Engagement, Disengagement, and Challenges in High School History Classrooms
Bibliographic record
Abstract
Polls and controversial figures like Jack Granatstein suggest that Canadians lack knowledge in Canadian History. Inspired by these claims, this study aims to examine strategies that Ontario History teachers use to engage grade 10 Applied Canadian History students. It further delves into the strategies, resources, and challenges teachers experience when seeking to engage their students in History. Two semi-structured interviews were conducted with teachers experienced in teaching grade 10 Applied Canadian History in Ontario, and literature was reviewed focusing on engagement in History. Findings suggest that Ontario Applied History teachers need to utilize primary sources, artefacts, and have students experience History for the best perceived outcomes. Further to this, teachers report encountering challenges such as the time to culminate primary sources, lack of support for the Applied stream, and encountering students with low motivation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".