A Fuzzy C-means Algorithm to Detect Learning Styles in Online Learning Environment

Bello Ahmad Muhammad, Wang Jianping

¹ School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, Henan, China

*Corresponding author

The ability to detect learners’ learning styles based on their learning behaviors is of utmost importance for online educational systems, as it greatly enhances student engagement, motivation, and overall learning outcomes. Knowing the learning preferences of learners may significantly aid in creating personalized learning recommendations and empower learners to identify their own learning styles. However, learners exhibit diverse behaviors in an online setting, which poses significant difficulties in detecting their learning style. This paper proposes a novel approach for detecting learning styles using graph representation learning techniques and machine learning algorithms. While our approach is not reliant on a particular learning style model, our approach may be divided into two distinct parts. Initially, we represented the behavior of learners as a bipartite graph and then transformed this graph into a lower-dimensional representation using the graph embedding approach. This lower-dimensional representation was then utilized for machine learning tasks. Furthermore, we categorize the encoded learner’s sequence using a clustering technique based on the selected learning style model. Our methodology uses the Felder-Silverman model as the learning style model and the Fuzzy C-means algorithm as a clustering technique. Our approach was evaluated using the 2015 KDD Cup dataset through a series of comprehensive experiments to showcase its effectiveness. The findings demonstrate that our approach surpasses the previous approach, achieving an average precision of 0.8737 and an accuracy of 0.9182.

Learning style, online learning environment, Fuzzy C-means algorithm, Graph representation learning, learning behavior

Bello Ahmad Muhammad, Wang Jianping, Guohong Gao, Chenping Zhao, Qian Li, Xiangang Zuo, and Yingying Lv (2024). A Fuzzy C-means Algorithm to Detect Learning Styles in Online Learning Environment. Journal of Networking and Network Applications, Volume 4, Issue 1, pp. 39–47. https://doi.org/10.33969/J-NaNA.2024.040105.

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