Intelligent preference selection model based on NRE for evaluating student learning achievement

  • Authors:

  • Yao-Hsien Chen;Ching-Hsue Cheng;Jing-Wei Liu

  • Affiliations:

  • Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Touliu, Yunlin 640, Taiwan, ROC and Department of Information Managemen ...;Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Touliu, Yunlin 640, Taiwan, ROC;Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Touliu, Yunlin 640, Taiwan, ROC

  • Venue:
  • Computers & Education
  • Year:
  • 2010

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Abstract

In order to evaluate student learning achievement, several aspects should be considered, such as exercises, examinations, and observations. Traditionally, such an evaluation calculates a final score using a weighted average method after awarding numerical scores, and then determines a grade according to a set of established crisp criteria. However, this approach lacks the potential to reflect the individual characteristics of a class compared to others. Several researches have used fuzzy techniques to devise practical methods for evaluating student learning achievement to ascertain linguistic terms that are usually used by teachers to assess student learning achievement. However, these approaches are largely based on expert opinions and require complicated computational processes. In this paper, we present a new method for evaluating student learning achievement using an adaptive ordered weighted averaging operator and K-nearest-neighbor classification method. The proposed method simulates the evaluation behavior of teachers when performing a student achievement evaluation based on a norm-referenced evaluation by identifying situations involving the application of intelligence and provides a useful means to award a reasonable grade to students. Furthermore, the proposed method provides a feedback mechanism to update the norm dataset. Therefore, the repetitious use of the feedback mechanism will gradually strengthen the representativeness of the norm dataset.