Learning to Solve Problems is a much-needed book that describes models for designing interactive learning environments to support how to learn and solve different kinds of problems. Using a research-based approach, author David H. Jonassen a recognized expert in the field shows how to design instruction to support three kinds of problems: story problems, troubleshooting, and case and policy analysis problems. Filled with models and job aids, this book describes different approaches for representing problems to learners and includes information about technology-based tools that can help learners mentally represent problems for themselves. Jonassen also explores methods for associating different solutions to problems and discusses various processes for reflecting on the problem solving process. Learning to Solve Problems also includes three methods for assessing problem-solving skills performance assessment, component skills; and argumentation.
List of Figures, Tables, and Exhibits. Acknowledgments. Introduction. Chapter 1: What Is Problem Solving? What Are Problems, and How Do They Vary? Structuredness. Complexity. Dynamicity. Domain (Context) Specificity/Abstractness. What Is Problem Solving, and How Does It Vary? Story Problems. Troubleshooting Problems. Case and System and Policy Analysis Problems. Summary. Chapter 2: Designing Learning Environments to Support Problem Solving. Story Problems. Problem Type and Typology. Worked Examples. Practice Items. Content Instruction. Summary. Troubleshooting Problems. Conceptual Model. Troubleshooter. Case Library. Worked Examples. Practice Items. Case, Systems, or Policy Analysis Problems. Problem Presentation. Problem Representation Tools. Summary. Chapter 3: Presenting Problems to Learners. Problem Posing. Anchoring Problems in Macrocontexts. Case-Based Instruction. Components of Case Problems. Case Format. Summary. Chapter 4: Tools for Representing Problems by Learners. Representing Semantic Organization. Representing Causal Reasoning. Causal Modeling. Influence Diagrams. Expert Systems. Modeling Dynamic Systems. Summary. Chapter 5: Associating Solutions with Problems. Worked Examples: Modeling Performance. Subgoals. Self-Explanations. Using Worked Examples. Case Libraries: Teaching with Stories. Supporting Problem Solving with Stories. Collecting Stories. Cognitive Flexibility Hypertexts: Conveying Complexity. Understanding Sexual Harassment. Freedom of Expression. Medical Diagnosis. Summary. Chapter 6: Supporting Solutions. Simulations. Using Microworlds to Simulate Solutions. Building Learning Objects to Simulate Solutions. Building Simulations of Problems. Using Versus Building Simulations. Argumentation. Argumentation Skills. Argumentation Technologies. Summary. Chapter 7: Reflecting on Problem-Solving Processes. Peer Instruction and Thinking-Aloud Pair Problem Solving. Peer Instruction. Thinking-Aloud Pair Problem Solving. Teachbacks and Abstracted Replays. Teachbacks. Abstracted Replays. Coding Protocols. Summary. Chapter 8: Assessing Problem Solutions and Learning. Assessing Problem-Solving Performance. Constructing Rubrics. Heuristics for Developing an Effective Rubric. Assessing Component Skills. Story Problems. Troubleshooting Problems. Case Analysis Problems. Knowledge Representation Tools. Assessing Argumentation and Justification. Objective Forms of Assessment of Argumentation. Coding Student Arguments. Assessing Student Essays and Problem-Solving Accounts. Summary. References. Index. About the Author. About the Series Editors. About the Advisory Board Members.