Automated Transformation from Competency List to Tree: Way to Competency-Based Adaptive Knowledge E-Evaluation

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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%.

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Margienė, A., Ramanauskaitė, S., Nugaras, J., & Stefanovič, P. (2022). Automated Transformation from Competency List to Tree: Way to Competency-Based Adaptive Knowledge E-Evaluation. Applied Sciences (Switzerland), 12(3). https://doi.org/10.3390/app12031582

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