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

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/107438

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107438

Title:	The success of ePortfolio-based programming learning style diagnosis: Exploring the role of a heuristic fuzzy knowledge fusion
Authors:	楊鎮華;Huang, Angus F.M.;Wu, John T.H.;Yang, Stephen J.H.;Hwang, Wu-Yuin
Contributors:	資訊電機學院資訊工程學系
Keywords:	Diagnosis;Education;Evaluation methodologies;Heuristic;Intelligent tutoring systems;Knowledge bases (artificial intelligence);Learning;Optimization;Programming;Programming and programming languages;Students;Teaching/learning strategies
Date:	2012-08-01
Issue Date:	2026-04-23 14:12:57 (UTC+8)
Publisher:	Elsevier Ltd.;Elsevier Ltd
Abstract:	摘要： ► ePortfolio can assist in diagnosing students’ programming learning styles. ► Cat intelligence is a good viewpoint for building multi-objective optimization. ► The CatOpt algorithm proposed is effective for fuzzy knowledge fusion. ► The CatOpt algorithm proposed outperformed the compared heuristic optimizations. ► The heuristic knowledge fusion improved the validity and stability of diagnosis. 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. 出版者： Elsevier Ltd 出版日期： 2012-08 出處： Expert systems with applications, 2012-08, Vol.39 (10), p.8698-8706 版權： 2012 Elsevier Ltd 識別號： ISSN: 0957-4174 識別號： EISSN: 1873-6793 識別號： DOI: 10.1016/j.eswa.2012.01.212
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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