Educational Technology Based on Data Mining: Mining Student Behavior Patterns to Optimize Teaching Strategies

Data mining (DM) approaches have been applied in the field of Educational Data Mining (EDM) to obtain the important insights about student preferences and behaviors. By using a variety of strategies to increase the effectiveness of e-learning environments, the design aims to enhance learning processes by forecasting the traits of students. Academic data can be used to study trends in student behavior using a selection of DM, such as grouping, classification, and forecasting. This study presents a novel approach to learning behaviors prediction in the Chaotic-Tuned Shuffled Frog Leaping Optimized Random Forest (CSFLO-RF). The CSFLO technique, which accelerates the global convergence of the traditional SFLO technique, improves the accuracy of RF categorization in the suggested methodology. The K-Means Clustering (KMC) methodology uses the student behavior patterns recorded in the log record to group learners into specific categories based on their e-learning system usage. The generated clusters were allocated multiple learning styles based on the framework for learning styles. Following that, the specified behaviors were employed as input for the proposed CSFLO-RF classification to evaluate the degree to predict the students’ learning behavior. According to the results of the suggested technique, which is implemented to utilize the Weka platform, the CSFLO-RF approach yielded more successful outcomes than other existing strategies.