From Context Dependence to Scientific Understanding: Modeling Students’ Learning Progressions of Force Concepts via Latent Transition Analysis

In response to ongoing calls in physics and science education to support conceptual change and promote deeper scientific understanding, research on learning progressions (LPs) has increasingly focused on modeling how students' conceptual reasoning evolves over time. However, substantial evidence indicates that novice learners' understanding remains fragmented and highly sensitive to contextual features. This study investigates the relationship between students’ conceptual development and their dependence on context by employing a person-centered analytical method—Latent Transition Analysis (LTA). Pre- and post-test data were collected from 474 students enrolled in a calculus-based introductory physics course, using eight items selected from the 1995 version of the Force Concept Inventory (FCI). These items targeted two fundamental conceptual domains: Force and Motion (F&M) and Newton’s Third Law (NTL). The analysis identified four latent statuses for F&M and five for NTL, representing qualitatively distinct levels of understanding ranging from naïve to near-scientific. Results indicate a clear pattern: as students’ conceptual understanding progressed, their reliance on surface-level contextual features decreased. These findings suggest a dynamic and interdependent relationship between conceptual development and context sensitivity. This study demonstrates the potential of LTA to reveal developmental trajectories in students’ conceptual understanding and underscores the importance of incorporating contextual features in both instructional design and diagnostic assessment strategies.

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Publication Details

Journal
X-Disciplinarity
Published
2025-05-18
DOI
https://doi.org/10.63174/xdi.wgbq7700
Primary Topic
Science Education and Pedagogy
Type
article
Field-Weighted Citation Impact
0.00

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article

From Context Dependence to Scientific Understanding: Modeling Students’ Learning Progressions of Force Concepts via Latent Transition Analysis

Yang Xiao, Ying Nie, Lei Bao, Yue Ming et al.
1 links
X-Disciplinarity
Science Education and Pedagogy
article

From Context Dependence to Scientific Understanding: Modeling Students’ Learning Progressions of Force Concepts via Latent Transition Analysis

Yang Xiao, Ying Nie, Lei Bao, Yue Ming, Lan Yang, H. Yang, Lianxin Lian
article en

Abstract

In response to ongoing calls in physics and science education to support conceptual change and promote deeper scientific understanding, research on learning progressions (LPs) has increasingly focused on modeling how students' conceptual reasoning evolves over time. However, substantial evidence indicates that novice learners' understanding remains fragmented and highly sensitive to contextual features. This study investigates the relationship between students’ conceptual development and their dependence on context by employing a person-centered analytical method—Latent Transition Analysis (LTA). Pre- and post-test data were collected from 474 students enrolled in a calculus-based introductory physics course, using eight items selected from the 1995 version of the Force Concept Inventory (FCI). These items targeted two fundamental conceptual domains: Force and Motion (F&M) and Newton’s Third Law (NTL). The analysis identified four latent statuses for F&M and five for NTL, representing qualitatively distinct levels of understanding ranging from naïve to near-scientific. Results indicate a clear pattern: as students’ conceptual understanding progressed, their reliance on surface-level contextual features decreased. These findings suggest a dynamic and interdependent relationship between conceptual development and context sensitivity. This study demonstrates the potential of LTA to reveal developmental trajectories in students’ conceptual understanding and underscores the importance of incorporating contextual features in both instructional design and diagnostic assessment strategies.

X-DisciplinarityVol. 1(2)
South China Normal University (CN), Shanghai Liangyou (China) (CN), Jiangsu Yonggang Group (China) (CN), Kangmei Pharmaceutical (China) (CN), China GreatWall Technology Group (China) (CN), The Ohio State University (US)
National Social Science Fund of China
Openalex Percentile: Top 89%
Science Education and Pedagogy
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