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Group problem solving
Experimental research by social and cognitive psychologists has established that cooperative groups solve a wide range of problems better than individuals. Cooperative problem solving groups of scientific researchers, auditors, financial analysts, air crash investigators, and forensic art experts are increasingly important in our complex and interdependent society. This comprehensive textbook--the first of its kind in decades--presents important theories and experimental research about group problem solving. The book focuses on tasks that have demonstrably correct solutions within mathematical, logical, scientific, or verbal systems, including algebra problems, analogies, vocabulary, and logical reasoning problems.
The book explores basic concepts in group problem solving, social combination models, group memory, group ability and world knowledge tasks, rule induction problems, letters-to-numbers problems, evidence for positive group-to-individual transfer, and social choice theory. The conclusion proposes ten generalizations that are supported by the theory and research on group problem solving.
Group Problem Solvingis an essential resource for decision-making research in social and cognitive psychology, but also extremely relevant to multidisciplinary and multicultural problem-solving teams in organizational behavior, business administration, management, and behavioral economics.
eBook
Task complexity moderates group synergy
Complexity—defined in terms of the number of components and the nature of the interdependencies between them—is clearly a relevant feature of all tasks that groups perform. Yet the role that task complexity plays in determining group performance remains poorly understood, in part because no clear language exists to express complexity in a way that allows for straightforward comparisons across tasks. Here we avoid this analytical difficulty by identifying a class of tasks for which complexity can be varied systematically while keeping all other elements of the task unchanged. We then test the effects of task complexity in a preregistered two-phase experiment in which 1,200 individuals were evaluated on a series of tasks of varying complexity (phase 1) and then randomly assigned to solve similar tasks either in interacting groups or as independent individuals (phase 2). We find that interacting groups are as fast as the fastest individual and more efficient than the most efficient individual for complex tasks but not for simpler ones. Leveraging our highly granular digital data, we define and precisely measure group process losses and synergistic gains and show that the balance between the two switches signs at intermediate values of task complexity. Finally, we find that interacting groups generate more solutions more rapidly and explore the solution space more broadly than independent problem solvers, finding higher-quality solutions than all but the highest-scoring individuals.
Journal Article
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their capacity for flexible, creative tool use—using objects in new ways to act on the world, achieve a goal, or solve a problem. To study this type of general physical problem solving, we introduce the Virtual Tools game. In this game, people solve a large range of challenging physical puzzles in just a handful of attempts. We propose that the flexibility of human physical problem solving rests on an ability to imagine the effects of hypothesized actions, while the efficiency of human search arises from rich action priors which are updated via observations of the world. We instantiate these components in the “sample, simulate, update” (SSUP) model and show that it captures human performance across 30 levels of the Virtual Tools game. More broadly, this model provides a mechanism for explaining how people condense general physical knowledge into actionable, task-specific plans to achieve flexible and efficient physical problem solving.
Journal Article
The effect of STEM problem-based learning on students’ mathematical problem-solving beliefs
The purpose of this quasi-experimental study was to investigate the effect of science, technology, engineering, and mathematics (STEM) problem-based learning (PBL) intervention on students’ problem-solving beliefs (PSB). To this end, the PSB questionnaire was administered to a group of eighty-six 10th graders across different socio-economic spectra working with an expert facilitator in a rural school that received curricular support and resources to specifically develop STEM teaching in eastern South Africa. The sample participated in two time periods (pre- vs. post-intervention) in which problem-based activities were utilized. A quantitative evaluation of the impact of an intervention on students’ subsequent beliefs as well as qualitative analysis of interviews with a sample of fourteen purposively selected students is presented. Results showed that participants increased their mathematical PSB (p<.001 , d=.50 ). The implications of these findings speak to the potential for teachers to utilize the results to provide opportunities for students to experience PBL activities.
Journal Article
Learning to Solve Problems
This book provides a comprehensive, up-to-date look at problem solving research and practice over the last fifteen years. The first chapter describes differences in types of problems, individual differences among problem-solvers, as well as the domain and context within which a problem is being solved. Part one describes six kinds of problems and the methods required to solve them. Part two goes beyond traditional discussions of case design and introduces six different purposes or functions of cases, the building blocks of problem-solving learning environments. It also describes methods for constructing cases to support problem solving. Part three introduces a number of cognitive skills required for studying cases and solving problems. Finally, Part four describes several methods for assessing problem solving. Key features includes:
Teaching Focus – The book is not merely a review of research. It also provides specific research-based advice on how to design problem-solving learning environments.
Illustrative Cases – A rich array of cases illustrates how to build problem-solving learning environments. Part two introduces six different functions of cases and also describes the parameters of a case.
Chapter Integration – Key theories and concepts are addressed across chapters and links to other chapters are made explicit. The idea is to show how different kinds of problems, cases, skills, and assessments are integrated.
Author expertise – A prolific researcher and writer, the author has been researching and publishing books and articles on learning to solve problems for the past fifteen years.
This book is appropriate for advanced courses in instructional design and technology, science education, applied cognitive psychology, thinking and reasoning, and educational psychology. Instructional designers, especially those involved in designing problem-based learning, as well as curriculum designers who seek new ways of structuring curriculum will find it an invaluable reference tool.
1. How Does Problem Solving Vary?
Part I. Problem-Specific Design Models
2. Solving Story Problems
3. Decision Making
4. Troubleshooting/Diagnosis
5. Strategic Performance
6. Policy Analysis
7. Design Problem Solving
Part II. Cases: The Building Blocks Problem-Solving Learning Environments
8. Cases as Problems to Solve
9. Cases as Worked Examples of Well-Structured Problems
10. Case Studies: Examples of Ill-Structured Problems
11. Cases as Analogues
12. Cases as Prior Experiences
13. Cases as Alternative Perspectives
14. Cases as Simulations
Part III. Cognitive Skills in Problem-Solving
15. Defining the Problem: Problem Schemas
16. Analogically Comparing Problems
17. Understanding Causal Relationships in Problems
18. Questions for Scaffolding Problem Solving
19. Modeling Problems
20. Arguing to Learn to Solve Problems
21. Metacognitive Regulation of Problem Solving
Part IV. Assessing Problem Solving
22. Assessing Problem Solving
David H. Jonassen is Curators’ Professor in the School of Information Science and Learning Technologies at the University of Missouri.
eBook