學習資訊專業學院—資訊教育研究所

Permanent URI for this communityhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/25

資訊教育研究所之碩士班成立於民國80年，博士班成立於民國86年，目前研究生共約160名。本所原屬資訊教育學系，於95學年度起因應系所組織調整，成為獨立研究所，歸屬教育學院。

本所以『資訊科技教育』和『數位學習』兩個專業領域之研究發展與人才培育為宗旨，課程設計分別針對此兩個專業領域規劃必、選修專業科目，提供學生紮實而嚴謹的學術專業知能及個別化之研究訓練。本所教育目標包括：

1、培育資訊科技教育人才；
2、培育數位學習產業人才；
3、培育資訊科技教育與數位學習研究人才。

本所目前六名專任教師，四位教授，二位副教授，在資訊教育領域均具有豐富之教學與研究經驗且均積極從事研究，每年獲科技部補助研究計畫之平均數量與金額在本校名列前茅。另外，本所教師積極參與國內重大資訊教育政策及課程綱要之制定，積極推動國內資訊教育之發展。
 

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Author: search.filters.author.Chen, S. W.

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Now showing 1 - 7 of 7
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    命題式屬性化概念圖的評量與回饋
    (2000-11-23) Lin, S. C.; Chang, R. B.; Chen, S. W.; Chang, K. E.
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    The effect of learning achievement using computer-supported concept mapping with relation-free or fixed
    (2000-05-29) Lin, S. C.; Chang, R. B.; Chen, S. W.; Chang, K. E.
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    Fuzzy Integration of Attributed Concept Maps
    (1999-11-04) Lin, S. C.; Chang, K. E.; Pan, H. M.; Chen, S. W.
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    Fuzzy integration of attributed concept maps
    (1998-01-01) Chang, K. E.; Lin, S. C.; Chen, S. W.; Pan, H. M.
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    Fuzzy assessment of attributed concept map
    (1998-10-14) Lin, S. C.; Chen, S. W.; Chang, K. E.
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    Neural simulation of Petri nets
    (Elsevier, 1999-02-01) Chen, S. W.; Fang, C. Y.; Chang, K. E.
    Petri nets and neural networks share a number of analogies. Investigations of their relationships can be sorted into two categories: (a) the modeling of neural activities with Petri nets, and (b) the neural simulation of Petri nets. The work presented in this paper belongs to the second category. Unlike divide-and-conquer approaches, the proposed method settles the extraneous skeleton of simulators. Inherent distinctions of Petri nets are characterized by the individual constituents of simulators. The constructed simulators thus reveal a consistently uniform structure on a macroscopic level. Compared with those generated by the divide-and-conquer approaches, ours look much portable and are empirically economic. Furthermore, in a fully parallel machine with enough nodes the overall time complexity of the neural simulator will be constant.
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    Attributed concept maps: fuzzy integration and fuzzy matching
    (Institute of Electrical and Electronics Engineers, 2001-10-01) Chen, S. W.; Lin, S. C.; Chang, K. E.
    A concept map, typically depicted as a connected graph, is composed of a collection of propositions. Each proposition forming a semantic unit consists of a small set of concept nodes interconnected to one another with relation links. Concept maps possess a number of appealing features which make them a promising tool for teaching, learning, evaluation, and curriculum planning. We extend concept maps by associating their concept nodes and relation links with attribute values which indicate the relative significance of concepts and relationships in knowledge representation. The resulting maps are called attributed concept maps (ACM). Assessing students will be conducted by matching their ACMs with those prebuilt by experts. The associated techniques are referred to as map matching techniques. The building of an expert ACM has in the past been done by only one specialist. We integrate a number of maps developed by separate experts into a single map, called the master map (MM), which will serve as a prototypical map in map matching. Both map integration and map matching are conceptualized in terms of fuzzy set discipline. Experimental results have shown that the proposed ideas of ACM, MM, fuzzy map integration, and fuzzy map matching are well suited for students with high performances and difficult subject materials.