Educational Data Mining for Monitoring and Improving Academic Performance at University Levels

This study applied Educational Data Mining on 712 sample of logs extracted from Moodle Learning Management System (LMS) at an African University in order to measure students and staff patterns of use of the LMS resources and hence determine if the quantity of participation measured in the amount of time spent on the use of LMS resources improved academic performance of students. Data collected from Moodle LMS was preprocessed and analyzed using machine learning algorithms of clustering, classification and visualization from WEKA system tools. The dataset consisted of Course tools (Quiz, Assignment, Chat, Forum, URL, Folder and Files), Lecturer and Student usage of the tools. Furthermore, SPSS was used to obtain a matrix for coefficients of correlations for course tools, tests and final grade. Correlation analysis was done to verify if students use of course tools had impact on student’s academic performance. Findings indicated the pattern of usage for course1 as Quiz (38358), System (17910), Forum (8663), File (8566), Assignment (1235), Folder (514, File Submission (172), and Chat (37); Course2 as System (11920), Quiz (8208), Forum (4476), File (4394), Assignment (257), Chat (247), URL (125), and File Submission (38); Course3 as System (2622),File (1022), Folder (570), Forum (258), and URL (2). Overall, evaluating the correlation between the use of LMS resources and students’ performance, findings indicated there is significant relationship between the use of LMS resources and students’ academic performance at 0.01 level of significant. The findings are useful for strategic academic planning purpose with LMS data at the university.

@article{Okike2020,
  title     = {Educational Data Mining for Monitoring and Improving Academic Performance at University Levels},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {11},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Ezekiel U Okike and Merapelo Mogorosi},
  doi       = {10.14569/IJACSA.2020.0111171},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111171}
}