可視化教師隱性知識之知識地圖輔助評量系統(英文版)
內容大鋼
長期以來，電腦或信息科技扮演著輔助教師處理試卷出題工作的重要角色，教師可以使用教材出版商所附之題庫光碟或在線題庫協助其進行教育試題的編製，然而，在教師出題的過程中，常發生教師忽略掉重要的課程概念或出題比重難以拿捏的問題。蘇建元編著的《可視化教師隱性知識之知識地圖輔助評量系統(英文版)》提出一個新的方法，利用知識地圖賦予概念權重的方式，實作出一個能輔助小學教師進行出題的評量系統，當教師進行出題時，該能確保並建構一個適當的概念與試題間的權重比例，做為教師的出題參考，首先借由分析課程的內容架構，並得出概念之間的重要關係，並且透過可視化的知識地圖進行呈現，接著教師除了能先行匯入所設計的電子試題來了解試題概念的比例分配之外，陸續收集來的相關電子試題，將提供系統進行自動試題分類與學習概念的粹取，使教師能進一步借由所設定之施測範圍來取得試題，形成學習概念節點的知識地圖，讓教師依照知識地圖所呈現的學習概念比重以及概念間的關連權重來判別並挑選出合適學習者的試題內容。該系統透過評估學習概念粹取的正確性、以及利用問卷方式取得教師對系統使用的滿意度進行調查，結果證明該系統確能有效的幫助教師進行出題，此外，借由形成性評估的方式能有效且持續的改善系統的運作與執行，而本研究所提之運作方法與出題系統的設計皆能有效且彈性地移轉與應用到其他的試題內容之中。
作者介紹
蘇建元
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.
目錄
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
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