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Increased Digital Resource Consumption in Higher Educational Institutions and the Artificial Intelligence Role in Informing Decisions Related to Student Performance

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  • Anjeela Jokhan

    (Permanent Secretary for Ministry of Education, Heritage and Arts, Suva, Fiji
    Current address: Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji.)

  • Aneesh A. Chand

    (School of Information Technology, Engineering, Mathematics and Physics (STEMP), Suva, Fiji
    Current address: Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji.)

  • Vineet Singh

    (School of Information Technology, Engineering, Mathematics and Physics (STEMP), Suva, Fiji
    Current address: Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji.)

  • Kabir A. Mamun

    (School of Information Technology, Engineering, Mathematics and Physics (STEMP), Suva, Fiji)

Abstract

As education is an essential enabler in achieving Sustainable Development Goals (SDGs), it should “ensure inclusive, equitable quality education, and promote lifelong learning opportunities for all”. One of the frameworks for SDG 4 is to propose the concepts of “equitable quality education”. To attain and work in the context of SDG 4, artificial intelligence (AI) is a booming technology, which is gaining interest in understanding student behavior and assessing student performance. AI holds great potential for improving education as it has started to develop innovative teaching and learning approaches in education to create better learning. To provide better education, data analytics is critical. AI and machine learning approaches provide rapid solutions with high accuracy. This paper presents an AI-based analytics tool created to predict student performance in a first-year Information Technology literacy course at The University of the South Pacific (USP). A Random Forest based classification model was developed which predicted the performance of the student in week 6 with an accuracy value of 97.03%, sensitivity value of 95.26%, specificity value of 98.8%, precision value of 98.86%, Matthews correlation coefficient value of 94% and Area Under the ROC Curve value of 99%. Hence, such a method is very useful in predicting student performance early in their courses of allowing for early intervention. During the COVID-19 outbreak, the experimental findings demonstrate that the suggested prediction model satisfies the required accuracy, precision, and recall factors for forecasting the behavioural elements of teaching and e-learning for students in virtual education systems.

Suggested Citation

  • Anjeela Jokhan & Aneesh A. Chand & Vineet Singh & Kabir A. Mamun, 2022. "Increased Digital Resource Consumption in Higher Educational Institutions and the Artificial Intelligence Role in Informing Decisions Related to Student Performance," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2377-:d:753240
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    References listed on IDEAS

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    1. Edin Osmanbegovic & Mirza Suljic, 2012. "Data Mining Approach For Predicting Student Performance," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 10(1), pages 3-12.
    2. Diego Buenaño-Fernández & David Gil & Sergio Luján-Mora, 2019. "Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
    3. Vasiliki Kioupi & Nikolaos Voulvoulis, 2019. "Education for Sustainable Development: A Systemic Framework for Connecting the SDGs to Educational Outcomes," Sustainability, MDPI, vol. 11(21), pages 1-18, November.
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