Prediction of engineering students’ academic performance using neural network and linear regression / Pauziah Mohd Arsad

This thesis describes the development of Electrical Engineering students’ performance prediction model using Artificial Neural Network (ANN) based on SIMS data from three generations of Matriculation and Diploma students. It was observed that there was a certain pattern or trend between the strong ability students and the weaker ones in terms of performance. The strong ability students managed to graduate steadily with high CGPA upon graduation, while the weaker ones tend to waver and finally graduate with minimum CGPA or even extended for another one or two semesters to complete the required credit hours. The Grade Points (GP) of fundamental subjects attempted at semester one was used as inputs to the developed Neural Network Students’ Performance Prediction Model (NNSPPM) to predict the output which is CGPA8 upon graduation. The fundamental subjects strongly influenced the overall performance of students. The NNSPPM was then tested with another set of input data consisting GP of subjects at semester three to see the predicted output. The NNSPPM was further validated with a different set of data, namely Diploma students taking the same subjects at semester three, sitting the same set of examination questions as that of Matriculation students…