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Chih-Ming Chen
Researcher at National Chung Hsing University
Publications - 327
Citations - 9729
Chih-Ming Chen is an academic researcher from National Chung Hsing University. The author has contributed to research in topics: Educational technology & Dye-sensitized solar cell. The author has an hindex of 44, co-authored 317 publications receiving 8328 citations. Previous affiliations of Chih-Ming Chen include University of Education, Winneba & Academia Sinica.
Papers
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Personalized e-learning system using Item Response Theory
TL;DR: This study proposes a personalized e-learning system based on Item Response Theory (PEL-IRT) which considers both course material difficulty and learner ability to provide individual learning paths for learners and shows that applying Item Response theory to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.
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Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle
Chih-Ming Chen,Ching-Ju Chung +1 more
TL;DR: A personalized mobile English vocabulary learning system based on Item Response Theory and learning memory cycle, which recommends appropriate English vocabulary for learning according to individual learner vocabulary ability and memory cycle is presented.
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Intelligent web-based learning system with personalized learning path guidance
TL;DR: Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.
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Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance
Chih-Ming Chen,Chung-Hsin Wu +1 more
TL;DR: Analysis results indicate that, while the three video lecture types enhance learning performance, learning performance with lecture capture and picture-in-picture types is superior to that associated with the voice-over type.
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An efficient fuzzy classifier with feature selection based on fuzzy entropy
TL;DR: This paper presents an efficient fuzzy classifier with the ability of feature selection based on a fuzzy entropy measure and investigates the use of fuzzy entropy to select relevant features.