Applying reinforcement and mastery learning: how it works based on personalized e-learning curriculum?

Abstract

Many researchers in the field of personalized learning or topic sequencing have proposed and implemented various mechanisms to improve the learning process with the main objectives of maximizing learning and dynamically selecting the closest teaching operation in order to achieve the learning goals. However, upon recommending the personalized learning sequence, based on literature review, the e-learning instructional strategists failed to perform or address the corrective measures to immediately remediate the learning difficulty. This paper presents the combination of the reinforcement learning concepts in the area of artificial intelligence and the mastery learning in educational psychology to remediate learning difficulties of the learners. The results show that based on the presented corrective measures, which are based on students’ background performance matrixes that are collected during the learning process and summative examination, the students are able to correct their learning difficulties and improved their learning performances. The end semesters’ results show the guarantee that almost all of the students who undergo the reinforcement and mastery learning procedures can successfully pass the e-learning course.