Academic Journal

Cognitive processes of scientific problem-solving while using simulations: an expert-novice comparison.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Cognitive processes of scientific problem-solving while using simulations: an expert-novice comparison.
Συγγραφείς: Zeng, Yating, Tao, Jiayi
Πηγή: Frontiers in Psychology; 2026, p1-11, 11p
Θεματικοί όροι: Science education, Comparative studies, Protocol analysis (Cognition), Logic, Cognitive psychology, Simulation methods & models, Scientific method
Περίληψη: The ability to utilize simulations to address problems is emphasized in current science standards, but learners often struggle with scientific tasks involving simulations. Understanding the gap between beginner and expert scientific problem solvers in simulation settings is useful for helping students to develop their expertise. However, limited studies have investigated the differences in cognitive processes between specialists and novices when they tackle simulation-based scientific tasks. Therefore, in this study, the think-aloud method was adopted to compare the cognitive mechanisms of 12 science teachers (experts) and 12 middle school students (novices) as they used simulations to solve scientific problems. The results showed that the experts demonstrated greater cognitive engagement than the novices. Specifically, the experts exhibited a more comprehensive understanding of the tasks. The novices typically ran the simulations blindly and rarely recorded the specific details of the scientific phenomena. The experts also engaged in more logical reasoning, in-depth questioning, and rational decision-making, whereas the novices were more inclined to rely on their intuition and gave hasty responses. [ABSTRACT FROM AUTHOR]
Copyright of Frontiers in Psychology is the property of Frontiers Media S.A. 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.)
Βάση Δεδομένων: Complementary Index
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  Data: Cognitive processes of scientific problem-solving while using simulations: an expert-novice comparison.
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  Data: The ability to utilize simulations to address problems is emphasized in current science standards, but learners often struggle with scientific tasks involving simulations. Understanding the gap between beginner and expert scientific problem solvers in simulation settings is useful for helping students to develop their expertise. However, limited studies have investigated the differences in cognitive processes between specialists and novices when they tackle simulation-based scientific tasks. Therefore, in this study, the think-aloud method was adopted to compare the cognitive mechanisms of 12 science teachers (experts) and 12 middle school students (novices) as they used simulations to solve scientific problems. The results showed that the experts demonstrated greater cognitive engagement than the novices. Specifically, the experts exhibited a more comprehensive understanding of the tasks. The novices typically ran the simulations blindly and rarely recorded the specific details of the scientific phenomena. The experts also engaged in more logical reasoning, in-depth questioning, and rational decision-making, whereas the novices were more inclined to rely on their intuition and gave hasty responses. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Frontiers in Psychology is the property of Frontiers Media S.A. 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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        Value: 10.3389/fpsyg.2026.1846556
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              Text: 2026
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