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| Analysis of Students' Enrollment and Graduation Rates in Gulu University in Uganda. A Case Study of Gulu University
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Nabaasa Mark , , Mr., Dr Ariyo Gracious Kazaara , Tukamuhebwa Deus , Kobusingye prudence , Kamugisha Nelson , Mutesi Catherine , Friday Christopher , Isabirye Benefansi , Barigye Brighton
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Abstract:
This research's main objective was to investigate the enrollment and graduation rates at Gulu University in Uganda. An approach to quantitative research was used in this work. All Gulu University students from the academic period 2002/2003 to 2019/2020 made up the study's population. Document from the admissions office with secondary data for the study was the primary tool utilized to collect data. The information came from academic management systems, which are employed to oversee daily academic activities. Because these systems are maintained by transactional databases, which are frequently modified and lack the ability to archive histories of data instances, they are unsuitable for performing the analysis on enrollment prediction and graduation prediction. Asked on the likelihood of enrollment based on the fact that department in charge of recruiting students are left to guess. In the majority of cases, this is incorrect because it causes budget shortages and resource strain. Universities experience difficulty predicting how many students will graduate from the school of interest in addition to speculating on those who are likely to enroll. There are various causes why a student doesn't complete their degree of study, such their financial situation and family history. As a result, models for forecasting enrollment and graduation are put forth to help in making forecasts about how many admitted students will enroll and how many will graduate. The components of the suggested The inputs to the proposed prediction system were sourced from student data stored in a worksheet regarding to student details the proposed system then transforms By include a time variant, this data was converted into time series data, which was then submitted to regression analysis using the appropriate econometric model in Stata. The enrollment and graduation of students will then be predicted using the generated models. With a 59% accuracy rate, the model's accuracy was remarkably good. The dataset utilized, which was noisy because it was taken from actual student transactional databases, resulted in the proposed model having a slightly poorer accuracy in comparison to some of the papers assessed.
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