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可视化教师隐性知识之知识地图辅助评量系统:英文 版权信息
- ISBN：9787308139212
- 条形码：9787308139212 ; 978-7-308-13921-2
- 装帧：一般胶版纸
- 册数：暂无
- 重量：暂无
- 所属分类：>
可视化教师隐性知识之知识地图辅助评量系统:英文 本书特色
长期以来，计算机或信息科技扮演着辅助教师处理试卷出题工作的重要角色，教师可以使用教材出版商所附之题库光盘或在线题库协助其进行教育试题的编制，然而，在教师出题的过程中，常发生教师忽略掉重要的课程概念或出题比重难以拿捏的问题。苏建元编著的《可视化教师隐性知识之知识地图辅助评量系统(英文版)》提出一个新的方法，利用知识地图赋予概念权重的方式，实作出一个能辅助小学教师进行出题的评量系统，当教师进行出题时，该能确保并建构一个适当的概念与试题间的权重比例，做为教师的出题参考，首先借由分析课程的内容架构，并得出概念之间的重要关系，并且透过可视化的知识地图进行呈现，接着教师除了能先行汇入所设计的电子试题来了解试题概念的比例分配之外，陆续收集来的相关电子试题，将提供系统进行自动试题分类与学习概念的粹取，使教师能进一步借由所设定之施测范围来取得试题，形成学习概念节点的知识地图，让教师依照知识地图所呈现的学习概念比重以及概念间的关连权重来判别并挑选出合适学习者的试题内容。该系统透过评估学习概念粹取的正确性、以及利用问卷方式取得教师对系统使用的满意度进行调查，结果证明该系统确能有效的帮助教师进行出题，此外，借由形成性评估的方式能有效且持续的改善系统的运作与执行，而本研究所提之运作方法与出题系统的设计皆能有效且弹性地移转与应用到其他的试题内容之中。
可视化教师隐性知识之知识地图辅助评量系统:英文 内容简介
长期以来，计算机或信息科技扮演着辅助教师处理试卷出题工作的重要角色，教师可以使用教材出版商所附之题库光盘或在线题库协助其进行教育试题的编制，然而，在教师出题的过程中，常发生教师忽略掉重要的课程概念或出题比重难以拿捏的问题。本书提出一个新的方法，利用知识地图赋予概念权重的方式，实作出一个能辅助小学教师进行出题的评量系统，当教师进行出题时，该能确保并建构一个适当的概念与试题间的权重比例，做为教师的出题参考，首先借由分析课程的内容架构，并得出概念之间的重要关系，并且透过可视化的知识地图进行呈现，接着教师除了能先行汇入所设计的电子试题来了解试题概念的比例分配之外，陆续收集来的相关电子试题，将提供系统进行自动试题分类与学习概念的粹取，使教师能进一步借由所设定之施测范围来取得试题，形成学习概念节点的知识地图，让教师依照知识地图所呈现的学习概念比重以及概念间的关连权重来判别并挑选出合适学习者的试题内容。该系统透过评估学习概念粹取的正确性、以及利用问卷方式取得教师对系统使用的满意度进行调查，结果证明该系统确能有效的帮助教师进行出题，此外，借由形成性评估的方式能有效且持续的改善系统的运作与执行，而本研究所提之运作方法与出题系统的设计皆能有效且弹性地移转与应用到其他的试题内容之中。
可视化教师隐性知识之知识地图辅助评量系统:英文 目录
preface
acknowledgements
chapter 1 introduction
1.1 background and motivation
1.2 goals and contributions
1.3 organization of this book
chapter 2 the role of assessment in classroom
chapter 3 assessment, course concepts and items
chapter 4 computer-assistedassessment system and tools
4.1 computer-based assessment (cba) and computer-assisted
assessment (caa)
4.2 existing computer-assistedassessment systems
chapter 5 implicit assessment knowledge of teachers
chapter 6 knowledge and knowledge management
6.1 tacit knowledge and explicit knowledge
6.2 knowledge management
6.3 reusing tacit knowledge
chapter 7 knowledge representation and visualization /49
7.1 knowledge representation
7.2 knowledge visualizations
chapter 8 knowledge map
8.1 the type of knowledge map
8.2 various applications of knowledge maps
8.3 knowledge map development and construction
8.4 automated construction of knowledge map
chapter 9 test item categorization
chapter 10 clustering technology and clustering in education
application
10.1 clustering technology
10.2 k-means and k-medoids
10.3 clustering in education application
10.4 teacher clusters
chapter 11 personalization and item recommendation
11.1 personalization
11.2 recommendation systems
11.3 item recommendation
chapter 12 knowledge map assisting assessment system
12.1 the kmaas architecture and the methodology
12.2 kmaas implementation and operations
12.4 experiment design
12.5 evaluation and discussion of kmaas
12.6 limitations of the evaluation of kmaas
chapter 13 internet-based knowledge map assisting assessment
system
13.1 knowledge accumulation and visualization methodology
13.2 ikmaas architecture
13.3 ikmaas implementation and operations
13.4 experiment design
13.5 evaluation and discussion of ikmaas
chapter 14 item recommendation uses the teacher cluster-based
collaborative filtering approach
14.1 clustering teacher's assessment knowledge
14.2 item recommendation methodology
14.3 the mechanism of personalized item recommendation in
ikmaas
14.4 experimental design
14.5 results and discussion
chapter 15 summary and future work
reference
subject index
acknowledgements
chapter 1 introduction
1.1 background and motivation
1.2 goals and contributions
1.3 organization of this book
chapter 2 the role of assessment in classroom
chapter 3 assessment, course concepts and items
chapter 4 computer-assistedassessment system and tools
4.1 computer-based assessment (cba) and computer-assisted
assessment (caa)
4.2 existing computer-assistedassessment systems
chapter 5 implicit assessment knowledge of teachers
chapter 6 knowledge and knowledge management
6.1 tacit knowledge and explicit knowledge
6.2 knowledge management
6.3 reusing tacit knowledge
chapter 7 knowledge representation and visualization /49
7.1 knowledge representation
7.2 knowledge visualizations
chapter 8 knowledge map
8.1 the type of knowledge map
8.2 various applications of knowledge maps
8.3 knowledge map development and construction
8.4 automated construction of knowledge map
chapter 9 test item categorization
chapter 10 clustering technology and clustering in education
application
10.1 clustering technology
10.2 k-means and k-medoids
10.3 clustering in education application
10.4 teacher clusters
chapter 11 personalization and item recommendation
11.1 personalization
11.2 recommendation systems
11.3 item recommendation
chapter 12 knowledge map assisting assessment system
12.1 the kmaas architecture and the methodology
12.2 kmaas implementation and operations
12.4 experiment design
12.5 evaluation and discussion of kmaas
12.6 limitations of the evaluation of kmaas
chapter 13 internet-based knowledge map assisting assessment
system
13.1 knowledge accumulation and visualization methodology
13.2 ikmaas architecture
13.3 ikmaas implementation and operations
13.4 experiment design
13.5 evaluation and discussion of ikmaas
chapter 14 item recommendation uses the teacher cluster-based
collaborative filtering approach
14.1 clustering teacher's assessment knowledge
14.2 item recommendation methodology
14.3 the mechanism of personalized item recommendation in
ikmaas
14.4 experimental design
14.5 results and discussion
chapter 15 summary and future work
reference
subject index
展开全部
可视化教师隐性知识之知识地图辅助评量系统:英文 作者简介
Chien-Yuan Su is currently an assistant professor in the Institute of Curriculum and Instruction (Institute of Educational Technology). In the past, he served a full-time postdoctoral in the Department of Engineering Science at Cheng Kung University, Taiwan, China. He received the M.S. in Department of Information and Learning Technology from University of Tainan in 2006; and acquired Ph.D. in Department of Engineering Science at Cheng Kung University in 2012. His major interests in the research field includes e- learning, learning and technology, e-Learning pedagogical strategies, developing e-Learning for specific subject domains and e-assessment.
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