LEARNING STYLE DETECTION USING K-MEANS CLUSTERING - Sani Ibrahim, Mubaraka (FUDMA JOURNAL OF SCIENCES, 2020)
Learning theorist established the fact that learners are characterized according to their distinct learning styles. Investigating learners' learning style is important in the educational system in order to provide adaptivity and improve learning experience. Past researches have proposed various approaches to detect learning styles. Among unsupervised learning methods, the K-means clustering has emerged as a widely used method to predict patterns in data because of its simplicity. This paper evaluates the performance of K-means clustering in automatically detecting learners’ learning style in an online learning environment. The experimental results prove differences in learning thus characterizing learners based on learning style.