Learning Style Identification Using Semi-Supervised Self-Taught Labeling
This addresses the need for adaptive learning environments in disrupted educational settings, but it is incremental as it applies existing semi-supervised techniques to a specific domain.
The paper tackles the problem of identifying students' learning styles in online education to enable personalized content delivery, achieving accuracies of 88.83% and 77.35% on two courses using a semi-supervised method.
Education is a dynamic field that must be adaptable to sudden changes and disruptions caused by events like pandemics, war, and natural disasters related to climate change. When these events occur, traditional classrooms with traditional or blended delivery can shift to fully online learning, which requires an efficient learning environment that meets students' needs. While learning management systems support teachers' productivity and creativity, they typically provide the same content to all learners in a course, ignoring their unique learning styles. To address this issue, we propose a semi-supervised machine learning approach that detects students' learning styles using a data mining technique. We use the commonly used Felder Silverman learning style model and demonstrate that our semi-supervised method can produce reliable classification models with few labeled data. We evaluate our approach on two different courses and achieve an accuracy of 88.83% and 77.35%, respectively. Our work shows that educational data mining and semi-supervised machine learning techniques can identify different learning styles and create a personalized learning environment.