Bibliographic record
Abstract
The benefits of taking students outside the classroom to learn are plentiful and have been well documented for decades. Yet, engagement in outdoor learning remains limited in Greater Saskatoon Catholic Schools (GSCS). In my research project I sought to better understand this reality and consider solutions. Through a division wide survey and an action research group, I asked teachers what were the barriers holding them back and more importantly, what supports would help them to engage more fully in outdoor learning. There were 69 survey respondents who answered questions about their experiences teaching outside the classroom. The action research group consisted of 7 teachers with a range of elementary school experience, from preschool to grade 8. Through dialogue, the group generated many possible solutions for their respective challenges and over the course of 8 months, I provided the participants with as many of the supports they brainstormed as possible. The reflections of the participants on their experiences indicated that effective professional development in outdoor learning should be holistic, encompassing multi-faceted supports which involve content knowledge, action competency, supportive relationships, worldview and motivation. These findings mirrored the themes uncovered in the literature reviewed. Additionally, the participants recognized certain factors that support outdoor learning, such as communication, scaffolding student experiences, creating a classroom culture, the potential of nearby learning locations, and “thinking outside” first. Moreover, they expressed a desire for a database organized with simple, straightforward, outdoor learning resources. As a researcher, I found considerable interest in outdoor learning but acknowledged that teachers need support to actualize the integration of this teaching practice. Intrinsic motivation is a crucial variable but there are also systemic factors which limit the engagement of teachers such as teaching with under supported, large, complex classes. Overall, the project demonstrated the value in: experiential learning techniques, responsive programming reflective of participant struggles, and professional development with continuity. Finally, to further reconciliation and the decolonization of education, I recognized the importance of authentically including Indigenous knowledge, critical reflection and positionality, and believe that outdoor learning should be a stepping stone towards land-based learning. I also came to better understand my own limitations as a non-Indigenous person trying to support land-based learning, a pedagogy grounded by Indigenous epistemologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.024 |
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".