Implementation of a Personalized Adaptive Mobile Learning System

Abstract

A notable obstacle in the field of education is the restricted flexibility of traditional teaching approaches. These approaches frequently take a consistent stance, disregarding the wide range of learning preferences that pupils possess. This leads to a decrease in student motivation and engagement, which in turn produces below-average learning outcomes. This research focuses on creating an adaptive learning system that classifies learners using the Felder-Silverman model in order to overcome these problems. After then, this system creates customized recommendations based on user choices in an effort to improve learning results. In order to keep enhancing the system's efficacy, the study have also included a feedback mechanism and performance evaluation.