Student Performance Prediction Model based on Supervised Machine Learning Algorithms

Abstract

Higher education institutions aim to forecast student success which is an important research subject. Forecasting student success can enable teachers to prevent students from dropping out before final examinations, identify those who need additional help and boost institution ranking and prestige. Machine learning techniques in educational data mining aim to develop a model for discovering meaningful hidden patterns and exploring useful information from educational settings. The key traditional characteristics of students (demographic, academic background and behavioural features) are the main essential factors that can represent the training dataset for supervised machine learning algorithms. In this study, we compared the performances of several supervised machine learning algorithms, such as Decision Tree, Naïve Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbour, Sequential Minimal Optimisation and Neural Network. We trained a model by using datasets provided by courses in the bachelor study programmes of the College of Computer Science and Information Technology, University of Basra, for academic years 2017–2018 and 2018–2019 to predict student performance on final examinations. Results indicated that logistic regression classifier is the most accurate in predicting the exact final grades of students (68.7% for passed and 88.8% for failed).

A Literature Review on Intelligent Services Applied to Distance Learning

“…Angeline et al [53] SCOPUS Discriminant analysis to measure student performance. Hashim et al [54] SCOPUS Student performance prediction model based on supervised machine learning algorithms (decision tree, Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, and minimal and neural sequential optimization Network). Maâloul and Bahou [55] SCOPUS…”

“…Hashim et al [54] compared the performance of various supervised machine learning algorithms to predict students' academic success and their performance in higher education. The authors used a set of data provided by the courses in the bachelor's degree programs of the Faculty of Informatics and Information Technology at the University of Basra, in the 2017-2018 and 2018-2019 academic years, to predict student performance on final exams.…”

A Literature Review on Intelligent Services Applied to Distance Learning

“…Hashim et al [49] Student performance prediction model based on supervised machine learning algorithms (decision tree, Naïve Bayes, logistic regression, support vector machine, nearest neighbor K, minimal and neural sequential optimization Network).…”

“…Hashim et al [49] compared the performance of various supervised machine learning algorithms to predict students' academic success and their performance in higher education. The authors used a set of data provided by the courses in the Bachelor's Degree Programs of the Faculty of Informatics and Information Technology at the University of Basra, in the 2017-2018 and 2018-2019 academic years, to predict student performance on final exams.…”

Implementing data-driven decision support system based on independent educational data mart

“…Educational staff members always need new technologies that support their decisions; these members seek different tools and applications to support their decisions on the basis of different algorithms and techniques, such as machine learning and data mining algorithms [18]- [25], mobile learning [26], DSSs [27] and web-based tools [28]. The decisions made on the basis of previous studies and solid analytical results are more considerable and reasonable than before.…”