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This chapter provides an in-depth understanding of the cognitive processes that facilitate creativity from a multi-level perspective. Because cognitive processes are viewed as residing within the individual and as an individual-level phenomenon, it is not surprising that a plethora of research has focused on various cognitive processes involved in creative production at the individual level and the factors that may facilitate or hinder the successful application of these processes. Of course, individuals do not exist in a vacuum, and many organizations are utilizing teams and groups to facilitate creative problem solving. We therefore extend our knowledge from the individual to the team level and group level, providing more than 50 propositions for testing and discussing their implications for future research.

Team cognition research continues to evolve as the need for understanding and improving complex problem solving itself grows. Complex problem solving requires members to engage in a number of complicated collaborative processes to generate solutions. This chapter illustrates how the Macrocognition in Teams model, developed to guide research on these processes, can be utilized to propose how intelligent tutoring systems (ITSs) could be developed to train collaborative problem solving. Metacognitive prompting, based upon macrocognitive processes, was offered as an intervention to scaffold learning these complex processes. Our objective is to provide a theoretically grounded approach for linking intelligent tutoring research and development with team cognition. In this way, team members are more likely to learn how to identify and integrate relevant knowledge, as well as plan, monitor, and reflect on their problem-solving performance as it evolves. We argue that ITSs that utilize metacognitive prompting that promotes team planning during the preparation stage, team knowledge building during the execution stage, and team reflexivity and team knowledge sharing interventions during the reflection stage can improve collaborative problem solving.

While deficits for students with learning disabilities (LD) are prevalent in almost all aspects of mathematics, difficulty in the application and understanding of problem-solving tasks are much more challenging to remediate than computational and procedural skills. Given the complexities involved in authentic problem-solving activities emphasized in current mathematics standards and the inherent challenges presented to students with LD, the importance of using strategies and techniques guided by evidence-based practices is paramount. Yet, ineffective instructional strategies for problem solving are still widespread in both mathematics curricula and available teacher resources. In this chapter, we provide a description of a commonly used ineffective problem-solving strategy (i.e., the keyword strategy), an overview of the keyword research, and an explanation for its ineffectiveness. We conclude with a description of three evidenced-based problem-solving approaches and practices that significantly improve the mathematical performance of students with LD.

Purpose – This chapter considers the consequences on liberty in relationship to the development of the international problem-solving court movement.

Design/methodology/approach – The research, which relies principally on ethnographic fieldwork in six different common law countries (England, Ireland, Scotland, Australia, Canada, and the United States), explores the development of local problem-solving courts in each jurisdiction. These include drug courts, community courts, domestic violence courts, and mental health courts. The ethnographic fieldwork was supplemented with data from various other sources, including government reports, parliamentary debates, evaluations of individual court programs, publications issued by various advocacy groups, media accounts, public statements and articles by problem-solving court judges, and analyses of specialty courts in law reviews and other academic journals.

Findings – The research reveal that the five countries outside of the United States demonstrate greater concern with protecting the dignity of the court, due process, and individual rights – or what the Australians refer to as open and natural justice.

Originality/value – It is the first large-scale comparative study of problem-solving courts in the common law countries where the movement is most advanced.

In a variety of domains, teams represent the main mechanism for dealing with change, complexity, and uncertainty in organizations. Consequently, teams need to be able to adapt and effectively use shared and complementary cognitive processing while collaborating to deal with these challenges.

A conceptual review is provided that addresses this type of complex collaborative cognition via discussion of macrocognition and the processes contributing to effective team problem-solving.

Despite extensive research on problem-solving, research and theories regarding how problem-solving changes over time as teams develop is missing. With this review, we extend research on team problem-solving and team development through integration of existing theory and concepts from the team literature.

This review provides a theoretical foundation for understanding and studying the developmental dynamic of team problem-solving.

A team problem-solving development model is described which outlines the degree to which the primary elements of team development are likely to affect macrocognitive processes within problem-solving phases. A set of propositions is offered in order to guide research on team development in collaborative problem-solving.

This chapter describes our innovative approach to the teaching of computer programming and writing; professors worked with students across classes united by a theme of narrative. A year-long study examined if using Alice, a three-dimensional microworld programming software that allows users to create interactive narratives, was more effective than Visual Basic (VB) in developing problem-solving abilities in first-year college students in introductory computer programming courses. Results revealed that although both the Alice and VB group showed a statistically significant (p<0.05) increase in performance for problem-solving questions related to computer programming, only the Alice group showed a significant increase in problem-solving abilities not directly related to computer programming, and an increase in student retention.

In this study, the authors unpack the micro-level processes of knowledge accumulation (experiential learning) and knowledge application (problem solving) to examine how task allocation structures influence organizational learning. The authors draw on untapped potential of the classical garbage can model (GCM), and extend it to analyze how restrictions on project participation influence differentiation and integration of organizational members’ knowledge and consequently organizational efficiency in solving the diverse, changing problems from an uncertain task environment. To isolate the effects of problem or knowledge diversity and experiential learning, the authors designed three simulation experiments to identify the most efficient task allocation structure in conditions of (1) knowledge homogeneity, (2) knowledge heterogeneity, and (3) experiential learning. The authors find that free project participation is superior when the members’ knowledge and the problems they solve are homogenous. When problems and knowledge are heterogeneous, the design requirement is on matching specialists to problem types. Finally, the authors found that experiential learning creates a dynamic problem where the double duty of adapting the members’ specialization and matching the specialists to problem types is best solved by a hierarchic structure (if problems are challenging). Underlying the efficiency of the hierarchical structure is an adaptive role of specialized members in organizational learning and problem solving: their narrow but deep knowledge helps the organization to adapt the knowledge of its members while efficiently dealing with the problems at hand. This happens because highly specialized members reduce the necessary scope of knowledge and learning for other members during a certain period of time. And this makes it easier for the generalists and for the organization as a whole, to adapt to unforeseen shifts in knowledge demand because they need to learn less. From this nuanced perspective, differentiation and integration may have a complementary, rather than contradictory, relation under environmental uncertainty and problem diversity.

We extend the classical garbage can model to examine how individual differences in ability and motivation will influence organizational performance. We find that spontaneous coordination provided by an organized anarchy is superior when agents are equally competent. The Weberian bureaucracy of planned coordination is effective when problems require specialist knowledge. However, errors in matching problems to specialized agents are a central challenge for bureaucracies. Actual organizations, therefore, combine elements of organized anarchies and bureaucracies. Heterogeneous motivation compounds coordination problems, but is usually less important than competence. Our findings point to matching and interactive learning as fruitful areas for further study.

This study was intended to determine the effects of self-instructional training on algebra problem solving performance of students with learning disabilities, students for whom English is a second language and students who were at risk of failing algebra. Four high school algebra classes consisting of 74 students, of whom 17 were classified as having learning disabilities, 37 had English as a second language, and 20 were considered at-risk for math failure, were assigned randomly to either a self-instructional training condition or a traditional instructional condition. All students were administered pretests, immediate posttests, and delayed posttests of algebra problem solving, pre and post strategy usage questionnaires, and attitude measures. After training, results indicated that both groups’ performance increased from pretest to immediate posttest and pretest to delayed posttest, but no statistical difference was found between groups. The self-instruction group significantly outperformed the traditional instruction group on independent strategy use. Significant correlations were obtained between strategy usage and immediate and delayed posttest scores, indicating that students who successfully learned the strategy had better performance on the math problem solving tests. No significant differences were found across groups in attitude change. Future research issues are discussed with respect to strategy instruction for at risk learners.

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.