An Adaptive E-Assessment for Self-Learning: A Sustainable Education Model in the Post COVID-19 ERA of Digital Media

Abstract

The integration of information and communication technologies in education has exploded various opportunities in learning and assessment. The state-of-the-art electronic learning and assessment systems are confined to delivering instructional content without focusing on learning analytics. Hence, more objective assessment systems are required that can keep track of the performance of students, especially for self-learning in the post-COVID-19 digital era. These assessment systems need to be optimized so that students can receive an accurate prescription in a limited time during the unavailability of teachers. Therefore, this study intends to propose an adaptive e-assessment model for learning and assessment. The proposed model comprises components including a domain model, student model, and assessment adaptation engine. The features of fuzzy logic have been utilized to address uncertainty and analyze student performance using a learner-centric approach. The prototype has been verified by deploying it on a computer science course offered at a degree-level program of an open university. The results reveal the improved performance of learners using the adaptive e-assessment system. The study also facilitates by providing a roadmap for the researchers to develop a generalized and personalized e-learning system for other courses.