Particle Swarm Model for Predicting Student Performance in Computing Programs

Predicting student performance in computing majors and the factors affecting his success can have a substantial effect on improving student academic performance and his on-time graduation with all the financial benefits that come with that. There is a limited amount of time an academic advisor may allocate to each student to identify problem areas in the curriculum and take appropriate actions and advise the student based on informed judgement. Thus, there is a need to predict which students are at risk early on in the program. In this work, we have built a prediction model based on particle swarm optimization to estimate the final graduation grade point average (GPA) of students enrolled in the information technology program at Ajman University. Input predictors used in this work were Students' final GPA scores in core courses and high school average grade. Based on records of 74 students who have graduated from the program so far, we have found that the most influential predictor of graduation GPA is high school grade average. Our results showed that the Data Structures and Discrete Mathematics have no role to play in the prediction of GPA while networking and security courses have the most significant prediction contribution. Forty per cent of predicted values fall within 0.25 of the real GPA, which has a maximum upper bound of four. However, the accuracy of the model significantly improved when applied to a much larger publicly available dataset with 88% of GPA scores falling within 0.25 of the actual GPA.