Assessing science understanding : a human constructivist view

목차 (Table of Contents)

• CONTENTS
• Contributors = xvii
• Preface = xix
• 1. LEARNING, TEACHING, AND ASSESSMENT : A HUMAN CONSTRUCTIVIST PERSPECTIVE / Joseph D. Novak ; Joel J. Mintzes ; lames H. Wandersee
• The Role of Assessment = 1
• The Learner = 2
• The Teacher = 7
• Knowledge and Knowledge Creation = 8
• The Social Milieu = 11
• A Foreword = 13
• References = 13
• 2. ASSESSING SCIENCE UNDERSTANDING THROUGH CONCEPT MAPS / Katherine M. Edmondson
• Concept Mapping to Portray Shared Meaning and Meaningful Learning = 19
• Concept Maps as Assessment Tools = 22
• Cases in Point : Assessing Shared Meaning in Specific Disciplines or Content Domains with a View to the Larger World = 30
• Summary and Conclusions = 35
• References = 36
• 3. ASSESSING SCIENCE UNDERSTANDING : THE EPISTEMOLOGICAL VEE DIAGRAM / Joel J. MiMtzcs ; Josefph D. Novak
• Understanding Understanding = 42
• Sharing Meanings （Intersubjectivity） = 43
• Resolving Inconsistencies （Coherence） = 44
• Seeking Simplicity （Parsimony） = 46
• Thinking Critically （Transparency） = 47
• Introducing Gowin's V Diagram = 47
• Focus Question = 49
• Objects and Events = 50
• Conceptual （Thinking） Side = 50
• Methodological （Doing） Side = 52
• V Diagrams in College Biology = 54
• Template and Rubric = 54
• Blaine = 57
• Melodie = 57
• corrie = 60
• Comments and Reflections = 60
• V Diagrams in College Biology = 60
• Improvement of Laboratory Study Guides = 62
• V Diagrams in Junior High School = 64
• The Need for Greater Epistemological Understanding = 66
• References = 68
• 4. "WHAT DO YOU MEAN BY THAT?" : USING STRUCTURED INTERVIEWS TO ASSESS SCIENCE UNDERSTANDING / Sherry A. Southerland ; Mike U. Smith ; and Catherine L. Cummins
• The Need for Qualitative Assessment Tools = 72
• What Is a Structured Interview? = 73
• Structured Interview Tasks = 73
• Interviews about Instances = 74
• Prediction Interviews = 76
• Sorting Interviews = 79
• Problem­Solving and Process Interviews = 82
• Conducting Structured Interviews = 84
• Practicalities of Preparation = 84
• Student Comfort = 84
• Timeframe = 85
• Prompts, FoIlow­Up Questions, and Interviewer Comments = 85
• Pilot Studies = 87
• Potential for Formal Evaluation = 88
• How to Make Sense of Interview Responses = 89
• Issues to Consider in the Use of Structured Interviews = 90
• Mode Validity = 90
• Content Specificity = 91
• Using Structured Interviews to Inform Teaching = 91
• References = 92
• 5. DIALOGUE AS DATA : ASSESSING STUDENTS' SCIENTIFIC REASONING WITH INTERACTIVE PROTOCOLS / Kathleen Hogan ; JoEllen Fisherkeller
• The Knowledge and Reasoning Connection = 96
• Broadening Conceptualizations of Scientific Reasoning = 97
• Using Verbal Interactions to Assess Scientific Reasoning = 100
• Steps for Assessing Reasoning Through Verbal Interactions in Classroom and Interview Contexts = 102
• Step 1 : Determine a Purpose and Approach = 102
• Step 2 : Establish and Communicate Standards for Reasoning = 104
• Step 3 : Choose Interactive Settings for Assessment = 106
• Step 4 : Choose Tasks for Eliciting and Assessing Reasoning = 110
• Step 5 : Collect Data = 113
• Step 6 : Analyze Data = 115
• Step 7 : Give Feedback or Make Claims = 122
• Challenges, Caveats, and Conclusions = 123
• References = 124
• 6. DESIGNING AN IMAGE­BASED BIOLOGY TEST / James H. Wandersee
• What Can an Image­Based Test Reveal about Biology Learning? = 131
• What Are the Testing Implications of Paivio's Dual Coding Theory? = 132
• What Are Some Salient Principles of Visual Perception and Cognition? = 133
• How Can the Student's Attention be Focused on Selected Aspects of the Photographic Image? = 135
• When Should a Color Image Be Used Instead of a Monochrome Image? = 135
• What Is the Author's Model of Image­Based Biology Test­Item Design? = 136
• What Are Some Examples of Model­Based Test Items? = 138
• How Can Image­Based Biology Test Results Be Analyzed and Interpreted? = 138
• References = 142
• 7. OBSERVATION RUBRICS IN SCIENCE ASSESSMENT / John E. Trowbridge ; James H. Wandersee
• Observation in the Naturalist Tradition = 147
• Agassiz' Biology Teaching Legacy = 147
• Science Advances Through Observation = 149
• Notable Scientists Who Use Observations as a Basis for Their Work = 150
• Pedagogy and the New Standards = 152
• Theory­Ladenness of Observation = 154
• Teaching the Power of Direct Observation = 154
• Complementary Sets of Criteria to Assess the Quality of Observation = 154
• Making Observations Well­The Norris Criteria = 154
• Reporting Observations Well­The Norris Criteria = 155
• Assessing Reports of Observations­The Norris Criteria = 155
• Levels of Observational Competency­The ESC Criteria = 156
• Human Vision = 157
• Filtering of Human Vision = 157
• Journals = 158
• Constructing Observation Rubrics = 158
• Teaching How to Make Good Observations = 161
• Teaching How to Report Observations Well = 163
• Teaching How to Assess Observations Well = 163
• Conclusion = 164
• References = 165
• 8. PORTFOLIOS IN SCIENCE ASSESSMENT : A KNOWLEDGE­BASED MODEL FOR CLASSROOM PRACTICE / Michael R. Vitale ; Nancy R. Romance
• Portfolios and Assessment in Science = 168
• Portfolios and Science Assessment = 168
• Portfolios in Science Classrooms and Assessment Methodology = 169
• Recognizing Other Instructional Uses of Portfolios in Science Teaching = 170
• Summary of Potential Benefits and Problems with Portfolio Assessment in Science = 171
• Limitations of the Chapter on the Scope of Portfolio Assessment = 171
• A Cognitive Science Perspective on Knowledge, Learning, and Assessment = 172
• Core Concepts and Concept Relationships as a Framework for Science Understanding = 172
• A Methodological Perspective on Science Knowledge and Understanding = 173
• Concept Mapping as a Practical Strategy for Knowledge Representation in Science Classrooms = 175
• Knowledge­Based Architectures for Science Teaching and Assessment = 176
• A Knowledge­Based Portfolio Assessment Model = 182
• Additional Perspectives Relating to Knowledge­Based Assessment = 182
• A Model for Knowledge­Based Portfolio Assessment = 184
• Some Elaborations of the Basic Model for Knowledge­Based Portfolio Assessment = 187
• Planning and Implementation Guidelines for Knowledge­Based Portfolio Assessment = 188
• Examples Illustrating the Knowledge­Based Portfolio Assessment Model = 189
• Formal and Informal Processes for Developing Knowledge­Based Portfolio Tasks = 189
• Selected Examples of Knowledge­Based Portfolio Tasks = 190
• Some Variations on the Knowledge­Based Portfolio Examples = 192
• Implications of the Knowledge­Based Model for Science Teachers and Researchers = 193
• References = 194
• 9. SemNet SOFTWARE AS AN ASSESSMENT TOOL / Kathleen M. Fisher
• What Is the SemNet Software? = 198
• What Is Meaningful Conceptual Understanding in Science? = 201
• How Can Meaningful Conceptual Understanding Be Assessed? = 202
• Using SemNet as an Assessment Tool = 203
• Nature of Relations = 204
• Generative Assessments with SemNet = 204
• Diagnostic Assessment = 205
• Embedded Assessments = 206
• Summative Assessment = 206
• Assessment of Prior Knowledge = 207
• Strategies for Evaluating Student­Generated Semantic Networks = 207
• Overview­About Net = 208
• Quality of Relations = 209
• Assessing Content = 211
• Quality of Concept Descriptions = 211
• Identifying Main Ideas­Concepts by Embeddedness = 212
• Hierarchies and Temporal Flows = 213
• Peer Review of Nets = 213
• Assessing Contributions of Members of a Group = 213
• Incorporating SemNet­Based Assessment into Printed Tests = 214
• Assessing Knowledge about Main Ideas = 214
• Assessing Knowledge about Details = 215
• Assessing Knowledge about Relations = 215
• Assessing Ability to Make Fine Discriminations = 216
• Summary : A Vision for the Future = 217
• References = 219
• 10. WRITING TO INQUIRE : WRITTEN PRODUCTS AS PERFORMANCE MEASURES / Audrey B. Champagne ; Vicky L. Kouba
• Definitions, Assumptions, and Perspectives = 224
• Assessment = 225
• Performance Assessment = 225
• Opportunity to Lean = 226
• Performance Expectations = 226
• Inquiry = 227
• The Learning Environment = 229
• Discourse in the Science Classroom = 232
• Evidence That Learning Has Occurred = 233
• Writing to Inquire = 234
• Writing to Develop Community Norms = 236
• The Teacher = 237
• Theoretical Perspective = 237
• Social Constructivism = 237
• Reflective Thinking = 238
• Rhetoric = 239
• Performance Expectations = 239
• Strategies for Developing Performance Expectations = 240
• An Example of the Process of Developing Performance Expectations = 241
• National Science Education Science as Inquiry Standard = 241
• Interpreting the Standard and Descriptions of the Abilities and Knowledge = 242
• Elaboration of the Abilities and Knowledge = 244
• Conclusions = 246
• References = 246
• 11. THE RELEVANCE OF MULTIPLE­CHOICE TESTING IN ASSESSING SCIENCE UNDERSTANDING / Philip M. Sadler
• Background Issues = 251
• History of Multiple­Choice Tests = 251
• Standardized Tests = 252
• Underlying Theories = 254
• The Origins of Testing for Preconceptions = 255
• Test Construction = 257
• Item Construction = 257
• Examples of Constructivist Tests in Science = 258
• Pychometric Tools = 259
• Item Response Theory = 259
• Analysis of Two Items and the J―Curve = 260
• Test Characteristics = 264
• Measuring Conceptual Change = 265
• Placement along a Continuum = 265
• Curriculum Maps = 267
• Cognitive Range = 268
• Logical Networks = 270
• Implications = 272
• References = 274
• 12. NATIONAL AND INTERNATIONAL ASSESSMENT / Pinchas Tamir
• Student Assessment = 279
• Curriculum Reform and Student Assessment = 280
• A Balanced Student Assessment = 280
• Types of Assessment = 280
• National Assessment = 281
• Scoring = 281
• The Meaning of Scores = 283
• Scoring Key : An Example = 284
• International Assessment = 285
• A Brief Introduction to TIMSS = 285
• Conceptual Framework for TIMSS = 286
• TIMSS Curriculum Framework = 287
• Who Was Tested? = 288
• Data Collection = 288
• Rigorous Quality Control = 288
• United States = 289
• United Kingdom = 290
• France = 291
• Germany = 291
• Israel = 292
• Science Education : International = 293
• Teachers' Autonomy = 293
• Freedom Built into the System （Not Specific to Biology） = 293
• Particular Features of the Biology Program = 294
• Empowering Teachers = 295
• Differences between SISS and TIMSS = 296
• Selected Findings = 298
• Conclusions 299
• References 300
• Bibliography 301
• 13. ON THE PSYCHOMETRICS OF ASSESSING SCIENCE UNDERSTANDING / Richard J. Shavelson ; Maria Araceli Ruiz­Primo
• Sampling Framework for Evaluating Alternative Science Achievement Tests = 304
• Psychometric Approaches to Modeling Science Achievement Scores = 306
• Classical Reliability Theory = 307
• Generalizability Theory = 307
• Item Response Theory = 308
• A Sketch of Generalizability Theory = 309
• Relative and Absolute Decisions = 313
• Measurement Error = 313
• Estimation of Generalizability and Dependability Coefficients = 314
• Generalizability and Decision Studies = 315
• Validity of Proposed Interpretations of Assessment Scores = 316
• Evaluation of Alternative Assessments : Examples and Summary of Findings = 317
• Concept Maps = 318
• Performance Assessment = 330
• Concluding Comments = 337
• References = 338
• 14. CAUTIONARY NOTES ON ASSESSMENT OF UNDERSTANDING SCIENCE CONCEPTS AND NATURE OF SCIENCE / Ronald G. Good
• Defining Understanding : Expert­Novice Studies = 344
• Assessing Understanding of Science Concepts = 346
• Assessment as Problem Solving = 346
• Assessment as Concept Mapping = 347
• Assessment as Prediction = 348
• Assessing Beliefs about Science and Scientists = 349
• NOS Assessment : Problems and Prospects = 349
• Reasonable Assessment Expectations for Science Teachers = 351
• References = 353
• 15. EPILOGUE : ON WAYS OF ASSESSING SCIENCE UNDERSTANDING / Joseph D. Novak ; Joel J. Mintzes ; James H. Wandersee
• Assessing Science Understanding : A Summary of Tools, Techniques, and Ideas = 355
• Learning, Teaching, and Assessment : A Human Constructivist View = 359
• Assessing Assessment and Valuing Student Work = 366
• Assessing Assessment = 366
• Valuing Student Work = 369
• Windows on the Mind : Concluding Remarks = 370
• References = 373
• index = 375