A Case Study on the Data Mining-Based Prediction of Students’ Performance for Effective and Sustainable E-Learning

Evelina Staneviciene, Daina Gudoniene (), Vytenis Punys and Arturas Kukstys
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Evelina Staneviciene: Faculty of Informatics, Kaunas University of Technology, Studentu Str. 50, LT-44249 Kaunas, Lithuania
Daina Gudoniene: Faculty of Informatics, Kaunas University of Technology, Studentu Str. 50, LT-44249 Kaunas, Lithuania
Vytenis Punys: Faculty of Informatics, Kaunas University of Technology, Studentu Str. 50, LT-44249 Kaunas, Lithuania
Arturas Kukstys: Bergen Kommune, Rådhusgaten 10, Bergen Rådhus, 5014 Bergen, Norway

Sustainability, 2024, vol. 16, issue 23, 1-15

Abstract: The study explores the application of data analytics and machine learning to forecast academic outcomes, with the aim of ensuring effective and sustainable e-learning. Technological study programs in universities often experience high dropout rates, which makes it essential to analyze and predict potential risks to reduce dropout percentages. Student performance prediction (SPP) offers potential benefits, including personalized learning and early interventions. However, challenges such as (1) data quality and availability and (2) incomplete and inconsistent data complicate this process. Moreover, to support the fourth Sustainable Development Goal (SDG), we focus on the quality of education. A case study approach is used using data mining techniques, particularly classification, regression, and clustering, to predict student performance. The case presented aims to predict risks and ensure academic success and quality. The cross-industry standard process for data mining (CRISP-DM) methodology is used to structure and guide the prediction process. The study shows that using data from student learning processes within an academic success prediction model and data mining can identify at-risk students.

Keywords: educational data mining; prediction; academic success; student performance; sustainable development goals; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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