Application of Data Mining to Predict Students Learning Behavior: A Case Study of Kebbi State Polytechnic Dakin-gari

Authors : Suleiman Sahabi; Anas Shehu; Shamsu Sani; Abubakar Sani

Volume/Issue : Volume 8 - 2023, Issue 3 - March

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This research was conducted in data mining. To our knowledge, no research covered seven programs of higher institutions for data mining purposes. This work used real dataset of students from seven department of Kebbi State Polytechnic Dakin-gari. Classification, association and clustering were used to discover hidden patterns in the dataset. WEKA workbench was used to run the experiment and evaluate the results. Classification was done with optimum accuracy where four classes were identified i.e. weaker, weak, good and better students. After association rule was done, the authors found out strong correlation between (ATT and CA) with (GPA), (EF and CA) with (GPA) and (EF and CA) with (CO). And that affect the students in their GPA results. Same test data was used for hierarchical clustering where two clusters were returned and 162 tuples was distributed between the clusters in 81% and 19% fashion, Conclusively, the authors strongly feel that the management of Kebbi State Polytechnic Dakin-gari with a matter of urgency need to tackle these discovered pattern to minimize rate of failure and drop out within the students.

Keywords : WEKA, Data Mining Tool, Dakin-Gari, Clustering, Association, Classification, Metric.


Paper Submission Last Date
31 - May - 2023

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