A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programs

Predicting student's successful completion of academic programs and the features that influence their performance can have a significant effect on improving students' completion, and graduation rates and reduce attrition rates. Therefore, identifying students are at risk, and the courses where improvements in content, delivery mode, pedagogy, and assessment activities can improve students' learning experience and completion rates. In this work, we have developed a prediction and explanatory model using adaptive neuro-fuzzy inference system (ANFIS) methodology to predict the grade point average (GPA), at graduation time, of students enrolled in the information technology program at Ajman University. The approach adopted uses students' grades in introductory and fundamental IT courses and high school grade point average (HSGPA) as predictors. Sensitivity analysis was performed on the model to quantify the relative significance of each predictor in explaining variations in graduation GPA. Our findings indicate HSGPA is the most influential factor in predicting graduation GPA, with data structures, operating systems, and software engineering coming closely in second place. On the explanatory side, we have found that discrete mathematics was the most influential course causing variations in graduation GPA, followed by software engineering, information security, and HSGPA. When we ran the model on the testing data, 77% of the predicted values fell within one root mean square error (0.29) of the actual GPA, which has a maximum of four. We have also shown that the ANFIS approach has better predictive accuracy than commonly used techniques such as multilinear regression. We recommend that IT programs at other institutions conduct comparable studies and shed some light on our findings.