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A Systematic Study on Tertiary Level Student Tuition Fee Waiver Management During Pandemic Using Machine Learning Approaches
Asif Khan Shakir, Ahsanul Hoque Sakib, Md. Abu Saleh, Shanjoy Sutradhar, Washim Akram,

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.

Machine learning, Supervised machine learning, Tuition fee waiver, Cross-Validation (CV), Decision Tree Regression (DTR), Random Forest Regression (RFR)
Journal or Conference Name
Lecture Notes in Networks and Systems
Publication Year