Interdisciplinary Education in the Elementary Curriculum: Exploring Teacher Perceptions and Practices
Bibliographic record
Abstract
This research study focused on interdisciplinary education practices in elementary schools and classrooms. The main research question was: how does a sample of Ontario elementary educators design and implement interdisciplinary lessons in the classroom, and what outcomes do they observe from students? This question was investigated using semi-structured interviews with two elementary educators working in schools in Ontario. Findings of this study suggest that a variety of subjects can be combined in an interdisciplinary curriculum, but certain subjects should be taught in isolation. Furthermore, educators identified external frameworks and published materials that were useful resources for educators planning interdisciplinary curriculum. Another finding was that participants identified clear starting points for planning and assessing an interdisciplinary curriculum. Recommendations arising from this research are that teacher education should explicitly address interdisciplinary education. A whole school approach is also more supportive of teachers in their interdisciplinary practices and consequently, administrators may want to consider adopting school-wide interdisciplinary practices. Finally, teachers looking to implement interdisciplinary practices in their classroom should seek out opportunities to collaborate with other educators in order to plan a cohesive interdisciplinary curriculum.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".