Machine Learning Based Decision Support for Student Academic Advisory System

In the era towards human capital development, higher education institutions such as universities and colleges need to equip with the substantial capability of analysing student’s academic achievement level for making appropriate academic decision such as selecting a course specialization. Choosing a suitable field of study significantly improves students’ learning experience in their university studies. As a positive impact, this can help to reduce the drop-out rate of universities. However, most of the existing academic progression process to assign students to their specialization is time-consuming as each single process involves extensive analysis of data from different aspects of study. Personal consultancy may result in bias of advice which most of the time is based on their consultant’s personal experience. This paper aims to propose a machine learning based decision support system for academic advisory. Artificial neural network (ANN) is applied as the decision support engine in the proposed model. This model assists academicians to make better decisions when assigning students to their specialization. This study has taken a school of SEGI University to be the platform to run the preliminary testing on the proposed system. The third year students’ academic records and their subjects’ assessment in previous semesters were sampled as training set for the model. Experimental results showed that the proposed decision support model exhibits high accuracy in classifying students into two different specializations according to their respective academic achievements levels.