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.
Highlights
In recent centuries, the academic performances of students have been appraised on the basis of memory-related tests or regular examinations and by comparing their performances to identify the factors for predicting their academic excellence
This paper predominantly focuses on Machine learning (ML) and AI
Based on the questions each article was able to answer and the data the data that could be obtained for further quality score assessment of the article, the thatsummary could beofobtained for up further quality score assessment of the article, the summary of articles taken for study is tabulated
Summary
The academic performances of students have been appraised on the basis of memory-related tests or regular examinations and by comparing their performances to identify the factors for predicting their academic excellence. Academicians and administrative personnel use data to predict a student’s performance during the time of admission, predict the job scope for a student at the time completion or the dropout based on the aggregate numbers from the entire set of students, or gauge a particular student’s success or failure rate in the subsequent grades. These have even led to recommendation systems for the students to select their area of expertise. These recommendation systems started its implementation from higher secondary schools [1], predicting the retention of students [2], family tutoring
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