A fuzzy-based prior knowledge diagnostic model with multiple attribute evaluation

Yi Chun Lin, Yueh Min Huang

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Prior knowledge is a very important part of teaching and learning, as it affects how instructors and students interact with the learning materials. In general, tests are used to assess students' prior knowledge. Nevertheless, conventional testing approaches usually assign only an overall score to each student, and this may mean that students are unable to understand their own specific weaknesses. To address this problem, previous work has presented a prior knowledge diagnosis model with a single attribute to assist instructors and students in diagnosing and strengthening prior knowledge. However, this model neglects the fact that a diagnostic decision might involve multiple attributes. In order to provide more a precise diagnosis to instructors and students, this study thus proposes a fuzzy prior knowledge diagnosis model with a multiple attribute decision making technique for diagnosing and strengthening students' prior knowledge. The experimental results from an interdisciplinary bioinformatics course have demonstrated the utility and effectiveness of this innovative approach.

Original languageEnglish
Pages (from-to)119-136
Number of pages18
JournalEducational Technology and Society
Volume16
Issue number2
Publication statusPublished - 2013

All Science Journal Classification (ASJC) codes

  • Education
  • Sociology and Political Science
  • Engineering(all)

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