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Vol.12, No.2, May 2023. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
A Regression Model and a Combination of Academic and Non-Academic Features to Predict Student Academic Performance
Muhammad Arifin, Widowati Widowati, Farikhin Farikhin, Gudnanto Gudnanto
© 2023 Muhammad Arifin, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 12, Issue 2, Pages 855-864, ISSN 2217-8309, DOI: 10.18421/TEM122-31, May 2023.
Received: 29 January 2023. Revised: 17 April 2023.
Abstract:
Predicting academic performance provides an effective way for students and faculties to monitor their academic progress. The identification of the most significant features was a key outcome of this research, and the college/university databases from online learning platforms are the main academic data sets used to ascertain performance. However, previous research emphasized the addition of other significant features in the prediction of academic performance. Universities’ organizational features include non-academic essential data used in determining student success, but no research has utilized this data to predict student academic performance. Generally, to evaluate binary classification, the relationship between the predicted classifications and the true classifications is analyzed, this approach can lead to the loss of important information from the data. Furthermore, to avoid such loss, this research proposes a regression model by comparing six regression algorithms, and combining academic and non-academic features for prediction student academic performance. Among the various models examined, the gradient-boosted trees regression model demonstrated the lowest error rate. The proposed features have been observed to have a significant impact on academic performance, making them suitable for use in predictions.
Keywords –Regression algorithm, educational data mining, predicting student academic performance, lms, campus organization. |
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