Runninghead: Join The (Eco) Club: Examining The Role Of Extracurriculars For Enhancing Environmental Education
Bibliographic record
Abstract
Ecoclubs are often devalued in their effectiveness at promoting Environmental Education because of their extracurricular nature. In theory, extracurricular programs offer a flexible learning opportunity that is founded in constructive learning where students can actively construct meaning while relating what they learn to real world issues and concepts (Olusegun 2015; Loughland, Reid, & Petocz 2010). The purpose of this study is to explore the roles of Ecoclubs in secondary schools as a means of contributing to the improvement of Environmental Education within Ontario. This qualitative study examines the effectiveness of Ecoclubs in developing environmental behaviour and action in students. A secondary data analysis of two case studies based within India and Australia was used to explore Ecoclubs within secondary schools. The Ecoclubs within these countries are framed within different contexts of Environmental Education, but nonetheless provide informative perspectives on Environmental Education in extracurriculars as a whole. The findings of my study suggested that Ecoclubs effectively promote learners’ environmental behaviour and actions through the development of the ecological self . The term ‘ecological self’ refers to connectedness with nature (Naess, 2005; Wilson, 1996) and existing with nature (Splitters, 2015). The findings of my study also highlighted the theme of interconnectedness through the cyclical progression and development of the ecological self and what I term ‘new’ environmental awareness. Integrating Environmental Education in extracurriculars suggests a need for more funding, improved management and execution of the program and more opportunities for the professional development of teachers in the field of Environmental Education.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".