Education cost as a new fickle in higher education for students learning via quantitively multinomial logistic regression

Abstract

The study was conducted to find out the effects of tuition fee on quality of education for students in the Islamia University of Bahawalpur (IUB), Pakistan to fulfill their future goals. The study used a multinomial logistic regression method for knowing the factors that either make it difficult or easy to pay academic fees. The primary data was gathered using an online survey made with a Google form and sent by email and WhatsApp to students in different departments. A certain amount of time was dedicated to the data collection process. Due to a research deficit on tuition fees for university students in Pakistan, this study was deemed necessary. It entails an examination of the elements that impact graduate quality, taking into account academic achievement, educational expectations, and tuition price donors. The study employed a multinomial logistic regression approach to identify the variables that either facilitate or impede the payment of academic fees. One thousand pupils responded in the positive. The results showed that expensive college tuition causes students to perform poorly academically, lack confidence, have poorly groomed personalities, and have fewer career options. On the other hand, students from well-off households and those who worked part-time performed better. Furthermore, the learning behavior and attitude of students who received scholarships and worked as teaching assistants improved because they were more punctual and attentive in class. By bringing attention to the need for lawmakers and government agencies to take significant steps toward improving student outcomes in higher education—like subsidizing tuition costs, supplying educational resources, and giving out scholarships and studentships—this study helps students. It also fills a research gap in the field of tuition fees in Pakistan by offering new researchers a case study example of how to use multinomial logistic regression to investigate the relationship between students’ academic objectives and the expense of their education.