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The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the
performance of students. Student performance prediction is not only important for the student but also helpful
for academic organization to detect the causes of success and failures of students. Furthermore, the features
selected through the students’ performance prediction models helps in developing action plans for academic
welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance
prediction model, where every feature is very important, as a neglection of any important feature can cause the
wrong development of academic action plans. Moreover, the feature selection is a very important step in the
development of student performance prediction models. There are different types of feature selection algorithms.
In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step
on the way to identifying the factors affecting the academic performance of the students. In this paper
performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected
well on a student dataset with greater no of features.
Maryam Zaffar, Manzoor Ahmad Hashmani, K.S. Savita, Syed Sajjad Hussain Rizvi, Mubashar Rehman. (2020) Role of FCBF Feature Selection in Educational Data Mining , Mehran University Research Journal of Engineering & Technology, Volume 39, Issue 4.
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