Predict student learning styles and suitable assessment methods using click stream

Abstract

Adaptive learning, which aims to give each learner engaging, effective learning experiences, is one method of offering modified education. Adaptive learning seeks to consider the student's unique characteristics by personalizing the learning course materials and evaluation procedures. To determine the student's preferred learning strategies, we first ascertain their attributes utilizing VAK learning styles. In this study, we developed an integrated model to classify learners based on their learning activity clicks by combining machine learning algorithms like K-Nearest Neighbor (KNN), random forest (RF), and support vector machine (SVM) and Logistic regression (LR) with semantic association, which is used to help us map learning activity with VAK learning style. This enables us to classify learners, determine their preferred methods of learning, and offer the most suitable; as a result, we were able to group pupils according to their learning styles and provide the best evaluation technique or strategies. To assess the effectiveness of the suggested model, several tests were executed on the actual dataset (Open University Learning Analytics Dataset, or OULAD). According to studies, using a Random Forest algorithm, the suggested model can predict which evaluation strategy or strategies will be most effective for each student and can classify individuals with the highest degree of accuracy—98%.