Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review

Abstract

Machine learning is emerging nowadays as an important tool for decision support in many areas of research. In the field of education, both educational organizations and students are the target beneficiaries. It facilitates the educational sector in predicting the student’s outcome at the end of their course and for the students in deciding to choose a suitable course for them based on their performances in previous exams and other behavioral features. In this study, a systematic literature review is performed to extract the algorithms and the features that have been used in the prediction studies. Based on the search criteria, 2700 articles were initially considered. Using specified inclusion and exclusion criteria, quality scores were provided, and up to 56 articles were filtered for further analysis. The utmost care was taken in studying the features utilized, database used, algorithms implemented, and the future directions as recommended by researchers. The features were classified as demographic, academic, and behavioral features, and finally, only 34 articles with these features were finalized, whose details of study are provided. Based on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability to predict the students’ performance based on specified features as categorized and can be used by students as well as academic institutions. A specific machine learning model identification for the purpose of student academic performance prediction would not be feasible, since each paper taken for review involves different datasets and does not include benchmark datasets. However, the application of the machine learning techniques in educational mining is still limited, and a greater number of studies should be carried out in order to obtain well-formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.

Investigating Students’ Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach

“…Four metrics—accuracy, recall, precision, and the F1 score [ 25 , 43 ]—are used to evaluate the performance of a model. Their values depend on the classification confusion matrix, listing the following outcomes: True positive (TP): the number of cases correctly predicted as positive, False positive (FP): the number of cases incorrectly predicted as positive, True negative (TN): the number of cases correctly predicted as negative and False negative (FN): the number of cases incorrectly predicted as negative.…”

“…Most existing EDM research has applied clustering, classification, association rule mining, and text mining into educational data [ 23 , 24 , 25 ]. The EDM community uses four major approaches: prediction models, structure discovery, relationship mining and discovery with models [ 26 ].…”

A novel adaptive control design for exponential stabilization of memristor‐based CVNNs with time‐varying delays using matrix measures

“…1 and time varying delay τ j t ð Þ ¼ e t e t þ1 . Figure 1A,B shows the phase plot of model (30). Figure 1A shows the phase plot of the real part of the CVMNNs (30).…”

“…Figure 1A,B shows the phase plot of model (30). Figure 1A shows the phase plot of the real part of the CVMNNs (30). Figure 1B shows the phase plot of the imaginary part of the CVMNNs (30) with the above constant values.…”

Predicting and Comparing Students’ Online and Offline Academic Performance Using Machine Learning Algorithms

“…In some chosen publications, decision tree (DT) and ensemble learning models [ 23 ] have been used. Neural networks (NNs) or transfer learning with the appropriate layers can be used to make an objective choice about the model that is best for the data obtained.…”