Designing historical empathy learning experiences: a pedagogical tool for history teachers
Bibliographic record
Abstract
This article explores the pedagogies that history teachers use to foster historical empathy, drawing from interviews with secondary school history teachers in Canada. The findings highlight that historical empathy is nurtured by teachers over time using a variety of different teaching approaches and activities, tasks and projects. The article begins by examining the pedagogical choices that teachers make when designing and implementing historical empathy lessons, categorised as implicit, thematic, student-centred, scaffolded and comparative approaches. Next, consideration is given to the types of learning experiences that teachers use to develop historical empathy, and some of the opportunities and challenges involved. These learning experiences include role plays and simulations, first- and third-person writing tasks, experiential learning and virtual reality, and collaborative and project-based learning. The perspectives shared by the teachers in this study contributed towards the development of a research-informed pedagogical tool to guide the future design of learning experiences that foster historical empathy, which may be applied by teachers across educational jurisdictions, and adapted for various grade levels.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".