Opportunities and Barriers for Ontario Teachers in their Delivery of Environmental Education using Information and Communication Technologies
Bibliographic record
Abstract
<div>Teaching and learning about the environment in the present day is filled with possibility, challenge, and urgency. Government-mandated environmental curriculum, where it exists, can provide important pedagogical and content guidance. However, bottom-up and teacher-initiated approaches to the timely delivery of relevant and contemporary environmental education are required. This research identifies and describes barriers and opportunities to the delivery of environmental education in Ontario, Canada. It also explores the receptivity of teachers to the use of information and communication technologies (ICTs) as tools to complement existing instructional methods, and proposes refocusing on local geographies to exemplify human-environment interactions. Two methods of data collection were used: an online survey (n=54), a semi-structured focus group (n=18). Both approaches engaged teachers within the Toronto District School Board (TDSB), Canada’s largest school board. Three-quarters of study participants (76%) identified that teaching about the environment with hands-on assignments (e.g., data collection, field observations, experiments) was beneficial to student learning. A similar majority of teachers (74%) agreed that environmental education was an afterthought in the Ontario curriculum. A strong positive response from teachers was solicited when they were asked if ICTs were useful teaching tools. Using a Mantel-Haenszel test of trend, teachers’ perception of student enjoyment in, and engagement with, subject matter was shown to be significantly positively associated with the frequency of environmental content included in their lessons (p<0.000). NVivo software was used with content arising from the focus group discussion, and a content analysis was run to identify the frequency with which educators described current environment-related teaching, providing both new details and offering greater context to the online survey responses. While highlighting systemic weaknesses in the delivery of environmental education in Ontario, this study identified tangible avenues that teachers and schools can pursue in order to bridge the gap between environmental rhetoric and action-oriented practice.</div><div><br></div><div>Keywords: environmental education, place-based learning, Ontario, information and communication technology (ICTs), policy, mixed methods </div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".