Data analytics on performance of computing students

Abstract

This study was conducted to determine the relationship between computing students' initial and final academic performance to support decision making in higher education institutions. The data used in this research contains the initial GPA and CGPA of 710 computing students for a period of 5 years. Test of normality for the final graduating results and linear regression models were fitted to the data to predict the overall performance based on the initial result. This study revealed that there are strong linear relationships between the GPAs and CGPAs of computing students over the period of this study. The contribution of this work is that the result enables the student and the teacher to understand the trend of students' academic performance for the purpose of decisionmaking. Keeping track of the performance of the students help provide support whenever is needed. This results aimed to decrease student dropout by means of facilitating student to predict their probability of success in computing courses after enrollment. In addition, teachers will be able to boost student performance in their courses as a result of enhanced determination of student's abilities to learn the course and finetuning teaching approaches and techniques.