This book directly addresses the topic of â next generationâ assessment design head-on by proposing a new perspective, a new understanding, of the challenge of designing, developing and implementing large (and small) scale educational testing programs.
This book directly addresses the topic of â next generationâ assessment design head-on by proposing a new perspective, a new understanding, of the challenge of designing, developing and implementing large (and small) scale educational testing programs.
Richard M. Luecht is a Professor Emeritus of Educational Research Methodology at UNC-Greensboro. He has designed numerous algorithms and software programs for automated test assembly and devised a computerized adaptive multistage testing framework used by several large-scale testing programs.
Inhaltsangabe
1. An Overview of Assessment Engineering 1.1. Some Definitions of AE 1.2. Limitations of Traditional Test Design 1.2.1. A Test Assembly Example 1.2.2. An AE Perspective on Test Design and Specifications 1.3. AE for Comprehensive Test Design Aligned to Proficiency Claims and Score Interpretations 1.4. Chapter Summary 2. Construct Mapping and Evidence Modeling 2.1. What Are Constructs? 2.2. Limitations of Traditional Score Scale Construction and Their Interpretations 2.2.1. Traditional Development of a Score Scale 2.2.2. Content Blueprints and Standards-Based Alignment 2.2.3. Achievement Level Descriptors and Standard Setting 2.2.4. Item Mapping 2.2.5. Limitations of Traditional Blueprints, Standard Setting, and Item Mapping 2.3. Evidence-Centered Design 2.4. Construct Mapping: An Ordered Progression of Proficiency Claims and Evidence 2.4.1. Choosing a Construct Trajectory 2.4.2. Evidence Models and Proficiency Claims as Building Blocks 2.5. Creating a Construct Map 2.6. Chapter Summary 3. Task Models and Task Model Families 3.1. What Is a Task Model? 3.2. Task Modeling and Cognitive Complexity 3.2.1. Task Model Grammars and the Structure of Task Models 3.2.2. Graphical Representations of Task Model Complexity 3.3. Item Scale Location (Difficulty) and Task Complexity 3.4. Complexity Design Layers 3.5. Chapter Summary 4. Task and Item Difficulty Modeling 4.1. The Need for IDM Research 4.2. Isomorphism and Composability in IDM Research 4.3. Characterizing Statistical Item Difficulty for IDM Research 4.4. Foundations of IDM Research 4.4.1 The Roles of Item Features and Complexity Design Layers 4.4.2 Some Methods for Statistically Modeling Item Difficulty 4.5. Phases in Implementing IDM 4.6. Chapter Summary 5. Task Model Mapping 5.1. Some Limitations of Traditional Test Blueprints and Test Assembly Methods 5.1.1. Conditional Measurement Precision in Test Design and Assembly 5.1.2. The Consequences for Test Assembly of Using Fallible of Content- and Cognitive-Coded Constraints 5.2. Building Task Model Maps (TMMs) 5.3. The Alignment of Complexity and Measurement Precision with a TMM 5.4. Chapter Summary 6. Item Model Families and Automatic Item Generation 6.1. Components and Types of Automatic Item Generation 6.1.1. AI and GPT Applications for AIG 6.1.2. Some Limitations of AIG 6.2. AE-Based Item Models 6.2.1. Item Model Structures and Content 6.2.2. Item Model Quality Control 6.3. Chapter Summary 7. AE Analytics and Quality Control 7.1. Object Analytics 7.1.1. Text Analytics 7.1.2. Image Analytics 7.1.3. Analytics for Audio/Visual Segments 7.1.4. Analytics for Tables and Tabular Data 7.1.5. Analytics for Mathematical Expressions and Equations 7.1.6. Analytics for Numbers/Numerical Sets 7.1.7. Analytics for Item Structures 7.2. Complexity Scoring Protocols 7.3. Psychometric QC Using Conditional Residual Analyses 7.4. An Object Analytic Architecture 7.5. Chapter Summary 8. AE Implementation and Future Directions 8.1. What Problems Does AE Actually Solve? 8.2. Implementing AE: A System of Integrated Systems 8.2.1. Versioning and Robust Integration 8.2.2. Quality Improvement Metrics 8.2.3. High-Level Procedures and Systems 8.3. Future Directions References
1. An Overview of Assessment Engineering 1.1. Some Definitions of AE 1.2. Limitations of Traditional Test Design 1.2.1. A Test Assembly Example 1.2.2. An AE Perspective on Test Design and Specifications 1.3. AE for Comprehensive Test Design Aligned to Proficiency Claims and Score Interpretations 1.4. Chapter Summary 2. Construct Mapping and Evidence Modeling 2.1. What Are Constructs? 2.2. Limitations of Traditional Score Scale Construction and Their Interpretations 2.2.1. Traditional Development of a Score Scale 2.2.2. Content Blueprints and Standards-Based Alignment 2.2.3. Achievement Level Descriptors and Standard Setting 2.2.4. Item Mapping 2.2.5. Limitations of Traditional Blueprints, Standard Setting, and Item Mapping 2.3. Evidence-Centered Design 2.4. Construct Mapping: An Ordered Progression of Proficiency Claims and Evidence 2.4.1. Choosing a Construct Trajectory 2.4.2. Evidence Models and Proficiency Claims as Building Blocks 2.5. Creating a Construct Map 2.6. Chapter Summary 3. Task Models and Task Model Families 3.1. What Is a Task Model? 3.2. Task Modeling and Cognitive Complexity 3.2.1. Task Model Grammars and the Structure of Task Models 3.2.2. Graphical Representations of Task Model Complexity 3.3. Item Scale Location (Difficulty) and Task Complexity 3.4. Complexity Design Layers 3.5. Chapter Summary 4. Task and Item Difficulty Modeling 4.1. The Need for IDM Research 4.2. Isomorphism and Composability in IDM Research 4.3. Characterizing Statistical Item Difficulty for IDM Research 4.4. Foundations of IDM Research 4.4.1 The Roles of Item Features and Complexity Design Layers 4.4.2 Some Methods for Statistically Modeling Item Difficulty 4.5. Phases in Implementing IDM 4.6. Chapter Summary 5. Task Model Mapping 5.1. Some Limitations of Traditional Test Blueprints and Test Assembly Methods 5.1.1. Conditional Measurement Precision in Test Design and Assembly 5.1.2. The Consequences for Test Assembly of Using Fallible of Content- and Cognitive-Coded Constraints 5.2. Building Task Model Maps (TMMs) 5.3. The Alignment of Complexity and Measurement Precision with a TMM 5.4. Chapter Summary 6. Item Model Families and Automatic Item Generation 6.1. Components and Types of Automatic Item Generation 6.1.1. AI and GPT Applications for AIG 6.1.2. Some Limitations of AIG 6.2. AE-Based Item Models 6.2.1. Item Model Structures and Content 6.2.2. Item Model Quality Control 6.3. Chapter Summary 7. AE Analytics and Quality Control 7.1. Object Analytics 7.1.1. Text Analytics 7.1.2. Image Analytics 7.1.3. Analytics for Audio/Visual Segments 7.1.4. Analytics for Tables and Tabular Data 7.1.5. Analytics for Mathematical Expressions and Equations 7.1.6. Analytics for Numbers/Numerical Sets 7.1.7. Analytics for Item Structures 7.2. Complexity Scoring Protocols 7.3. Psychometric QC Using Conditional Residual Analyses 7.4. An Object Analytic Architecture 7.5. Chapter Summary 8. AE Implementation and Future Directions 8.1. What Problems Does AE Actually Solve? 8.2. Implementing AE: A System of Integrated Systems 8.2.1. Versioning and Robust Integration 8.2.2. Quality Improvement Metrics 8.2.3. High-Level Procedures and Systems 8.3. Future Directions References
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