Using Artificial Intelligence to Predict Students’ Academic Performance in Blended Learning

Nawaf N. Hamadneh (), Samer Atawneh, Waqar A. Khan, Khaled A. Almejalli and Adeeb Alhomoud
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Nawaf N. Hamadneh: College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi Arabia
Samer Atawneh: College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
Waqar A. Khan: College of Sciences & Human Studies, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi Arabia
Khaled A. Almejalli: College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
Adeeb Alhomoud: College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi Arabia

Sustainability, 2022, vol. 14, issue 18, 1-13

Abstract: University electronic learning (e-learning) has witnessed phenomenal growth, especially in 2020, due to the COVID-19 pandemic. This type of education is significant because it ensures that all students receive the required learning. The statistical evaluations are limited in providing good predictions of the university’s e-learning quality. That is forcing many universities to go to online and blended learning environments. This paper presents an approach of statistical analysis to identify the most common factors that affect the students’ performance and then use artificial neural networks (ANNs) to predict students’ performance within the blended learning environment of Saudi Electronic University (SEU). Accordingly, this dissertation generated a dataset from SEU’s Blackboard learning management system. The student’s performance can be tested using a set of factors: the studying (face-to-face or virtual), percentage of attending live lectures, midterm exam scores, and percentage of solved assessments. The results showed that the four factors are responsible for academic performance. After that, we proposed a new ANN model to predict the students’ performance depending on the four factors. Firefly Algorithm (FFA) was used for training the ANNs. The proposed model’s performance will be evaluated through different statistical tests, such as error functions, statistical hypothesis tests, and ANOVA tests.

Keywords: students’ performance; e-learning; artificial neural network; optimization algorithms; firefly algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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