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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    國中資訊組長之社會網路分析
    (2008) 康瑜芳
      本研究從社會網路分析(Social Network Analysis)角度研究台北市國中資訊組長之資訊科技實務分享網路及資訊科技融入教學諮詢網路。每一個網路有「獲得」及「回饋」兩個面向,從七個社會網路分析指標進行分析,包括密度、可達性、相互性、中心性、中介者角色、結構洞、子群組。   本研究訪談台北市39位國中資訊組長,使用UCINET 6及Pajek 1.19進行資料分析。結論如下: 1.資訊組長之資訊科技實務分享網路明顯大於資訊科技融入教學諮詢網路。 2.在資訊科技實務分享網路與資訊科技融入教學諮詢網路中,「獲得」網路明顯大於「回饋」網路。 3.資訊科技實務回饋網路密度越高,資訊組長學校任教年資越高。 4.教育網路中心於資訊科技實務分享網路與資訊科技融入教學諮詢網路中的中介功能不多。
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    Forum Visualizer: Visualizing an Online Discussion Forum Using Social Network Analysis
    (2004) 賴偉誠
    Virtual communities play an important role in knowledge sharing and learning. Recent research has shown that knowledge management and knowledge sharing in virtual communities are important issues for researchers to study more. In addition, the notation of social networks and the methods of social network analysis have attracted considerable interest from social science research community. In this thesis, we developed Forum Visualizer, a web-based tool for visualizing an online discussion forum at a variety of scales with social network analysis techniques. The participants are 44 students from a class of Information Ethics at NTNU. Our goal is to provide a general, intuitive but useful tool for participants to help them understand the community in which they are a part of and find appropriate forum topics for interaction. These visualizations, therefore, are inherently designed to be interfaces for participants in the community rather than for observers, managers, administrators, etc.; their function is to provide a colorful sense of this abstract space, rather than to accurately depict its statistical features. Besides the graphs/sociograms we provide, data matrices are included to help them understand their sociograms easily and to be as a comparison for mapping their social patterns. We hope that this tool can help build a framework for future directions that every online discussion forum could apply to it as embedding functions for analyzing human interactions, finding great knowledge sharing methods, and exploring social patterns and structures.