A Model for Predicting Student Future Performance Using Neural Network
The primary objective of the study is to develop a model that predicts student’s future academic performance based on their High School Grades and Entrance Examination Result. It aims to improve the advising process of the state colleges and help the students to inform how well or poorly they would perform in a specific course. Also, it will help students to prepare for the future low academic performance. Neural Network (NN) supervised learning method was used to analyze the pattern of collected data (former student’s academic record) to see the probability of incoming first-year student if they can graduate on time and not graduate on time. NN Feedforward and Backpropagation algorithm were executed to teach the model to learn from the pattern of the dataset and then built the predictive model. The model created was called ICA (Intelligent Career Advising) designed for Engineering, Information Technology, and Education courses as the basis for predicting student’s success in the mentioned fields. The created model attained 68.81% accuracy. The model can be a tool for faculty and staff who act as an academic adviser in advising incoming first-year students.
Keywords- Neural Network Feedforward, Backpropagation Predictive algorithm, Future Student Performance