Competency-Based E-Learning Systems: Automated Integration of User Competency Portfolio

Abstract

In today’s learning environment, e-learning systems are becoming a necessity. A competency-based student portfolio system is also gaining popularity. Due to the variety of e-learning systems and the increasing mobility of students between different learning institutions or e-learning systems, a higher level of automated competency portfolio integration is required. Increasing mobility and complexity makes manual mapping of student competencies unsustainable. The purpose of this paper is to automate the mapping of e-learning system competencies with student-gained competencies from other systems. Natural language processing, text similarity estimation, and fuzzy logic applications were used to implement the automated mapping process. Multiple cases have been tested to determine the effectiveness of the proposed solution. The solution has been shown to be able to accurately predict the coverage of system course competency by students’ course competency with an accuracy of approximately 77%. As it is not possible to achieve 100% mapping accuracy, the competency mapping should be executed semi-automatically by applying the proposed solution to obtain the initial mapping, and then manually revising the results as necessary. When compared to a fully manual mapping of competencies, it reduces workload and increases resource sustainability.