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Auteurs principaux: Xu, Yuanyuan, Sun, Zhehao, Zhen, Chi, Lin, Yin-Shan, Thorogood, Miles, Smith, Megan, Lasserre, Patricia, Dulic, Aleksandra
Format: Preprint
Publié: 2026
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Accès en ligne:https://arxiv.org/abs/2604.15317
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author Xu, Yuanyuan
Sun, Zhehao
Zhen, Chi
Lin, Yin-Shan
Thorogood, Miles
Smith, Megan
Lasserre, Patricia
Dulic, Aleksandra
author_facet Xu, Yuanyuan
Sun, Zhehao
Zhen, Chi
Lin, Yin-Shan
Thorogood, Miles
Smith, Megan
Lasserre, Patricia
Dulic, Aleksandra
contents As the global climate crisis intensifies, 3D video games have emerged as powerful, interactive simulations for Environmental Education (EE). However, empirical assessment of their pedagogical efficacy remains epistemologically challenged. Traditional evaluation metrics, such as pre-post surveys, often suffer from response bias and fail to capture the nuanced, emergent psychological shifts players experience during gameplay. This paper proposes a novel, non-intrusive approach: utilizing Semantic Network Analysis (SNA) to map the 'unsupervised' cognitive structures of players. We scraped and qualitatively filtered 1,825 rich-text user reviews from Steam for two distinct titles representing opposing ecological philosophies: Eco (anthropocentric systemic management) and WolfQuest (biocentric embodied survival). By constructing co-occurrence networks and calculating topological metrics, we visualized the divergence in how players conceptualize human-nature relationships. Results indicate a fundamental pedagogical split: Eco promotes 'Socio-Political Cognition,' where environmental challenges are framed as legislative and economic frictions; conversely, WolfQuest fosters 'Effective Empathy,' where players internalize the fragility of life through the vulnerability of the avatar. We argue that semantic topology offers a rigorous methodological tool for serious games assessment, revealing that effective environmental education requires a strategic tension between systemic logic and emotional resonance.
format Preprint
id arxiv_https___arxiv_org_abs_2604_15317
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Mapping Ecological Empathy: A Semantic Network Analysis of Player Perceptions in 3D Environmental Education Games
Xu, Yuanyuan
Sun, Zhehao
Zhen, Chi
Lin, Yin-Shan
Thorogood, Miles
Smith, Megan
Lasserre, Patricia
Dulic, Aleksandra
Human-Computer Interaction
As the global climate crisis intensifies, 3D video games have emerged as powerful, interactive simulations for Environmental Education (EE). However, empirical assessment of their pedagogical efficacy remains epistemologically challenged. Traditional evaluation metrics, such as pre-post surveys, often suffer from response bias and fail to capture the nuanced, emergent psychological shifts players experience during gameplay. This paper proposes a novel, non-intrusive approach: utilizing Semantic Network Analysis (SNA) to map the 'unsupervised' cognitive structures of players. We scraped and qualitatively filtered 1,825 rich-text user reviews from Steam for two distinct titles representing opposing ecological philosophies: Eco (anthropocentric systemic management) and WolfQuest (biocentric embodied survival). By constructing co-occurrence networks and calculating topological metrics, we visualized the divergence in how players conceptualize human-nature relationships. Results indicate a fundamental pedagogical split: Eco promotes 'Socio-Political Cognition,' where environmental challenges are framed as legislative and economic frictions; conversely, WolfQuest fosters 'Effective Empathy,' where players internalize the fragility of life through the vulnerability of the avatar. We argue that semantic topology offers a rigorous methodological tool for serious games assessment, revealing that effective environmental education requires a strategic tension between systemic logic and emotional resonance.
title Mapping Ecological Empathy: A Semantic Network Analysis of Player Perceptions in 3D Environmental Education Games
topic Human-Computer Interaction
url https://arxiv.org/abs/2604.15317