Applying a fuzzy, multi-criteria decision-making method to the performance evaluation scores of industrial design courses

Juan Li, Zhe Li, Shuo-Fang Liu, Meng Cheng

Research output: Contribution to journalArticle

Abstract

Traditional methods for conducting performance evaluations of academic courses are somewhat limited in that they are unable to account for both quantitative and qualitative data. For example, the data used to assess student performance in a typical industrial design course are generally complex, multi-criteria, multi-variable, and frequently vague or ambiguous, they are qualitative and therefore non-quantifiable. This research proposes a fuzzy decision-making evaluation model to solve this problem. To this purpose, we gathered sample data from an industrial design course taught at a university in China, and a total of 108 students (from Class A and Class B) and 5 review experts participated in the experiment. We then employed a fuzzy TOPSIS methodology to the 7 evaluation criterias in order to assess their relative importance as course evaluation criteria. In order to achieve an integrated evaluation of each student’s design scheme, we applied a series of fuzzy calculations. Finally, the results of T-tests show that the overall student performance for Class B was superior to that of Class A. We therefore posit that a fuzzy, multi-criteria approach to decision-making can in fact be used to evaluate both quantitative and qualitative data with an acceptable degree of objectivity and accuracy.

Original languageEnglish
JournalInteractive Learning Environments
DOIs
Publication statusPublished - 2019 Jan 1

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decision making criterion
Product design
Decision making
Students
evaluation
performance
student
decision making
objectivity
expert
China
university
experiment
methodology
Experiments

All Science Journal Classification (ASJC) codes

  • Education
  • Computer Science Applications

Cite this

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abstract = "Traditional methods for conducting performance evaluations of academic courses are somewhat limited in that they are unable to account for both quantitative and qualitative data. For example, the data used to assess student performance in a typical industrial design course are generally complex, multi-criteria, multi-variable, and frequently vague or ambiguous, they are qualitative and therefore non-quantifiable. This research proposes a fuzzy decision-making evaluation model to solve this problem. To this purpose, we gathered sample data from an industrial design course taught at a university in China, and a total of 108 students (from Class A and Class B) and 5 review experts participated in the experiment. We then employed a fuzzy TOPSIS methodology to the 7 evaluation criterias in order to assess their relative importance as course evaluation criteria. In order to achieve an integrated evaluation of each student’s design scheme, we applied a series of fuzzy calculations. Finally, the results of T-tests show that the overall student performance for Class B was superior to that of Class A. We therefore posit that a fuzzy, multi-criteria approach to decision-making can in fact be used to evaluate both quantitative and qualitative data with an acceptable degree of objectivity and accuracy.",
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