Bridging the Divide: The Integration of Nature-Based Learning and Technology Together in Education
Bibliographic record
Abstract
This qualitative research study examined the challenges, benefits, and outcomes associated with the integration of nature-based learning and technology together in education, guided by the research question: How is a small sample of primary/junior elementary educators in Canada integrating nature-based learning and technology together to support students’ learning and development, and what outcomes do they observe from students? Convenience sampling was used to contact an elementary teacher and an outdoor education technician who integrate nature-based learning and technology together in their practice in the Greater Toronto Area. Data was collected through semi-structured interviews with these educators, and the transcripts were reviewed to reveal three main themes. The findings suggest that nature-technology integration has benefits for students in relation to academics, socio-emotional development, and engagement. The findings also propose that program goals, staff initiative, and access to resources are necessary supports for nature-technology integration. Finally, it was revealed that educators encounter challenges related to resources and staff initiative. The implications of these findings suggest that pre-service and in-service teachers require more knowledge and preparation to integrate nature and technology together, and that there needs to be increased support for this integration amongst all stakeholders including ministries of education, administrators, and teachers. Key Words: nature-based learning, technology, integration
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".