Advancing the Science of Collaborative Problem Solving. (November 2018)

• Record Type: Journal Article
• Authors: Graesser, Arthur C.; Fiore, Stephen M.; Greiff, Samuel; Andrews-Todd, Jessica; Foltz, Peter W.; Hesse, Friedrich W.
• Is Part Of: Psychological science in the public interest. Volume 19:Number 2(2018)
• Page Start: 59
• Page End: 92
• Publication Date: 2018-11
• Subjects: assessment -- collaboration -- collaborative problem solving -- education -- groups -- PISA -- teams; Psychology, Applied -- Periodicals; Community psychology -- Periodicals; 150.5
• DOI: 10.1177/1529100618808244
• Languages: English
• ISSNs: 1529-1006

Abstract

Collaborative problem solving (CPS) has been receiving increasing international attention because much of the complex work in the modern world is performed by teams. However, systematic education and training on CPS is lacking for those entering and participating in the workforce. In 2015, the Programme for International Student Assessment (PISA), a global test of educational progress, documented the low levels of proficiency in CPS. This result not only underscores a significant societal need but also presents an important opportunity for psychological scientists to develop, adopt, and implement theory and empirical research on CPS and to work with educators and policy experts to improve training in CPS. This article offers some directions for psychological science to participate in the growing attention to CPS throughout the world. First, it identifies the existing theoretical frameworks and empirical research that focus on CPS. Second, it provides examples of how recent technologies can automate analyses of CPS processes and assessments so that substantially larger data sets can be analyzed and so students can receive immediate feedback on their CPS performance. Third, it identifies some challenges, debates, and uncertainties in creating an infrastructure for research, education, and training in CPS. CPS education and assessment are expected to improve when supported by larger data sets and theoretical frameworks that are informed by psychological science. This will require interdisciplinary efforts that include expertise in psychological science, education, assessment, intelligent digital technologies, and policy.