元认知
平面图(考古学)
计算机科学
认知
反射(计算机编程)
自反性
知识管理
心理学
社会科学
历史
社会学
考古
神经科学
程序设计语言
作者
Olivia B. Newton,Travis J. Wiltshire,Stephen M. Fiore
出处
期刊:Research on Managing Groups and Teams
日期:2018-09-04
卷期号:: 33-54
被引量:14
标识
DOI:10.1108/s1534-085620180000019006
摘要
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.
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