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Prediction of Students’ Cumulative Grade Point Averages (CGPAs) at Graduation: A Case Study
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 174 - Number 24 |
| Year of Publication: 2021 |
| Authors: Suleiman Khalifa Arafa Ibrahim, Mahmoud Ali Ahmed |
10.5120/ijca2021921149
|
Suleiman Khalifa Arafa Ibrahim, Mahmoud Ali Ahmed . Prediction of Students’ Cumulative Grade Point Averages (CGPAs) at Graduation: A Case Study. International Journal of Computer Applications. 174, 24 ( Mar 2021), 35-44. DOI=10.5120/ijca2021921149
Abstract
Predicting student’s performance achievement based on their academic grades continues to be one of the most popular applications of educational data mining and, therefore, it has become a valuable source of knowledge that has been used for different purposes in particular universities. This paper aims to implement the prediction method J48 to predict the students final CGPAs at graduation based on their academic data. For that matter, two different scenarios were investigated in this study. The students’ GPAs from the first 3 years were used for prediction in the first scenario, whereas the students’ GPAs from the first 2 years scores were used in the second scenario. In this study, students academic data for those who did their graduation from the department of Information Technology during the period [2007-2015] , at Comboni College of Science and Technology, SUDAN. As the results indicate, the prediction J48 method performed reasonably well in predicting the student GPA at graduation in both scenarios. According to the 10-fold cross validation test, J48 algorithm produced the accurate prediction result of 83.3333 % for the first scenario. The experiment was repeated for the second scenario. J48 algorithm again produced the accurate prediction results with 81.0345 % was obtained with 10- fold cross validation test. In conclusion, the prediction algorithm J48 in data mining able to accurately predict student CGPA at graduation well in advance, which can identify students needing extra help to improve their academic performance. Moreover, in this study, the Apriori algorithm was used to extract hidden patterns from the graduates’ data. Finally, based on the achieved results, this study could offer helpful feedback and recommendations to the department planners to take corrective measures to assist weak students and in turn, increase their chances of graduating with better grade.
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