Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

Yahia Baashar,Abdulrazzaq Qasem Ali,Yazan A Alsariera,Ammar Ahmed Alkahtani,Gamal Alkawsi,Sieh Kiong Tiong,Abdulsalam Mustafa,Wahidah Hashim

doi:10.3390/app12031289

Yahia Baashar, Abdulrazzaq Qasem Ali + Show 6 more

Open Access

https://doi.org/10.3390/app12031289

Copy DOI

Abstract

Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

Highlights

Introduction published maps and institutional affilEducation institutions at different educational levels are established to provide highquality education capable of changing people’s levels of awareness, knowledge, and mental capacity
This review found a diverse range of types of Artificial Neural Networks (ANNs) analysis methods and a strong emphasis on higher education
The systematic review of the literature methodology adopted in this paper leads to specific observations and conclusion points concerning using Artificial Neural Networks in predicting student achievements and performance

Summary

Introduction

Education institutions at different educational levels are established to provide highquality education capable of changing people’s levels of awareness, knowledge, and mental capacity. Teachers and educators are always looking to enhance student achievement and monitor their performance to determine the efficiency of the teaching process. The new advancement of technology enables educators to use analytics and data mining methodologies to search large datasets for patterns that reflect their students’ behavior and learning [1]. Student performance is critical to the learning process, it is a complex phenomenon influenced by various factors such as the teaching environment and individual study habits. Various studies [2,3] have used a variety of indicators/variables to develop models that can predict students’ academic performance at different levels of education, including high school and university education in various disciplines, in particular engineering and medicine. Various studies [2,3] have used a variety of indicators/variables to develop models that can predict students’ academic performance at different levels of education, including high school and university education in various disciplines, in particular engineering and medicine. iations.

Results

Discussion

Conclusion