A study published in the Smart Learning Environments journal in 2022 explored the use of machine learning algorithms to predict undergraduate students' final exam grades based on their midterm scores. The research utilized various algorithms, including Random Forests, Support Vector Machines, and Logistic Regression, to analyze data from 1,854 students enrolled in a Turkish university's Turkish Language-I course during the 2019–2020 fall semester. The study aimed to assess the predictive accuracy of these models using only academic performance data, excluding demographic or socio-economic factors. SpringerOpen

Insights from Educational Data Mining:

Educational Data Mining (EDM) can provide valuable insights into student learning patterns and outcomes. In this study, the machine learning models achieved a classification accuracy of 70–75%, indicating that midterm exam grades are a significant predictor of final exam performance. Such predictive models can assist educators in identifying students who may need additional support and in making data-informed decisions to enhance teaching strategies.

Limitations of Educational Data Mining:

While EDM offers promising applications, it also has limitations. The study's reliance solely on academic performance data means it did not account for other influential factors such as student motivation, socio-economic background, or external support systems. Therefore, while EDM can highlight patterns and correlations, it may not fully capture the complexities of student learning experiences. Additionally, the exclusion of demographic variables limits the generalizability of the findings across diverse student populations.

This research exemplifies how EDM can be utilized to predict academic outcomes and inform educational practices. However, it also underscores the importance of considering a holistic range of factors to gain a comprehensive understanding of student performance and to ensure equitable educational support.