Improving the Quality and Education Systems Through Integration’s Approach of Data Mining Clustering in E-Learning

Abstract

Educational Data Mining (EDM) is a discipline developed by focusing on improving independent and adaptive learning methods to find hidden education patterns. In this area, heterogeneous data is known to continue to develop in a big-data paradigm. Several specific data mining techniques are required to extract information with an adaptive value from the available educational data. Therefore, this study aims to present a grouping approach related to partitioning students into a different group or cluster based on the students’ behavior during lessons. Then, the architecture related to the e-learning system will be personalized to detect and provide suitable teaching methods and content according to each student's learning ability so that students can improve their quality and learning ability. The grouping methods that can be done in this educational data mining include K-Means, K-Medoids, Agglomerative Hierarchical Cluster Trees, Noise-Based Application Density-Based Spatial Clustering, and Fast Search and Density Peak Findings through Heat Diffusion (CFSFDP-HD) Shows the average compute time with different student count benchmarks: 600, 1200, 1800, 2400, 3000, 3600. Then, it has been found that the CFSFDP-HD method has strong results compared to other methods.