Khine / Saleh Models and Modeling

Cognitive Tools for Scientific Enquiry
1. Auflage 2011
ISBN: 978-94-007-0449-7
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 6, 292 Seiten

Reihe: Models and Modeling in Science Education

The process of developing models, known as modeling, allows scientists to visualize difficult concepts, explain complex phenomena and clarify intricate theories. In recent years, science educators have greatly increased their use of modeling in teaching, especially real-time dynamic modeling, which is central to a scientific investigation. Modeling in science teaching is being used in an array of fields, everything from primary sciences to tertiary chemistry to college physics, and it is sure to play an increasing role in the future of education. Models and Modeling: Cognitive Tools for Scientific Enquiry is a comprehensive introduction to the use of models and modeling in science education. It identifies and describes many different modeling tools and presents recent applications of modeling as a cognitive tool for scientific enquiry.

Autoren/Hrsg.

Khine, Myint Swe

Saleh, Issa M.

Inhaltsverzeichnis

1;Contents;6
2;Contributors;8
3;Part I Theory Formation and Modeling in Science Education;10
3.1;1 Modeling and the Future of Science Learning;11
3.1.1; Introduction;11
3.1.2; The Nature of Models and Modeling;11
3.1.3; Models, Modeling, and the Nature of Science;14
3.1.4; Models and Modeling in Scientific Enquiry;16
3.1.5; Modeling as a Cognitive Tool;18
3.1.6; Modeling in the Teaching and Learning of Science;20
3.1.7; Future Work;21
3.1.8;References;22
3.2;2 A Study of Expert Theory Formation: The Role of Different Model Types and Domain Frameworks;30
3.2.1; Creating Models;30
3.2.2; A Protocol Study of Expert Theory Formation;32
3.2.3; Analysis of Three Protocols;38
3.2.4; Discussion;42
3.2.5; Conclusion;45
3.2.6;References;46
3.3;3 The Nature of Scientific Meta-Knowledge;48
3.3.1; Introduction;48
3.3.1.1; Perspectives on the Nature of Science;49
3.3.2; Meta-theoretic Knowledge;52
3.3.2.1; Different Types of Models;52
3.3.2.1.1; An Agent Model of Social Interaction (Community Model);54
3.3.2.2; Purposes of Different Models;55
3.3.2.3; Creating Models;56
3.3.2.4; Characteristics of a Good Model;57
3.3.2.5; How Different Models Fit Together;58
3.3.3; Meta-questioning Knowledge;59
3.3.3.1; Different Types of Research Questions;59
3.3.3.2; Purposes for Research Questions;61
3.3.3.3; Criteria for Good Research Questions;62
3.3.3.4; Generating Research Questions;62
3.3.3.5; How Research Questions Fit Together;63
3.3.4; Meta-investigation Knowledge;63
3.3.4.1; Different Types of Investigations;63
3.3.4.1.1; Exploratory Inductive Investigations;64
3.3.4.1.2; Confirmatory Investigations;64
3.3.4.1.3; Examples of Investigations;65
3.3.4.2; Purposes of Different Investigations;66
3.3.4.3; Creating Investigations;66
3.3.4.3.1; Further Issues to Consider When Designing Investigations;67
3.3.4.4; Characteristics of a Good Investigation;68
3.3.4.5; How Different Investigations Fit Together;69
3.3.5; Meta-knowledge for Data Analysis;70
3.3.5.1; Different Data Analysis Methods;70
3.3.5.1.1; Study 1. Qualitative and Quantitative Exploratory Analyses Based on Interviews with Students About Their Views of Friendship;70
3.3.5.1.2; Study 2. A Quantitative, Confirmatory Analysis to Test Hypotheses About Strategies for Making Friends;71
3.3.5.1.3; Study 3. Qualitative and Quantitative Exploratory Analyses Based on Observation of the Development of Friendships Within a Book Group;72
3.3.5.2; Purposes of Data Analysis;73
3.3.5.3; Creating Analyses;73
3.3.5.4; Characteristics of a Good Data Analysis;76
3.3.5.5; How Different Analyses Fit Together;76
3.3.6; Discussion;76
3.3.6.1; Summary of the Meta-knowledge Framework;76
3.3.6.2; Teaching Scientific Meta-knowledge;78
3.3.6.3; Concluding Thoughts About the Utility of the Framework;79
3.3.7;References;80
3.4;4 From Modeling Schemata to the Profiling Schema: Modeling Across the Curricula for Profile Shaping Education;84
3.4.1; Modeling Theory in Science Education;84
3.4.1.1; Paradigmatic Perspective;85
3.4.1.2; Critical Thresholds;86
3.4.1.3; Modeling Schemata;88
3.4.1.4; Progressive Middle-Out Approach;89
3.4.1.5; Experiential Learning Cycles;90
3.4.1.6; Mediated Regulation;91
3.4.2; Profile Shaping Education;93
3.4.3; The Profiling Schema;93
3.4.4; Cognitive Taxonomy;98
3.4.5; Deployment;100
3.4.6;References;102
4;Part II Modeling and Student Learning in Science Education;104
4.1;5 Helping Students Construct Robust Conceptual Models;105
4.1.1; A Brief History of Modeling Instruction;105
4.1.2; The Modeling Classroom;107
4.1.2.1; Culture;107
4.1.2.2; Motivation;109
4.1.2.3; Student Thinking;109
4.1.3; The Modeling Experience;110
4.1.3.1; The Study;110
4.1.4; Findings;111
4.1.4.1; Modeling Activities;111
4.1.4.1.1; Whiteboarding;111
4.1.4.1.2; The Architecture of Modeling Discourse;111
4.1.4.1.3; Connecting Discourse with Whiteboarded Representations to Make Sense of Student Thinking;112
4.1.4.1.4; Whiteboard-Centered Activities;113
4.1.4.1.5; The Power of the Marker and the Power of the Eraser;115
4.1.4.1.6; The Role of the Teacher;116
4.1.4.1.7; Critical Factors in Discourse Management––The Board Meeting;116
4.1.4.1.8; Understanding the Conceptual Models Students Construct;118
4.1.4.2; Implications for Instruction;123
4.1.4.3; Suggestions for Teachers;123
4.1.5; Conclusion;125
4.1.6;References;125
4.2;6 The Molecular Workbench Software: An Innovative Dynamic Modeling Tool for Nanoscience Education;127
4.2.1; Introduction;127
4.2.2; The Importance of Dynamic Modeling;129
4.2.3; Why Use First Principles to Build Educational Simulations?;131
4.2.4; The Molecular Workbench Software;133
4.2.4.1; The Computational Engines;134
4.2.4.1.1; The Molecular Dynamics Engine;134
4.2.4.1.2; The Quantum Dynamics Engine;136
4.2.4.2; The Modeling and Authoring System;137
4.2.4.3; The Delivery System;139
4.2.4.4; The Assessment System;139
4.2.5; Results;141
4.2.6; Future Work;143
4.2.7;References;143
4.3;7 Lowering the Learning Threshold: Multi-Agent-Based Models and Learning Electricity;146
4.3.1; Introduction;146
4.3.2; Theoretical Overview: Electricity and the Micro–Macro Link;147
4.3.2.1; Misconceptions in Electricity as ''Slippage Between Levels'';147
4.3.2.2; The ''Emergent'' Approach: The Microscopic Theory of Conduction and Its Affordances;149
4.3.2.3; Potential Design Challenges from a Developmental Perspective;151
4.3.3; Lowering the Threshold for Learning: Designing NIELS to Leverage Naïve Intuition;152
4.3.3.1; NetLogo: A ''Glass-Box'' Platform for Learning and Modeling;152
4.3.3.2; The Original Model: Electric Current in a Wire;154
4.3.3.3; The Redesigned Model: Electron-Sink Model;154
4.3.4; The Study: Setting, Method, and Data;155
4.3.5; Differences Between the Models Used;159
4.3.6; Coding and Analysis;159
4.3.6.1; Registration;160
4.3.6.2; Causal Schema;160
4.3.6.3; Phenomenological Primitives;160
4.3.7; Findings;161
4.3.7.1; Mental Models of Students in the Electron-Sink Group (Fifth and Seventh Grade): Understanding Conservation of ''Filling-Time'';161
4.3.7.1.1; Amber (fifth grade);161
4.3.7.1.2; David (and Sam) (seventh Grade);163
4.3.7.2; Written Explanations of the ''Balancing Filling-Time'' Activity;165
4.3.7.3; From ''Filling-Time'' to Electric Current;166
4.3.7.4; Between-Group Quantitative Comparisons of Post-explanations of ''Balancing Current'';168
4.3.8; Discussion;170
4.3.9;References;172
4.3.10;NetLogo Models References;176
4.4;8 Engineering-Based Modelling Experiences in the Elementary and Middle Classroom;177
4.4.1; Introduction;177
4.4.2; Engineering, Science, Mathematics, and Technology Education;178
4.4.3; Engineering Education for Young Learners;178
4.4.4; A Models and Modelling Perspective for Engineering Education;179
4.4.5; An Engineering Model-Eliciting Activity;180
4.4.6; Principles for Designing Model-Eliciting Activities;181
4.4.6.1; The Model Construction Principle;182
4.4.6.2; The Personal Meaningfulness Principle;182
4.4.6.3; The Self-Assessment Principle;182
4.4.6.4; The Model Documentation Principle;183
4.4.6.5; The Model Generalization Principle;183
4.4.7; The Present Study;183
4.4.7.1; Participants and Procedures;183
4.4.7.2; Data Sources and Analyses;184
4.4.8; Results;185
4.4.8.1; Model A;185
4.4.8.2; Model B;186
4.4.8.3; Model C;187
4.4.8.4; Model D;188
4.4.8.5; Remaining Groups' Model Creations;189
4.4.9; Discussion and Concluding Points;190
4.4.10; Appendix: There Is a Trouble in Paradise: Severe Water Shortage Problem in Cyprus;191
4.4.11; The Problem;195
4.4.12;References;196
4.5;9 Engaging Elementary Students in Scientific Modeling: The MoDeLS Fifth-Grade Approach and Findings;199
4.5.1; The MoDeLS Approach to Scientific Models and Modeling;200
4.5.1.1; Conceptual Framework;200
4.5.1.2; Methodological Framework: A Learning Progression Framework for Students' Scientific Modeling;201
4.5.2; The Model-Centered Instructional Sequence and the Unit of Evaporation and Condensation for Elementary Students' Engagement in Scientific Modeling;203
4.5.2.1; The Authors' Work on Engaging Elementary Students in Scientific Modeling;203
4.5.2.2; The Instructional Sequence as a Pedagogical Tool;203
4.5.2.3; The Model-Centered Instructional Sequence (MIS);204
4.5.2.4; The MIS-Embedded Unit of Evaporation and Condensation;207
4.5.3; Method;208
4.5.4; The Influence of the MIS on Students' Modeling;211
4.5.4.1; The Influence of Empirical Investigations in MIS: Students' Attention to Empirical Evidence;211
4.5.4.2; The Influence of the Computer Simulations in MIS: Students' Attention to Invisible Objects as an Explanatory Feature;214
4.5.4.3; The Influence of the Social Interactions in MIS: Students' Attention to Audience and Communicative Features of Models;216
4.5.5; Discussion and Concluding Remarks;218
4.5.6;References;220
5;Part III Modeling and Teachers' Knowledge;223
5.1;10 Relationships Between Elementary Teachers' Conceptions of Scientific Modeling and the Nature of Science;224
5.1.1; Introduction;224
5.1.2; Literature Review and Framework;225
5.1.3; Method;227
5.1.3.1; Participants;227
5.1.3.2; Intervention;227
5.1.3.3; Data Collection;230
5.1.3.4; Data Analysis;230
5.1.4; Results;231
5.1.4.1; Teachers' Conceptions of NOS Aspects;231
5.1.4.2; Teachers' Conceptions of Scientific Models and Relationships of Scientific Models and NOS Aspects;233
5.1.5; Implications;235
5.1.6; Recommendations;236
5.1.7; Appendix;238
5.1.8;References;238
5.2;11 Science Teachers' Knowledge About Learning and Teaching Models and Modeling in Public Understanding of Science;241
5.2.1; Introduction;241
5.2.1.1; Aim of the Study;241
5.2.2; Public Understanding of Science (PUSc);242
5.2.2.1; Models and Modeling in Public Understanding of Science;243
5.2.3; Method and Procedure;245
5.2.3.1; Participants in the Study;246
5.2.3.2; The Repertory Grid Instrument;246
5.2.3.3; The Semi-structured Interview;249
5.2.3.4; Data Analyses;249
5.2.3.5; Rep Grid Data Analyses: Research Question 1;250
5.2.3.6; FOCUS Sorting and Hierarchical Clustering;250
5.2.3.7; COMPARE;250
5.2.3.8; Interview Data Analyses: Research Question 2;251
5.2.3.9; Knowledge About Instructional Strategies (1) and About Students' Understanding (2);251
5.2.3.10; Knowledge About Ways to Asses Students' Understanding (3);251
5.2.3.11; Knowledge About Goals and Objectives of the Topic in the Curriculum (4);252
5.2.4; Results from the Data Analyses;252
5.2.4.1; Research Question 1: Rep Grid Results;252
5.2.4.2; Research Question 2: Interview Results;254
5.2.4.3; Type I of PCK: Focused on Model Content;255
5.2.4.4; Type II of PCK: Focused on Model Content, Model Creation, and Model Thinking;256
5.2.4.5; PCK Development;257
5.2.5; Conclusions;257
5.2.5.1; Type A: Science as a Body of Knowledge;258
5.2.5.2; Type B: Science as a Method of Generating and Validating Knowledge;258
5.2.6; Discussion and Implications;259
5.2.6.1; Possible Explanations of the Differences Between Type A and Type B;259
5.2.6.2; Implications;260
5.2.7;References;261
5.3;12 Teaching Pre-service Elementary Teachers to Teach Science with Computer Models;264
5.3.1; Introduction;264
5.3.2; Methods;268
5.3.2.1; Participants;268
5.3.2.2; The Computer-Modeling Tool;268
5.3.2.3; The Instructional Design Model;270
5.3.2.4; Assessment Task;275
5.3.3; Results and Discussion;276
5.3.4; Concluding Remarks;277
5.3.5;References;278
6;Name Index;281
7;Subject Index;288