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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nawaf N. Hamadneh & Samer Atawneh & Waqar A. Khan & Khaled A. Almejalli & Adeeb Alhomoud, 2022. "Using Artificial Intelligence to Predict Students’ Academic Performance in Blended Learning," Sustainability, MDPI, vol. 14(18), pages 1-13, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11642-:d:916709
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    References listed on IDEAS

    as
    1. Nawaf N. Hamadneh, 2020. "Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 11(3), pages 19-29, July.
    2. Ioanna Lykourentzou & Ioannis Giannoukos & George Mpardis & Vassilis Nikolopoulos & Vassili Loumos, 2009. "Early and dynamic student achievement prediction in e‐learning courses using neural networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(2), pages 372-380, February.
    3. Wintoki, M. Babajide & Linck, James S. & Netter, Jeffry M., 2012. "Endogeneity and the dynamics of internal corporate governance," Journal of Financial Economics, Elsevier, vol. 105(3), pages 581-606.
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    Cited by:

    1. Sufyan Habib & Mohammed Arshad Khan & Nawaf N. Hamadneh, 2022. "Gender Sensitivity in Accessing Healthcare Services: Evidence from Saudi Arabia," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    2. Uthman Alturki & Ahmed Aldraiweesh, 2023. "The Factors Influencing 21st Century Skills and Problem-Solving Skills: The Acceptance of Blackboard as Sustainable Education," Sustainability, MDPI, vol. 15(17), pages 1-22, August.
    3. Dragan Milosevic & Dragana Trnavac & Dragoljub Ilic & Miroslav Vulic & Mica Djurdjev & Maja Radic & Branka Markovic & Nena Tomovic & Srdjan Ljubojevic & Aleksandar Cakic & Istvan Bodolo & Mladen Dobri, 2022. "A Practical Model of the Application of Information Technology in Various Fields of Online Education during the COVID-19 Pandemic: Mechanical Engineering, Traffic, Informatics and Statistics, Accounti," Sustainability, MDPI, vol. 14(23), pages 1-20, December.

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