Decoding Spatial Empathy: Using Digital Storytelling to Overcome Barriers in Geographic Understanding
Bibliographic record
Abstract
This study applies the Decoding the Disciplines framework to address a persistent bottleneck in geographic education: students' difficulty developing spatial empathy in increasingly hybridized learning environments. Spatial empathy—the ability to deeply connect with and understand places and their inhabitants beyond cognitive recognition—requires students to overcome ontological and epistemological barriers rooted in colonial perspectives of space. Through careful analysis, we identify expert mental moves that geographers employ, including multi-sensory engagement with place, recognition of temporal layers and multiple narratives, embodied spatial cognition, connecting personal experience to broader contexts, and transferring spatial understanding across physical and digital realms. We created immersive 3D audio experiences featuring pandemic-related campus narratives and measured their impact on 47 university students. Results demonstrate significant differences in emotional responses between students who experienced campus closure (more negative emotional tone, higher intensity) versus those who didn't, though both groups reported high empathy levels. Qualitative data revealed three key themes: perspective-taking, accessibility awareness, and sensory connection to place. Digital storytelling effectively modeled expert mental moves by making tacit knowledge visible and fostering embodied engagement through sonic pedagogies. This approach offers geography educators a framework for teaching place-based concepts in hybrid contexts while challenging visual dominance in spatial representation. Our findings extend the Decoding paradigm to encompass multi-sensory dimensions of spatial understanding, demonstrating how immersive soundscapes can bridge disconnections from place while fostering mutual understanding across diverse experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".