Cited by 390Innovative Teaching and Learning MethodsProblem and Project Based LearningProblem Solving Skills Development

Advancing the Science of Collaborative Problem Solving

Open access
Graesser, Arthur C., Fiore, Stephen M., Greiff, Samuel, Andrews-Todd, Jessica, Foltz, Peter W., Hesse, Friedrich W.·2018·Psychological Science in the Public Interest

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.

Integrity signals

Registry

Crossref · 10.1177/1529100618808244

Cites

161 of 161 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., & Hesse, F. W.
Affiliations
  • University of Memphis (US)
  • University of Central Florida (US)
  • University of Luxembourg (LU)
  • Educational Testing Service, Princeton, New Jersey
  • University of Colorado Boulder (US)
  • Leibniz-Institut für Wissensmedien (DE)
Corresponding author
Graesser, Arthur C.
Venue
Psychological Science in the Public Interest
Published
2018-11
Volume
19
Issue
2
Pages
59-92
ISSN
  • 1539-6053 (ISSN-L)
  • 1529-1006
OpenAlex
W2902754622
Cited by
390
References
160
Citations by year
2026: 53 · 2025: 71 · 2024: 71 · 2023: 67 · 2022: 42 · 2021: 42 · 2020: 22 · 2019: 21 · 2018: 1
FWCI
42.20
Type
journal article
Language
en
Open access
Locations
  • Gothic.net (publishedVersion)
  • PubMed (publishedVersion)
  • Journal of International Crisis and Risk Communication Research (submittedVersion)
  • Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
  • Social Sciences › Education › Problem and Project Based Learning
  • Health Sciences › Health Professions › Speech and Hearing › Problem Solving Skills Development
Keywords
collaborative problem solving, teamwork, PISA, interdisciplinary research, employee training
MeSH terms
Cooperative Behavior, Education, Group Processes, Humans, Models, Psychological, Problem Solving

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review