A Hierarchical Fuzzy Model for Assessing Student’s Competency

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Abstract

The aim of the study is to improve a system of competency’ assessment, described in the Educational Program of the specialty. This assessment is based on a hierarchical fuzzy model in the fuzzyTECH program. The adequacy of proposed elaborated model is confirmed by a statistic agreement with thesis attestation results of Master of Arts. Besides, the model showed that combining the competency in the course of writing and attestation the Master of Arts thesis shall result in synergy effects. The model input parameters consist of the data of academic assessment and the structure of the educational-professional program. These inputs simplify the process of assessing the competency while comparing with the existing approaches. Thus, stakeholders are able to assess the professional qualities of a graduate without requiring additional resources, because the model is built on the open source data such as the Educational Program of the specialty and the diploma supplement. The proposed model can assess the quality of educational-professional programs, and assist their comparative evaluation. The model’s uniqueness is that output results of competency assessment are described both by linguistic terms, more common for employers, and numeric assessment, using 100-point scale, more common for students and teaching staff. The main innovation of the model is its ability to assess adequately the obtained competency not only by the final learning outcomes, but also at any stage of the curriculum implementation. Therefore, the fuzzy model can also be utilized to predict the graduates’ final performance, which enables students to apply the model for self-assessing of their own competency in a learning process. Such self-assessment may help students promote learning scores in the courses which is relevant to determined competency.

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APA

Hu, Z., & Koroliuk, Y. (2020). A Hierarchical Fuzzy Model for Assessing Student’s Competency. In Advances in Intelligent Systems and Computing (Vol. 1126 AISC, pp. 380–393). Springer. https://doi.org/10.1007/978-3-030-39162-1_35

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