Fuzzy-Clustering Embedded Regression for Predicting Student Academic Performance

The prediction of student academic performance is important to both educational institutions and students themselves for a variety of reasons. However, previous techniques often consider only past numeric data for prediction, whereas others overuse different types of indicative attribute, leading to the creation of complicated predicting methods whose results are difficult to interpret. This paper proposes a novel approach to predicting student final period grade, using attributes related to student past academic records and attributes of normal study behaviour, which are readily obtainable and easily interpretable. The proposed approach works by employing fuzzy clustering and multi-variable regression within an integrated framework, which also includes an offset value mechanism to support the use of attributes that are related to normal student study behaviour. Comparative experimental investigations are carried out, demonstrating the potential of the proposed work in producing more accurate results.