Walking in their footsteps: Historical empathy and experiential learning on battlefield study tours
Bibliographic record
Abstract
Reflecting on my experience leading battlefield study tours for secondary school students, this article explores the pedagogical benefits of experiential learning for fostering historical empathy. I suggest that experiential learning offers students opportunities to engage with both the cognitive and affective dimensions of history, which are necessary for developing historical empathy. In doing so, I adopt Davison’s (2017) conceptualization of historical empathy as a cognitive-affective “pathway” to demonstrate how experiential learning supports students’ understandings of perspectives and experiences in the past. On the study tours, students entered the past by developing emotional connections to historical actors and particular places, based on their family histories and backgrounds. While visiting historic sites and interpreting battlefield landscapes, students worked with the historical record to build contextual knowledge and consider diverse perspectives. Finally, students exited the past to form ethical judgments about the World Wars and applied their learning within their communities back home in Canada.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".