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A Systematic Study on Tertiary Level Student Tuition Fee Waiver Management During Pandemic Using Machine Learning Approaches

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Advances in Information, Communication and Cybersecurity (ICI2C 2021)

Abstract

Machine learning techniques are compressively used in various fields like business, health, education, etc. The growing necessity in the education segment has concreted the approach for plenty of research projects that strongly emphasize student academic achievement and behavior analysis. This research study is about an analysis performed to identify the significant tuition waiver significance of student academic background, accomplishment, and family background. Supervised machine learning approaches gradually, Decision Tree Regression (DTR) and Random Forest Regression (RFR) are utilized in predicting tuition waivers. In addition, Cross-Validation (CV) is used for data overfitting. For better accuracy, DTR and RFR are implemented twice before and after cross-validation. There is enough data to train the models thoroughly and get quite good accuracy and performance. Results appear that the accuracy of DTR and RFR are 74.49% and 77.82%, respectively for before applied CV; and, 71.41%, and 76.90% respectively, for after applied CV.

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Shakir, A.K., Sutradhar, S., Sakib, A.H., Akram, W., Saleh, M.A., Abedin, M.Z. (2022). A Systematic Study on Tertiary Level Student Tuition Fee Waiver Management During Pandemic Using Machine Learning Approaches. In: Maleh, Y., Alazab, M., Gherabi, N., Tawalbeh, L., Abd El-Latif, A.A. (eds) Advances in Information, Communication and Cybersecurity. ICI2C 2021. Lecture Notes in Networks and Systems, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-030-91738-8_25

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