Macro- and Micro-Level Behavioral Patterns in Simulation-Based Scientific Inquiry: Linking Processes to Performance Among Elementary Students

Scientific inquiry is fundamental to science education, encompassing the processes through which students construct scientific knowledge and develop thinking skills. However, the unfolding of these inquiry processes and their relation to performance remain underexplored. Drawing on process data from a structured simulation-based assessment task, this study investigated the inquiry processes of 259 fourth-grade students. We applied a multi-analytic approach including sequential pattern mining, entropy analysis, and process mining to capture macro- and micro-level behavioral patterns and examine their associations with task performance operationalized by effectiveness and efficiency. Macro-level analyses revealed that effective students generally organized their inquiry processes into more iterative cycles of evidence collection, demonstrating a more dedicated approach before committing to a final response. Micro-level analyses further indicated that effective and efficient students showed better strategic coordination during experimentation. Together, these findings provide a multi-level characterization of elementary students' scientific inquiry processes and link inquiry patterns to task effectiveness and efficiency. The study also underscores the potential of process data from simulation-based assessments for diagnosing inquiry skills and informing the design of personalized scaffolds in elementary science education.

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Publication Details

Journal
Journal of Intelligence
Published
2026-01-04
DOI
https://doi.org/10.3390/jintelligence14010006
Primary Topic
Science Education and Pedagogy
Type
article
Field-Weighted Citation Impact
0.00

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article

Macro- and Micro-Level Behavioral Patterns in Simulation-Based Scientific Inquiry: Linking Processes to Performance Among Elementary Students

An Hu, Wei Tian, Shuang Wang, Tao Xin et al.
Journal of Intelligence
Science Education and Pedagogy
article

Macro- and Micro-Level Behavioral Patterns in Simulation-Based Scientific Inquiry: Linking Processes to Performance Among Elementary Students

An Hu, Wei Tian, Shuang Wang, Tao Xin, Lu Yuan
article en

Abstract

Scientific inquiry is fundamental to science education, encompassing the processes through which students construct scientific knowledge and develop thinking skills. However, the unfolding of these inquiry processes and their relation to performance remain underexplored. Drawing on process data from a structured simulation-based assessment task, this study investigated the inquiry processes of 259 fourth-grade students. We applied a multi-analytic approach including sequential pattern mining, entropy analysis, and process mining to capture macro- and micro-level behavioral patterns and examine their associations with task performance operationalized by effectiveness and efficiency. Macro-level analyses revealed that effective students generally organized their inquiry processes into more iterative cycles of evidence collection, demonstrating a more dedicated approach before committing to a final response. Micro-level analyses further indicated that effective and efficient students showed better strategic coordination during experimentation. Together, these findings provide a multi-level characterization of elementary students' scientific inquiry processes and link inquiry patterns to task effectiveness and efficiency. The study also underscores the potential of process data from simulation-based assessments for diagnosing inquiry skills and informing the design of personalized scaffolds in elementary science education.

Journal of IntelligenceVol. 14(1)
Peking University (CN), Beijing Normal University (CN), Shanghai Academy of Educational Sciences (CN)
Beijing Normal University
Quality Education
Openalex Percentile: Top 98%
Science Education and Pedagogy
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Macro- and Micro-Level Behavioral Patterns in Simulation-Based Scientific Inquiry: Linking Processes to Performance Among Elementary Students — An Hu, Wei Tian, et al. · Journal of Intelligence (2026) | TGRS Research Map | TGRS