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%.
Highlights
The knowledge testing process is evolving, and student-oriented teaching, based on the estimation of the student competency portfolio, is gaining its application in schools
Some e-learning systems are based on a competency tree, while others use a competency list
While competency tree transformation to competency list requires no effort, the transformation from competency list to the tree is mostly done by a human as it requires context understanding and additional analysis, such as text interpretation
Summary
The knowledge testing process is evolving, and student-oriented teaching, based on the estimation of the student competency portfolio, is gaining its application in schools. While competency tree transformation to competency list requires no effort (nodes or just leaves of the tree are listed), the transformation from competency list to the tree is mostly done by a human as it requires context understanding and additional analysis, such as text interpretation. This slows down the integration of different e-learning systems. We investigate the problem of transforming the text-written competency list to the competency tree structure This is done by applying different natural language processing (NLP) methods.
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