Automated transformation from competency list to tree: Way to competency-based adaptive knowledge e-evaluation

Abstract

E-learning is rapidly gaining its application. While actively adapting student-oriented learning with the competency evaluation model, the standard of competency support in existing e-learning systems is not implemented and varies. This complicated integration of different e-learning systems or transfer from one system to another might be challenging if the student had his or her competency portfolio in list form, while another system supports tree-based competency portfolios. Therefore, in this paper, we propose a transformation model dedicated to converting the competency list to a competency tree. This solution incorporates text processing and analysis, competency ranking based on Bloom’s taxonomy, and competency topic area clustering. The case analysis illustrates the model’s capability to generate a qualitative tree from the competency list, where the average accuracy of competency assignment to appropriate parent competency is 72%, but, in some cases, it reaches just 50%.