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Team cognition research continues to evolve as the need for understanding and improving complex problem solving itself grows. Complex problem solving requires members to…
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 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…
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.
In this chapter we discuss attitudinal and affective factors in the context of science teams. We review some of the key findings on conflict, trust, and cohesion in teams…
In this chapter we discuss attitudinal and affective factors in the context of science teams. We review some of the key findings on conflict, trust, and cohesion in teams and discuss the differentiation between team-related and task-related definitions of each. In so doing, we discuss their relevance to team effectiveness in science teams and provide guidance on notional areas of research for understanding how these are related to effectiveness in science teams.
Cognitive Load Theory (CLT) is the product of over a decade of research in the instructional science domain (Chandler & Sweller, 1991; Sweller & Chandler, 1994), and its applications to other areas of inquiry continues to expand (see Cuevas, Fiore, & Oser, 2002; Paas, Renkl, & Sweller, 2003a; Paas, Tuovinen, Tabbers, & Van Gerven, 2003b; Scielzo, Fiore, Cuevas, & Salas, 2004). The core of CLT is based on two sets of what are termed cognitive load factors that are either endogenous or exogenous from the viewpoint of an operator interacting with the environment. Endogenous (or intrinsic) factors are sources of cognitive load in terms of the general amount and complexity of information with which the operator has to interact. In training environments, intrinsic load is directly proportional to the amount of materials that trainees need to acquire. As such, the more complex the information is in terms of volume and conceptual interactivity, the higher the cognitive load will be. In operational settings, high intrinsic load can occur whenever informational demands that need to be processed are high. Within the context of human–robot team environments, there is likely to be unique intrinsic load factors emerging from this hybrid teamwork interaction (e.g., information produced by synthetic team members). Another source of cognitive load comes from exogenous or extraneous factors. In training and operational settings alike, extraneous cognitive load may occur dependent upon the manner in which information needing attention is presented. Specifically, the more complex the human–robot team interface is in relation to the process by which information is displayed and/or communicated, the more extraneous cognitive load can be present. For example, the technological tools involved in the communication of information, and the associated modalities used to process information may inadvertently result in cognitive load. Simply put, high extraneous cognitive load can be produced as a result of using sub-optimal information presentation and communication. Overall, exogenous factors can stem from the added complexity of human–robot operations in terms of distinct command-and-control systems that emerge from using novel technology. Within such operations, it is particularly important to control sources of extraneous cognitive load that have been shown to produce two distinct negative effects on information processing – redundancy of information and split-attention. These have been shown to attenuate processing capacity thereby minimizing optimal information processing (e.g., Sweller, 1994; Mayer, 1999).
Many organizations have implemented distance-learning (DL) courses and programs as an economical, efficient way to deliver training. The purpose of this chapter is to…
Many organizations have implemented distance-learning (DL) courses and programs as an economical, efficient way to deliver training. The purpose of this chapter is to summarize some of the major considerations that are associated with distance-learning programs. We describe a number of the issues surrounding DL, ranging from how organizations use DL to the differing forms of training being delivered and how organizations are reacting to DL. We close with a discussion of issues in practice and suggest directions for future research.
Most traditional research on work groups has studied groups and teams that are homogeneous with respect to culture. To alleviate the dearth of material on culturally…
Most traditional research on work groups has studied groups and teams that are homogeneous with respect to culture. To alleviate the dearth of material on culturally heterogeneous teams, this chapter provides an overview of the impact of cultural diversity on groups and teams in today’s workforce. First, we focus on the problems involved in defining the constructs of “teams” and “culture.” Second, we provide a brief review of the cultural factors that have been identified as affecting human performance. This review serves as the basis for the third section of this chapter, which investigates if – and how – cultural heterogeneity affects team performance. Finally, we conclude with how culturally diverse workplaces can be managed and how to improve performance when faced with cultural diversity.