Parents’ Sum of Salaries Analyses towards School Tuition Fee Arrears Potential with Decision Tree Method

Iskandar, Iqbal Dzulfiqar

doi:10.24002/ijis.v2i1.2168

Cited by 1 publication

(1 citation statement)

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“…Tuition fees are a mount of money charged to a person by the educational institution in which he or she enrolls, based on the number of course credits in which he or she is enrolled and the school budget for each period of study. In other words, it is an amount of money to support education within an institution in teaching and various activities (Iskandar, 2019). Higher education is characterized by significant private returns, both monetary and non-monetary.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Predicting University Academic Fees in a Semi-Urban Area

Heritier Nsenge,

Mystere Kivuyirwa,

Ange Katya

2023

Am. J. Educ. Technol.

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Academic fees are an annual amount that students must pay in return for the training they receive. These fees can change according to several factors. Among these factors, some can influence the increase of tuition fees, while others can ensure that fees are moderate. Therefore, this research aimed to analyze these factors and integrate them as predictors in a deep-learning model for predicting university tuition fees. Hence, the authors used quantitative analysis based on secondary data on tuition fees at the Université de l’Assomption au Congo (UAC). These data were used to develop two regressive neural network models, namely the three-hidden-layer neural network and the four-hidden-layer neural network, to determine the best model for prediction and deployment purposes. The metrics used to evaluate the performance of these two models were mean absolute error, mean square error, root mean square error and coefficient of determination. The results revealed that academic costs increase as a student moves up the promotion ladder. After developing these models, although they all performed successively at 95.3% and 95.6%, the hidden 4-layer model was deployed. The predictors used as features were six: academic year, promotion, tuition fees, dissertation fees, partial time lecturers fees and equipment fees.

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Section: Literature Reviewmentioning

confidence: 99%