Detecting learning style using hybrid model

Abstract

Previous research believed that identification of learning style can increase learner's motivation and understanding in the online learning process. The process of identifying learning styles can employ conventional and automatic methods. Conventional method uses questionnaires to detect learning styles. The use of questionnaires faces obstacle in which the detection results are less accurate. As to automatic method, its detection results are obtained from learner's interaction with a system. Some learners interact through Forum, Outline, Content, Exercise, and Example (FOCEE). However, automated detection of learning styles faces obstacle in which it is compared with the results of the foregoing questionnaires and Felder Silverman Learning Style Model (FSLSM) learning style, which is more as an approach to learning process. The paper proposes a VARK learning styles (Visual, Audio, Read and Kinesthetic) detection as an approach to learning materials. The model proposes a combination of literature-based detection and automatic detection, known as hybrid model. This detection model is expected to be able to detect learning styles more accurately.