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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.

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.

In this chapter we highlight a neurodynamic approach that is showing promise as a quantitative measure of team performance.

During teamwork the rapid electroencephalographic (EEG) oscillations that emerge on the scalp were transformed into symbolic data streams which provided historical details at a second-by-second resolution of how the team perceived the evolving task and how they adjusted their dynamics to compensate for, and anticipate new task challenges. Key to this approach are the different strategies that can be used to reduce the data dimensionality, including compression, abstraction and taking advantage of the natural redundancy in biologic signals.

The framework emerging is that teams continually enter and leave organizational neurodynamic partnerships with each other, so-called metastable states, depending on the evolving task, with higher level dynamics arising from mechanisms that naturally integrate over faster microscopic dynamics.

The development of quantitative measures of the momentary dynamics of teams is anticipated to significantly influence how teams are assembled, trained, and supported. The availability of such measures will enable objective comparisons to be made across teams, training protocols, and training sites. They will lead to better understandings of how expertise is developed and how training can be modified to accelerate the path toward expertise.

The innovation of this study is the potential it raises for developing globally applicable quantitative models of team dynamics that will allow comparisons to be made across teams, tasks, and training protocols.

Team cohesion and other team processes are inherently dynamic mechanisms that contribute to team effectiveness. Unfortunately, extant research has typically treated team cohesion and other processes as static, and failed to capture how these processes change over time and the implications of these changes. In this chapter, we discuss the characteristics of team process dynamics and highlight the importance of temporal considerations when measuring team cohesion. We introduce innovative research methods that can be applied to assess and monitor team cohesion and other process dynamics. Finally, we discuss future directions for the research and practical applications of these new methods to enhance our understanding of the dynamics of team cohesion and other processes.

Teams focus on a common and valued goal, and effective teams are able to alter their behaviors in pursuit of this goal. When teams are viewed in the context of a dynamic environment, they must adapt to challenges in the environment in order to maintain team effectiveness. In this light, we describe various sources of team variation and how they combine with individual-level, team-level, and dynamical mechanisms for maintaining team effectiveness in a dynamic environment. The combination of these elements produces a systems view of team effectiveness. Our goals are to begin to define, both in words and in operational terms, team effectiveness from this perspective and to evaluate this definition in the context of team training using intelligent tutoring systems (team ITS). In addressing these goals, we present an example of real-time analysis of team effectiveness and some challenges for team ITS training based on a dynamical systems view of team effectiveness.

A key challenge for cost-effective Intelligent Tutoring Systems (ITSs) is the ability to create generalizable domain, learner, and pedagogical models so they can be re-used many times over. Investment in this technology will be needed to succeed in developing ITSs for team training. The purpose of this chapter is to propose an instructional framework for guiding team ITS researchers in their development of these models for reuse. We establish a foundation for the framework with three propositions. First, we propose that understanding how teams develop is needed to establish a science-based foundation for modeling. Toward this end, we conduct a detailed exploration of the Kozlowski, Watola, Jensen, Kim, and Botero (2009) theory of team development and leadership, and describe a use case example to demonstrate how team training was developed for a specific stage in their model. Next, we propose that understanding measures of learning and performance will inform learner modeling requirements for each stage of team development. We describe measures developed for the use case and how they were used to understand teamwork skill development. We then discuss effective team training strategies and explain how they were implemented in the use case to understand their implications for pedagogical modeling. From this exploration, we describe a generic instructional framework recommending effective training strategies for each stage of team development. To inform the development of reusable models, we recommend selecting different team task domains and varying team size to begin researching commonalities and differences in the instructional framework.

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.

The rapidly growing body of global leadership literature still lacks research on both global change and global leader cognition. This chapter presents two case studies describing large-scale global change efforts led by expert global leaders. This is complemented with the results of cognitive task analysis interviews with the two expert global leaders. The findings include task diagrams of the change process they employed and knowledge audits of the most difficult cognitive step in the change processes they led. The audit identifies the elements of expert cognition they utilized, the cues and strategies they employed, and the perceived difficulties novices would experience in similar situations. The findings confirm previous research, solidifying the role and nature of expert cognition in global leaders. We conclude with a discussion of the implications our analysis holds for research and practice.