On the Design of a DSS for Academic Achievement Prediction | ||||
| Journal of the ACS Advances in Computer Science | ||||
| Article 4, Volume 8, Issue 1, 2014, Page 45-59 PDF (1.53 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asc.2014.158147 | ||||
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| Abstract | ||||
| This paper tries to examine the relationship between students’ overall academic performance (GPA), students’ grade of each subject of the first semester and their the high school grade, then comparing the obtained results to highlight which is more likely to be predicted from the high school grade, would it be the GPA or the grade of each subject by itself. This is done using the Decision Trees algorithm for predicting the academic performance of the first semester for the undergraduate engineering students at the Modern Academy for Engineering (MAE) by using the high school grade as the only input. The data-mining tools were able to achieve levels of accuracy for predicting student performance: Decision Trees score for the {pass, fail} set scored 72% for the “Mechanics” which was the least one while the highest score was for “Chemistry” with a score 89%, and as for the GPA grade the score was 80%. For {excellent, very good, good, pass, fail, very bad, absent} set, the score was much less for all of them and had a wide range of variance, it reached a minimum of 34% for “Physics” and a maximum of 62% for “English” while the GPA grade scored 42%. In this analysis, the Decision Tree was more accurate predicting at the {pass, fail} than at the {excellent, very good, good, pass, fail, very bad, absent} data sets. The results of these case studie give insight into techniques for accurately predicting student performance. | ||||
| Keywords | ||||
| predicting the academic performance; Decision Tree; admission system | ||||
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