A Comparative Study of Predictions of Students’ Performances in Offline and Online Modes using Adaptive Neuro-Fuzzy Inference System

Abstract

Predicting a student's performance can help educational institutions to support the students in improving their academic performance and providing high-quality education. Creating a model that accurately predicts a student's performance is not only difficult but challenging. Before the pandemic situation students were more accustomed to offline i.e., physical mode of learning. As covid-19 took over the world the offline mode of education was totally disturbed. This situation resulted into the new beginning towards online mode of teaching over the Internet. In this article, these two modes are analysed and compared with reference to students’ academic performances. The article models a predicting academic performance of students before covid i.e., physical mode and during Covid i.e., online mode, to help the students to improve their performances. The proposed model works in two steps. First, two sets of students’ previous semester end results (SEE) i.e., after offline mode and after online mode, are collected and pre-processed using normalizing the performances in order to improving the efficiency and accuracy. Secondly, Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to predict the academic result performances in both learning modes. Three membership functions gaussian (Gausmf), triangular (Trimf) and gausian-bell (Gbellmf) of ANFIS are used to generate the fuzzy rules for the prediction process proposed in this paper.