Exploring Teacher Anxiety in Relation to Outdoor Teaching and Learning
Bibliographic record
Abstract
I believed students should be outdoors more often for learning, but I felt anxious about ensuring I was doing a good job as a teacher with respect to the curriculum. Why was I, a teacher with 30 years experience, feeling so anxious about this aspect of my teaching? I wondered, what was causing my anxiety and how I could resolve that feeling? Over a period of 5 months, I took my grade 4/5 class of 23 public elementary school students outdoors for teaching and learning at least two days a week out of the four days I worked. I conducted a self-study and took a qualitative research approach in collecting data. During our outdoor explorations, I kept a journal, wrote field notes, took photographs, examined students’ work in relation to our outdoor explorations, wrote reflections and discussed my journal entries and reflections with critical friends. Using a latent thematic analysis method allowed me to analyse and report on patterns within my data, independently, by comparing two data sets or by finding a topic in the whole data set. From analysis of my data, I was able to identify three main themes and sub themes: Teaching- curriculum and intentionality, Learning – my learning and student learning- physical activity, experiential, safety, and engagement and finally Emotions- my feelings and student feelings. The results showed my anxiety was evident across the three themes throughout but by the end of my self-study I had a better understanding of my anxiety and what was causing it. I saw the positive effects the outdoors had on mine and students’ wellbeing and across a variety of subject areas and competencies which helped me feel more confident about taking my class outdoors for explorations. I plan to continue developing a cross-curricular outdoor programme.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".