Learning Styles Identification Model in a MOOC Learning Environment

Different online learners have different learning styles that are influenced by their prior knowledge and personalities, which necessitates the use of an online platform to identify these learning behaviors in order to enhance the course. Based on the Felder-Silverman model, we offer a novel learning style theory model suitable for MOOC education environments in this work. Then we extract high-dimensional features from the MOOCCube data set produced from China’s XuetangX platform. Furthermore, to identify online users’ learning styles, we apply a two-level hierarchical learning style classification model. First, a learning autonomy classification model is used to filter inactive learners by collecting the learner autonomy index from the data set. Then, to detect distinct learning styles, we construct a clustering-based behavior identification model using the Gaussian Mixture Model. Our hierarchical classification model demonstrates great capability and enables researchers to conduct analytical studies on the learning patterns of online learners.