Technology and K-12 Environmental Education in Ontario, Canada: Teacher Perceptions and Recommendations
Bibliographic record
Abstract
This research explores the perspectives of kindergarten through to Grade 12 (K-12) teachers on incorporating information and communication technology (ICT) into the environmental education (EE) curriculum. In the context of the increasing influence of ICT in education, this study examines both the potential enhancements ICT offers to EE and the challenges it poses. Using data from an online survey and an in-person focus group, the investigation addresses the capacity of ICT to promote environmental stewardship and personal growth, alongside concerns regarding technology’s potential to alienate students from nature and the divided opinions among educators regarding optimal technology use. Attention is given to systemic barriers that complicate EE integration and the variability of its implementation in Ontario, Canada, where EE is mandated across K-12 curricula. The findings illuminate educators’ concerns about digital dependencies among their students and the difficulty they face in striking a balance between the use of ICT and non-technical pedagogical approaches when engaging students in environmental lessons. Importantly, study participants identified limited contemporary and timely technological tools to support EE delivery that deemphasize using personal mobile devices (e.g., smartphones and tablets). In response, we recommend three forms of technology (and accompanying lesson ideas) that are affordable, easy to integrate into classrooms, and do not require off-site trips, thereby enhancing accessibility and equity. This study’s implications are aimed at educators, policymakers, and stakeholders seeking to enhance EE delivery within a technologically evolving educational framework and ensure the development of environmentally conscious students.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".