Creative Model Construction in Scientists and Students

Introduction: A "Hidden World" Of Nonformal Expert Reasoning 1.1 Why Study Nonformal Reasoning? 1.2 The Background From Which I Approached This Work 1.3 Methodology: Qualitative Nature Of The Study 1.4 General Features Of The Descriptive Methodology Used 1.5 General Theoretical Framework 1.6 Section Summaries And Approaches To Reading This Book PART ONE: ANALOGIES, MODELS AND CREATIVE LEARNING IN EXPERTS AND STUDENTS SECTION I: EXPERT REASONING AND LEARNING VIA ANALOGY 2 Major Subprocesses Involved in Spontaneous Analogical Reasoning 2.1 Some Major Issues in Analogical Reasoning 2.2 Method of Study 2.3 Initial Observations from Transcripts 2.4 Major processes used in analogical reasoning 2.5 Conclusion 3 Methods Experts Use to Generate Analogies 3.1 Introduction 3.2 Definitions of Basic Concepts and Observations 3.3 Discussion 4 Methods Experts Use to Evaluate an Analogy Relation 4.1 The Importance of Establishing the Validity of an Analogy Relation 4.2 Examples from Case Studies 4.3 Analogy Evaluation in the Doughnut Problem 4.4 Discussion of Findings and Connections to History of Science 4.5 Summary 5 Expert Methods for Developing an Understanding of the Analogous Case and Transferring Findings 5.1 Evaluating and Developing Understanding of the Analogous Case. 5.2 Transferring Findings 5.3 Summary on Creative Analogy Generation SECTION II: MODEL CONSTRUCTION AND SCIENTIFIC INSIGHT IN EXPERTS 6 Case Study of Model Construction Cycles in Expert Reasoning 6.1 Introduction 6.2 Background Questions from Philosophy of Science 6.3 How Are Theoretical Hypotheses Formed in the Individual Scientist? 6.4 Protocol Evidence on Construction Cycles That Use Analogies 6.5 Summary of Evidence For A Model Construction Cycle as A Non-Inductive Source for Hypotheses 6.6 Major Nonformal Reasoning Patterns in the Preceding Chapters 7 Creativity and Scientific Insight in the Case Study for S2 7.1 Eureka or Accretion? The Presence of Insight in S2's Protocol 7.2 Creative Mental Processes 7.3 Darwin's Theory of Natural Selection 7.4 Initial List of Features of Creative Thinking from this Case Study and Remaining Challenges SECTION III: NONFORMAL REASONING IN STUDENTS AND IMPLICATIONS FOR INSTRUCTION 8 Spontaneous Analogies Generated by Students Solving Science Problems 8.1 Use of Analogies by Students 8.2 Conclusion 8.3 Appendix: Examples of Problems and Spontaneous Analogies 9 Case Study of a Student who Counters and Improves his own Misconception by Generating a Chain of Analogies 9.1 Spontaneous Analogies in a Student's Problem Solution 9.2 Conclusion 10 Using Analogies and Models in Instruction to Deal with Students Preconceptions 10.1 Introduction 10.2 Teaching Strategy 10.3 Teaching Interviews 10.4 Applications to Classroom Teaching 10.5 Conclusion 10.5.2 Explanatory Models vs. Specific Analogous Cases PART TWO: ADVANCED USES OF IMAGERY AND PLAUSIBLE REASONING IN SCIENCE AND MATHEMATICS SECTION IV: IMAGERY AND PHYSICAL INTUITION IN EXPERTS AND STUDENTS 11 Analogy, Extreme Cases, and Spatial Transformations in Mathematical Problem Solving by Experts 11.1 Introduction 11.2 Case Study of Analogical Reasoning in a Mathematics Problem 11.3 Results on the Use of Analogies for Eight Subjects 11.4 Other Creative Non-formal Reasoning Processes 11.5 Discussion 11.6 Conclusion 12 Depictive Gestures and Other Case Study Evidence for Mental Simulation in Experts and Students 12.1 Introduction 12.2 Constructing Observational and Theoretical Descriptors 12.3 Case Studies 12.4 Discussion 12.4.1 Types of Processes Associated with Motioning 12.4.2 Can Depictive Hand Motions Be a Direct Product of Imagery? 12.4.3 Summary of Relations between Observations and Hypotheses 12.5 Relationship of These Findings to Others in the Literature 12.5.1 The Existence of Kinesthetic Imagery 12.5.2 Depictive Motions Are Not Simply Translated from Sentences 12.5.3 Movements are a Partial Reflection of Core Meaning or Reasoning 12.5.4 Gestures Can Reflect Imagery 12.6 Conclusion 12.6.1 Hand Motions as a Source of Information about Imagery and Mental Simulations 12.6.2 Limitations 12.7 Appendix to Chapter 12 - Detailed Analysis of Evidence for Imagery from Hand Motions in S15's Protocol 12.7.1 Motions Can Be a Direct Product of Solution Process 12.7.2 Motions are not Translated from Verbal Sentences 12.7.3 Evidence for Imagery 13 Physical Intuition, Imagistic Simulation and Implicit Knowledge 13.1 Introduction: Issues in the Area of Imagery, Simulation and Physical Intuition 13.2 Initial Examples of Physical Intuition 13.3 Imagery Reports and Imagistic Simulation 13.4 Implicit Knowledge 13.5 Knowledge Can Be Dynamic 13.6 Conclusion: The Role of Concrete Physical Intuitions and Simulations in Expert Thinking SECTION V: ADVANCED USES OF IMAGERY IN ANALOGIES, THOUGHT EXPERIMENTS, AND MODEL CONSTRUCTION 14 The Use of Analogies, Imagery, and Thought Experiments in both Qualitative and Quantitative Model Construction 14.1 Introduction 14.2 Monologue 14.3 Stages in the Solution 14.4 Explanatory Models vs. Expedient Analogies 14.5 Conclusion 15 Thought Experiments and Imagistic Simulation in Plausible Reasoning 15.1 Nature of Thought Experiments 15.2 Addressing the Thought Experiment Paradox: How Can an Untested Thought Experiment Generate Findings with Conviction? 15.3 Imagery Enhancement Phenomena Support The Proposed Answer To the Paradox 15.4 How Are Thought Experiments Used Within More Complex Reasoning Modes? 15.5 Are Imagistic Simulations Operating in the Mathematical Part of the Solution? 15.6 How Thought Experiments Contribute to Model Evaluation 15.7 Chapter Summary 16 An Evolutionary Model of Investigation and Model Construction Processes 16.1 Abductive Processes for Generating and Modifying Models 16.2 Investigation Processes 16.3 Quantitative Modeling 16.4 Abduction II: How Evaluation Processes Complement Generative Abduction. 16.5 Seeking an Optimal Level of Divergence 16.6 Chapter Summary 17 Imagistic Processes in Analogical Reasoning: Transformations and Dual Simulations 17.1 Two Precedents from the Literature 17.2 Conserving Transformations 17.3 Conserving Transformations In Science 17.4 Dual Simulation 17.5 Overlay Simulation 17.6 Summary and Discussion of Types of Evaluation Methods and Evaluation Process: Contrasting Mechanisms for Determining Similarity 17.7 Use of Imagistic Transformations During the Generation of Partitions, Analogies, Extreme Cases, and Explanatory Models 17.8 Conclusion 18 How Grounding in Runnable Schemas Contributes to Producing Flexible Scientific Models in Experts and Students 18.1 Introduction: Does Intuitive Anchoring Lead to Any Real Advantages? 18.2 Cognitive Benefits of Anchoring and Runnability for Models 18.4 Conclusion SECTION VI: CONCLUSIONS 19 Summary of Findings on Plausible Reasoning and Learning in Experts I: Basic Findings 19.1 Overview of the Book 19.2 Analogy Findings Part I 19.3 Model Construction Findings Part I and Initial Connections to General Issues in History/Philosophy of Science 19.4 Imagistic Simulation Findings Part I 19.4.4 Connection to Experiments and Situated Action 20 Summary of Findings on Plausible Reasoning and Learning in Experts* II: Advanced Topics 20.1 Analogy Findings, Part II 20.2 Imagistic Simulation Findings Part II: Thought Experiments and their Uses in Plausible Reasoning 20.3 Model Construction Findings Part II: An Evolutionary Model of Investigation Processes 20.4 The Important Role of Imagery in the Expert Investigations 20.5 Transfer of Runnability Leads to Outcomes of Flexible Model Application and Generativity 20.6 Comments On Methodology 21 Creativity in Experts, Nonformal Reasoning, and Educational Applications 21.1 Summary Of The Overall Framework 21.2 How Experts Used Creativity Effectively 21.3 Educational Applications: Needed Additions to the Classical Theory of Conceptual Change in Education 21.4 Expert-Novice Similarities in Non-Formal Reasoning and Learning 21.5 Implications For Instructional Strategies And Theory 21.6 Are Creative Processes in Experts a Natural Extension of Everyday Thinking? 21.7 Assessing The Potential For a Model of Creative Theory Construction in Science 21.8 Conclusion