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Title |
Predicting Students’ Academic Performance Using Na?ve Bayes |
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Author |
Abdullah Baz, Fatima Alshareef, Ebtihal Alshareef, Hosam Alhakami, Tahani Alsubait |
| Citation |
Vol. 20 No. 4 pp. 182-190 |
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Abstract |
Nowadays, universities and many educational institutions have a critical responsibility towards society, shaping multiple social factors. Therefore, it is very important to predict the educational output of these institutions at early stages. It is very challenging to predict students' academic performance because of the huge bulks of data stored in the environments of educational databases. Students' performance can be predicted with the help of various available techniques. Data Mining is the most prevalent family of techniques to predict students’ performance and is extensively used in the educational sector, referred to as Educational Data Mining. In this paper, a dataset is collected from Umm Al-Qura University database. This dataset consists of 138 records of students who graduated from College of Computer and Information Systems in the year 2019, associated with 13 attributes including student ID, gender, eight courses’ grades, GPA of both first and second semester in the first’s year and the final GPA. The classification algorithm called Na?ve Bayes is employed on the dataset by using the WEKA tool. Results achieved show that Na?ve Bayes can be used for predicting students’ academic performance at early stages in the first year with an accuracy of 72.46%. |
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Keywords |
Educational Data Mining Performance Prediction Na?ve Bayes |
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