Model-based reasoning in STEM education: a systematic literature review.
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| Title: | Model-based reasoning in STEM education: a systematic literature review. |
|---|---|
| Authors: | Udosen, Abasiafak N.1 (AUTHOR) audosen@purdue.edu, Magana, Alejandra J.1 (AUTHOR) admagana@purdue.edu |
| Source: | International Journal of STEM Education. 7/9/2026, Vol. 13 Issue 1, p1-27. 27p. |
| Subject Terms: | *STEM education, *Evidence synthesis, *Cognitive psychology, *Evaluation methodology, Model-based reasoning, Inscriptions, Cooperativeness, Scientific models |
| Abstract: | This systematic review examines model-based reasoning (MBR) in STEM education, focusing on how it is defined, how it works in practice, and how it is measured and taught across classrooms and laboratories. Guided by PRISMA 2020, this review synthesized 146 peer-reviewed studies published between 1980 and 2025 to address four research questions. We report a qualitative synthesis and descriptive frequencies from the findings. First, the literature converges on MBR as an iterative, distributed, and representation-mediated practice that links mental models with external inscriptions, including diagrams, equations, prototypes, code, and simulations, while integrating abductive, inductive, deductive, causal-mechanistic, and computational forms of reasoning. Second, the reviewed studies suggest recurring stage-based patterns in modeling and simulation activities: abductive and analogical reasoning are especially visible during early problem analysis and formulation; deductive, quantitative, and algorithmic reasoning support model construction and execution; diagnostic, inductive, and probabilistic reasoning support verification, validation, and debugging activities. Third, the field broadly agrees on the centrality of iteration, external representations, and collaboration in model-based reasoning activities, while debates persist over primary theoretical emphasis, particularly whether model-based reasoning is best grounded in mental models or distributed cognition; whether reasoning modes should be treated as analytically separable or as hybrid in use; and the extent to which domain-specific standards should guide model evaluation and acceptance decisions. Fourth, the ways MBR is characterized and measured shape what can be claimed about it: micro-level approaches, such as think-aloud protocols and time-stamped coding, capture moment-to-moment strategy use; meso-level approaches, such as computational notebooks, simulation logs, and rubric-based assessments, reveal workflow and representational competence; and macro-level approaches, such as model-evidence link diagrams and portfolios, capture longer-term development over weeks or semesters. These findings position MBR as a useful integrative lens for scientific sensemaking that is applicable across STEM disciplines, while also showing that its meaning and assessment remain shaped by disciplinary, instructional, and methodological context. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of STEM Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 195219472 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Model-based reasoning in STEM education: a systematic literature review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Udosen%2C+Abasiafak+N%2E%22">Udosen, Abasiafak N.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> audosen@purdue.edu</i><br /><searchLink fieldCode="AR" term="%22Magana%2C+Alejandra+J%2E%22">Magana, Alejandra J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> admagana@purdue.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+STEM+Education%22">International Journal of STEM Education</searchLink>. 7/9/2026, Vol. 13 Issue 1, p1-27. 27p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22STEM+education%22">STEM education</searchLink><br />*<searchLink fieldCode="DE" term="%22Evidence+synthesis%22">Evidence synthesis</searchLink><br />*<searchLink fieldCode="DE" term="%22Cognitive+psychology%22">Cognitive psychology</searchLink><br />*<searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Model-based+reasoning%22">Model-based reasoning</searchLink><br /><searchLink fieldCode="DE" term="%22Inscriptions%22">Inscriptions</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperativeness%22">Cooperativeness</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+models%22">Scientific models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This systematic review examines model-based reasoning (MBR) in STEM education, focusing on how it is defined, how it works in practice, and how it is measured and taught across classrooms and laboratories. Guided by PRISMA 2020, this review synthesized 146 peer-reviewed studies published between 1980 and 2025 to address four research questions. We report a qualitative synthesis and descriptive frequencies from the findings. First, the literature converges on MBR as an iterative, distributed, and representation-mediated practice that links mental models with external inscriptions, including diagrams, equations, prototypes, code, and simulations, while integrating abductive, inductive, deductive, causal-mechanistic, and computational forms of reasoning. Second, the reviewed studies suggest recurring stage-based patterns in modeling and simulation activities: abductive and analogical reasoning are especially visible during early problem analysis and formulation; deductive, quantitative, and algorithmic reasoning support model construction and execution; diagnostic, inductive, and probabilistic reasoning support verification, validation, and debugging activities. Third, the field broadly agrees on the centrality of iteration, external representations, and collaboration in model-based reasoning activities, while debates persist over primary theoretical emphasis, particularly whether model-based reasoning is best grounded in mental models or distributed cognition; whether reasoning modes should be treated as analytically separable or as hybrid in use; and the extent to which domain-specific standards should guide model evaluation and acceptance decisions. Fourth, the ways MBR is characterized and measured shape what can be claimed about it: micro-level approaches, such as think-aloud protocols and time-stamped coding, capture moment-to-moment strategy use; meso-level approaches, such as computational notebooks, simulation logs, and rubric-based assessments, reveal workflow and representational competence; and macro-level approaches, such as model-evidence link diagrams and portfolios, capture longer-term development over weeks or semesters. These findings position MBR as a useful integrative lens for scientific sensemaking that is applicable across STEM disciplines, while also showing that its meaning and assessment remain shaped by disciplinary, instructional, and methodological context. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of STEM Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s40594-026-00621-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1 Subjects: – SubjectFull: STEM education Type: general – SubjectFull: Evidence synthesis Type: general – SubjectFull: Cognitive psychology Type: general – SubjectFull: Evaluation methodology Type: general – SubjectFull: Model-based reasoning Type: general – SubjectFull: Inscriptions Type: general – SubjectFull: Cooperativeness Type: general – SubjectFull: Scientific models Type: general Titles: – TitleFull: Model-based reasoning in STEM education: a systematic literature review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Udosen, Abasiafak N. – PersonEntity: Name: NameFull: Magana, Alejandra J. IsPartOfRelationships: – BibEntity: Dates: – D: 09 M: 07 Text: 7/9/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 21967822 Numbering: – Type: volume Value: 13 – Type: issue Value: 1 Titles: – TitleFull: International Journal of STEM Education Type: main |
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