Spatial and Collective Learning through Mobile Sensory Photography and Creative Cartography
Bibliographic record
Abstract
This dissertation explores how the educational tools of mobile sensory photography and creative cartography can be used in art education settings to encourage youth to attend to their everyday surroundings. Mobile sensory photography utilizes the connectivity of mobile devices to enable learning collectives to create and share photographs of their everyday surroundings. I anchored this process in Sensory Studies as a strategy to engage learners with the everyday places they inhabit. I use creative cartography as an umbrella term to describe a diverse set of practices that use maps to represent subjective, social, collective, political, and spatial experiences. A central question I examine is what kinds of spatial and collective learning occur when integrating these tools in art classrooms. My understanding of spatial and collective learning is rooted in the complementary theoretical frameworks of complexity thinking and spatiality, which regard learning as social, relational, and situated within existing spatio-temporal relations. I used the methodology of Design-Based Research (DBR) to integrate these tools in high school art classrooms. I worked collaboratively with two art teachers at two different schools in Montreal to design and test a series of educational activities that involved taking sensory photographs of everyday places and sharing these images on social media and online mapping networks. By analyzing student and teacher interviews and their artistic productions, I was able to develop pedagogical strategies and a spatial theoretical framework for using mobile devices to enrich teaching and learning in art classrooms. This research has convinced me of the pertinence of attending to the spatial and temporal dimension of teaching and learning in physical and online spaces, and the symbiotic relationship between photography, the senses, and place-based learning. I also found the particular form of creative cartography that I termed Collective Online Sensory Mapping (COSM) to be a powerful tool for enabling a group of learners to attend to their everyday surroundings, express their identities, and learn about others. \nKeywords: Mobile sensory photography, creative cartography, spatiality in education, senses in Art Education
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".