An Adaptive Neural Fuzzy Inference System for prediction of student performance in Higher Education

The prediction of student performance helps the teaching learning process identify the direction of student progression and take remedial measures to step up the performance of weak students before it is too late. Additionally it also assists in identifying students with great potential and cater to their intellectual needs. This works as a feedback mechanism as well as feed forward mechanism. The feedback mechanism. It is also useful as feedback mechanism in apprising guardians of student progression and as feed forward approach for improving student result. It enables effective and intelligent data mining of student result data to forecast their potential performance. The paper presents an adaptive neuro fuzzy inference system with back propagation for prediction of student performance progression based on trends of past performances. The model is developed MATLAB R2018a software and validated by real time data set comprising of results of mix of students of various higher education institutions under an Indian state university. The model has a huge scope for quality management of teaching learning process in higher education.