tions, vol. 75, pp. 94–108, 2017.

[22] M. Prabhani, P. Liyanage, K. S. L. Gunawardena, and M. Hirakawa, “Detecting learning styles in learning management systems using data mining,” J. Inf. Process., vol. 24, pp. 740–749, 2016.

[23] H. A. Fasihuddin, G. Skinner, and R. I. Athauda, “Towards adaptive open learning environments: Evaluating the precision of identifying learning styles by tracking learners’ behaviours,” Education and Information Technologies, vol. 22, pp. 807–825, 2017.

[24] M. Jebbari, B. Cherradi, W. Moutaouakil, O. El Gannour, S. Hamida, and A. Raihani, “Prediction and classification of learning styles using machine learning approach,” in 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–7, IEEE, 2023.

[25] H. Y. Ayyoub and O. S. Al-Kadi, “Learning style identification using semi-supervised self-taught labeling,” IEEE Transactions on Learning Technologies, 2024.

[26] H.-H. Nguyen, K. Do Trung, L. N. Duc, L. D. Hoang, P. T. Ba, and V. A. Nguyen, “A model to create a personalized online course based on the student’s learning styles,” Education and Information Technologies, vol. 29, no. 1, pp. 571–593, 2024.

[27] H.-H. Nguyen, L. Nguyen Duc, K. Do Trung, L. Dang Hoang, T. Vu, T. V. Vu, and V. A. Nguyen, “Applying machine learning techniques to detect student’s learning styles,” in Proceedings of the 14th International Conference on Education Technology and Computers, pp. 456–462, 2022.

[28] J. Bernard, E. Popescu, and S. Graf, “Improving online education through automatic learning style identification using a multi-step archi-tecture with ant colony system and artificial neural networks,” Applied Soft Computing, vol. 131, p. 109779, 2022.

[29] R. Nazempour and H. Darabi, “Personalized learning in virtual learning environments using students’ behavior analysis,” Education Sciences, vol. 13, no. 5, p. 457, 2023.

[30] H. J. Cha, Y. S. Kim, S. H. Park, T. B. Yoon, Y. M. Jung, and J.-H. Lee, “Learning styles diagnosis based on user interface behaviors for the customization of learning interfaces in an intelligent tutoring system,” in Intelligent Tutoring Systems: 8th International Conference, ITS 2006, Jhongli, Taiwan, June 26-30, 2006. Proceedings 8, pp. 513–524, Springer, 2006.

[31] E. Özpolat and G. B. Akar, “Automatic detection of learning styles for an e-learning system,” Computers & Education, vol. 53, no. 2, pp. 355–367, 2009.

[32] P. García, A. Amandi, S. Schiaffino, and M. Campo, “Evaluating bayesian networks’ precision for detecting students’ learning styles,” Computers & Education, vol. 49, no. 3, pp. 794–808, 2007.

[33] N. Hidayat, R. Wardoyo, A. Sn, and H. D. Surjono, “Enhanced performance of the automatic learning style detection model using a combination of modified k-means algorithm and naive bayesian,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 3, pp. 638–648, 2020.

[34] Y. P. Valencia Usme, M. Normann, I. Sapsai, J. Abke, A. Madsen, and G. Weidl, “Learning style classification by using bayesian networks based on the index of learning style,” in Proceedings of the 5th European Conference on Software Engineering Education, pp. 73–82, 2023.

[35] I. Azzi, A. Jeghal, A. Radouane, A. Yahyaouy, and H. Tairi, “A robust classification to predict learning styles in adaptive e-learning systems,” Education and Information Technologies, vol. 25, pp. 437–448, 2020.

[36] F. Rasheed and A. Wahid, “Learning style detection in e-learning systems using machine learning techniques,” Expert Systems with Ap-plications, vol. 174, p. 114774, 2021.

[37] H. Y. Ayyoub and O. S. Al-Kadi, “Learning style identification using semisupervised self-taught labeling,” IEEE Transactions on Learning Technologies, vol. 17, pp. 1093–1106, 2024.

[38] M. Ndognkon Manga and M. Fouda Ndjodo, “An approach for non-deterministic and automatic detection of learning styles with deep belief net,” in Intelligent Computing: Proceedings of the 2021 Computing Conference, Volume 2, pp. 427–452, Springer, 2021.

[39] M. Gao, X. He, L. Chen, T. Liu, J. Zhang, and A. Zhou, “Learning vertex representations for bipartite networks,” IEEE transactions on knowledge and data engineering, vol. 34, no. 1, pp. 379–393, 2020.

[40] F. A. Dorça, L. V. Lima, M. A. Fernandes, and C. R. Lopes, “Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimen-tal analysis,” Expert Systems with Applications, vol. 40, no. 6, pp. 2092–2101, 2013.

[41] I. Karagiannis and M. Satratzemi, “An adaptive mechanism for moodle based on automatic detection of learning styles,” Education and Infor-mation Technologies, vol. 23, pp. 1331–1357, 2018.

[42] J. Bernard, E. Popescu, and S. Graf,“Improving online education through automatic learning style identifica-tion using a multi-step architecture with ant colony system and artificial neural networks,” Applied Soft Computing, vol. 131, p. 109779, 2022.

[43] Q. Ni, Y. Mi, Y. Wu, L. He, Y. Xu, and B. Zhang, “Design and implementation of the reliable learning style recognition mechanism based on fusion labels and ensemble classification,” IEEE Transactions on Learning Technologies, vol. 17, pp. 241–257, 2024.

[44] B. A. Muhammad, Z. Wu, and H. K. Ahmad, “A conceptual framework for detecting learning style in an online education using graph represen-tation learning,” in 2020 International Conference on Networking and Network Applications (NaNA), pp. 136–140, IEEE, 2020.

[45] B. A. Muhammad, C. Qi, Z. Wu, and H. K. Ahmad, “Grl-ls: A learning style detection in online education using graph representation learning,” Expert Systems with Applications, vol. 201, p. 117138, 2022.

[46] B. A. Muhammad, C. Qi, Z. Wu, and H. K. Ahmad, “An evolving learning style detection approach for online education using bipartite graph embedding,” Applied Soft Computing, vol. 152, p. 111230, 2024.

[47] B. A. Muhammad, W. Jianping, G. Gao, C. Zhao, Q. Li, X. Zuo, and Y. Lv, “A fuzzy c-means algorithm to detect learning styles in online learning environment,” Journal of Networking and Network Applications, vol. 4, no. 1, pp. 39–47, 2024.

[48] C. Troussas, A. Krouska, and M. Virvou, “A multilayer inference engine for individualized tutoring model: adapting learning material and its granularity,” Neural Computing and Applications, vol. 35, no. 1, pp. 61–75, 2023.

[49] W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” Advances in neural information processing systems, vol. 30, 2017.

[50] I. Chami, S. Abu-El-Haija, B. Perozzi, C. Ré, and K. Murphy, “Machine learning on graphs: A model and comprehensive taxonomy,” The Journal of Machine Learning Research, vol. 23, no. 1, pp. 3840–3903, 2022.

[51] P. Veliˇckovi´c, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Ben-gio, “Graph attention networks,” arXiv preprint arXiv:1710.10903, 2017. 

[52] Y. Jiang, H. Ma, Y. Liu, Z. Li, and L. Chang, “Enhancing social recom-mendation via two-level graph attentional networks,” Neurocomputing, vol. 449, pp. 71–84, 2021.

[53] C. Zhang, J. James, and Y. Liu, “Spatial-temporal graph attention networks: A deep learning approach for traffic forecasting,” IEEE Access, vol. 7, pp. 166246–166256, 2019.

[54] H. Linmei, T. Yang, C. Shi, H. Ji, and X. Li, “Heterogeneous graph attention networks for semi-supervised short text classification,” in Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp. 4821–4830, 2019. 

[55] Y. Rong, W. Huang, T. Xu, and J. Huang, “Dropedge: Towards deep graph convolutional networks on node classification,” arXiv preprint arXiv:1907.10903, 2019.

[56] S. Graf, Kinshuk, and T.-C. Liu, “Supporting teachers in identifying students’ learning styles in learning management systems: An automatic student modelling approach,” Journal of Educational Technology & Society, vol. 12, no. 4, pp. 3–14, 2009.

[57] J. Bernard, E. Popescu, and S. Graf, “Improving online education through automatic learning style identification using a multi-step ar-chitecture with ant colony system and artificial neural networks,” Appl. Soft Comput., vol. 131, p. 109779, 2022.