A Concept Framework of Using Education Game With Artificial Neural Network Techniques to Identify Learning Styles

Abstract

Although learning style is an important issue in STEM (Science, Technology, Engineering, and Mathematics), most of previous studies adopted questionnaire instrument to identify learning style. Therefore, this study proposes a concept framework of using artificial neural networks to identify students' learning styles based on the learning portfolio data in our designed education balance game with the Felder-Silverman learning style model (FSLSM). An education balance game is designed to train student's physical balance knowledge and collect their learning portfolio data. These portfolio data is input variables in support vector machine to identify students' learning style.