Predicting Students Academic Performance Using Machine Learning Techniques

EasyChair Preprint 9194

Abstract

Educational institutes are concerned with determining the outputs of their educational programs, which are the knowledge and skills that their students acquire during their studies, and generally determine the educational level provided by these institutes. So, it was important to work on predicting academic performance by looking at the data and activities on student performance, attitudes, and interactions at school or university, then trying to predict whether a student would get a high, intermediate, or low score.  Furthermore, it involves the need to achieve an accurate prediction of student’s academic performance in the future based on their present behavior and performances is unquestionable. The use of a machine learning (ML) techniques is regarded as the most suitable approach for achieving this objective. Predicting student performance from present academic data is one of the most useful applications at educational organizations in taking early action of improving learning outcome, thus is a valuable and good source of information as it can be used to improve student performance at the start of learning process, having a high prediction accuracy of their performance. The paper attempts to identify best machine learning technique in predicting student academic performance and demonstrate students’ levels, through train several models for predicting whether a given student will have a high, medium, or low grades based on academic and behavior information. Therefore, it is crucial to evaluate the accuracy, precision and recall of machine learning models to determine which one best predicts students' performance. The study comes to the conclusion that it would conclude that it would be imperative to use various be imperative to use a variety of machine learning techniques to effectively forecast student performance. It's critical to appropriately mattress machine learning models according to how accurate they can anticipate students' performance.

Keyphrases: Machine Learning Techniques, Student Performance Prediction., academic performance

@booklet{EasyChair:9194,
  author    = {Amal Shaker and Abdelmoniem Helmy},
  title     = {Predicting Students Academic Performance Using Machine Learning Techniques},
  howpublished = {EasyChair Preprint 9194},
  year      = {EasyChair, 2022}}