FEATURE SELECTION ALGORITHM FOR PREDICTING STUDENTS’ ACADEMIC
DOI:
https://doie.org/10.5281/qa0kmc66Keywords:
Student performance, Educational Data Mining; Learning Analytics model; FPSO; SVM; KNN; Navie Bayes,,Abstract
Predicting the performance of pupils is necessary in order to evaluate whether or not
there is room for improvement. Evaluations should be done on a regular basis since they not
only assist students enhance their performance but also shed light on areas in which they need
improvement. Due to the fact that a single institution might have thousands of students, the
assessment procedure requires a significant amount of human labour to be completed. In this
article, a comparison was made between two different automated approaches to predicting the
students' performance using machine learning. Because educational databases include such a
vast amount of information, it is becoming more difficult to accurately forecast the
performance of pupils. There are primarily two explanations for why this is taking place. To
begin, the research that has been done on the many current prediction approaches is not nearly
enough to determine which techniques are most suited for forecasting the performance of
students. The second reason is that there haven't been enough studies done to determine the
elements that influence students' performance in various classes. As a result, in order to raise
student accomplishment levels, a comparative research on forecasting student performance via
the use of machine learning technologies has been conducted. The primary purpose of this
study is to determine which machine learning methods have shown to be the most accurate
when used to forecast the performance of pupils. This study also focuses on how the prediction
algorithm may be used to determine which characteristics of a student's data are the most
relevant to concentrate on.
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