Redesigning an Environmental Curriculum for Student Engagement
Bibliographic record
Abstract
Apart from equipping learners with 21st-century skills, environmental science (ES) education fosters problem-solving, creativity, critical thinking, and a sense of responsibility and agency in children. Community science centers contribute to ES education by stirring up interest, enthusiasm, and awareness in both science and environmental issues; however, they face challenges. This case study uses narrative inquiry to explore how two preservice teachers identified opportunities for improvement at a community science center, and how they consequently redesigned the curriculum to improve teaching and learning. The pedagogical opportunities for improvement at the science center covered learner experiences, teaching experiences and backgrounds, scaffolded learning, learner engagement with resources, learner connections, and programming at the center. The successful curriculum redesign was influenced by the Technological Pedagogical Content Knowledge (TPACK) model, which provided strategies for improvement. Our findings highlight pedagogical strategies and recommendations to improve ES curricula for young learners at informal learning centers.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".