Promoting Complex Problem Solving by Introducing Schema-Governed Categories of Key Causal Models
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
Abstract
The ability to recognize key causal models across situations is associated with expertise. The acquisition of schema-governed category knowledge of key causal models may underlie this ability. In an experimental study (n = 183), we investigated the effects of promoting the construction of schema-governed categories and how an enhanced ability to recognize the key causal models relates to performance in complex problem-solving tasks that are based on the key causal models. In a 2 × 2 design, we tested the effects of an adapted version of an intervention designed to build abstract mental representations of the key causal models and a tutorial designed to convey conceptual understanding of the key causal models and procedural knowledge. Participants who were enabled to recognize the underlying key causal models across situations as a result of the intervention and the tutorial (i.e., causal sorters) outperformed non-causal sorters in the subsequent complex problem-solving task. Causal sorters outperformed the control group, except for the subtask knowledge application in the experimental group that did not receive the tutorial and, hence, did not have the opportunity to elaborate their conceptual understanding of the key causal models. The findings highlight that being able to categorize novel situations according to their underlying key causal model alone is insufficient for enhancing the transfer of the according concept. Instead, for successful application, conceptual and procedural knowledge also seem to be necessary. By using a complex problem-solving task as the dependent variable for transfer, we extended the scope of the results to dynamic tasks that reflect some of the typical challenges of the 21st century.
Details
Originalsprache | Englisch |
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Aufsatznummer | 701 |
Fachzeitschrift | Behavioral Sciences |
Jahrgang | 13 |
Ausgabenummer | 9 |
Publikationsstatus | Veröffentlicht - Aug. 2023 |
Peer-Review-Status | Ja |
Externe IDs
ORCID | /0000-0001-5165-4459/work/142248309 |
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ORCID | /0000-0002-4280-6534/work/142251731 |
WOS | 001076952200001 |
Scopus | 85172758527 |
Schlagworte
Forschungsprofillinien der TU Dresden
DFG-Fachsystematik nach Fachkollegium
- Automatisierungstechnik, Regelungssysteme, Robotik, Mechatronik, Cyber Physical Systems
- Arbeitswissenschaft, Ergonomie, Mensch-Maschine-Systeme
- Chemische und Thermische Verfahrenstechnik
- Allgemeines und fachbezogenes Lehren und Lernen
- Entwicklungspsychologie und Pädagogische Psychologie
- Sozialpsychologie und Arbeits- und Organisationspsychologie
Fächergruppen, Lehr- und Forschungsbereiche, Fachgebiete nach Destatis
Ziele für nachhaltige Entwicklung
- SDG 7 – Erschwingliche und saubere Energie
- SDG 8 – Anständige Arbeitsbedingungen und wirtschaftliches Wachstum
- SDG 9 – Industrie, Innovation und Infrastruktur
- SDG 5 – Gleichberechtigung der Geschlechter
- SDG 10 – Weniger Ungleichheiten
- SDG 3 – Gute Gesundheit und Wohlergehen
- SDG 4 – Qualitativ hochwertige Bildung
ASJC Scopus Sachgebiete
Schlagwörter
- Complex problem solving, Relational reasoning, Schema-governed category, Transfer