Learning style prediction of e-learner using hybrid optimizer-based neural network

Abstract

The Learning Style prediction model in e-learning systems has gained immense attention in the area of education. In the current scenario, the major demand for online platforms is to provide a substantiated interface that acclimatizes the learning styles of the learners. People learn in different ways, and their preferences can change over time. The accurate prediction of learning style can raise the learners' learning gain. This Research proposed a technique to predict the learning styles, by capturing the interaction behavior of the learner. The learning styles are predicted and grounded on the uprooted features using a Neural Network. It is trained and classified using a hybrid optimizer which is a fusion of Squirrel Search (SS) and Rider Optimization Algorithm (ROA). Felder-Silverman Learning Style Model is used to map the learner's learning styles.  Eventually, the pupil and course ID, learning style, course completion status, and test score data are recorded to find the correlation. The proposed hybrid optimizer-based model provides superior performance compared to techniques with an accuracy of 0.95 and a maximal correlation of 0.406.