The success of ePortfolio-based programming learning style diagnosis: Exploring the role of a heuristic fuzzy knowledge fusion

Angus F.M. Huang, John T.H. Wu, Stephen J.H. Yang, Wu Yuin Hwang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Computer programming is a high-level thinking activity. In the educational area, using learning styles to understand how students learn is a significant issue. The electronic Portfolio (ePortfolio) is a popular educational management and assessment tool. Unfortunately, few researchers investigate programming learning style diagnosis. This paper addresses this gap in research: this study constructs an ePortfolio-based programming learning style diagnosis to detect students' styles. The fusion of multiple fuzzy-based diagnosis knowledge is the main contribution of this work. This paper built a heuristic optimization method to integrate multiple diagnosis knowledge bases. Performance evaluations and empirical studies were implemented to verify the proposed algorithm and fusion solution. Experimental results showed that the proposed heuristic optimization firms the validity and stability of a diagnostic system, and the ePortfolio-based programming learning style diagnosis is highly accepted by students. Furthermore, teachers agreed that the knowledge fusion mechanism and diagnosis system were usable.

Original languageEnglish
Pages (from-to)8698-8706
Number of pages9
JournalExpert Systems with Applications
Volume39
Issue number10
DOIs
StatePublished - Aug 2012

Keywords

  • Evaluation methodologies
  • Intelligent tutoring systems
  • Programming and programming languages
  • Teaching/learning strategies

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