Educational Data Mining to Investigate the Impact of Students’ Online Learning Activities on Their Assessment Marks

Abstract

Discovering pedagogically useful information included in databases acquired from Web-based educational systems is made easier with the assistance of different data mining algorithms. Researchers are trying to continuously evolve virtual learning environments by collecting and analysing data related to all the aspects of online learning. The purpose of this study was to investigate whether student involvement in an online learning activities on virtual learning environment is a good predictor of final course grades and to determine whether the below suggested clustering method can achieve comparable accuracy to conventional clustering algorithms. This study aims to determine the effect of a variety of online learning activities on students' learning progress. . We focused more on applying this data mining technique for virtual learning environment the better insights of students. The findings indicate that engagement in online learning activities had the most significant influence on students' final grades of the course module. We demonstrate how K-Means clustering can be used to understand online learning activities in a virtual learning environment and how data mining techniques may be used to aid in the discovery of student-related and educator-related information included in databases derived from a virtual learning environment. We identified four clusters out of which we have focused more on Cluster 2 because the students falling into this category have scored the maximum marks and performed the most important and important online learning activities. These results may be utilised to assist educators in managing their classes, comprehending their students' learning, reflecting on their own teaching, and encouraging learner reflection and constructive feedback. This study begins with data collection, pre-processing, data mining methods application, and display of the findings. We have conducted a survey for the Web-based educational data mining tool in which we have included ten educators and ten students, tried to reach on constructive feedback about the Web based educational data mining tool by asking certain number of questions. The aggregative outcome for the survey of the Web-based educational data mining tool, educators seems to be a bit more unsatisfied as we reached an average score of 77.2 per cent. Teachers require some extra and better features in the web-based tool, while the students are quite more comfortable as the average score was 86.0 per cent. Students do not require too many changes to the tool. It is identified from the study that there is huge impact for the final assessment marks from the online learning activities the students engaged in.