Chapter 1 Historical Video Games and Teaching Practices
Bibliographic record
Abstract
Video games are both a medium and a social practice occupying a growing place in the lives of students. History teachers are increasingly using this medium in the classroom and taking into account the effects of this social practice on the construction of students’ historical culture. These teaching situations should be investigated, and history didacticians are doing so more and more frequently in Québec. Since the mythologies used in video games might differ from country to country, this issue should also be compared internationally. As part of research conducted over five years in some 20 French-language secondary schools in the Montreal region on how history teachers and their 4,000 students use “texts” of various genres from various media (artifacts, written archives, songs, films, manuals, photos, plays, novels, etc.), we analyzed the instructions, tasks, and materials used in 10 classes to learn ancient history (Sparta, 500 BCE, and Rome 50 BCE) with a commercial video game (Assassin’s Creed). In 2018 and 2019, we analyzed the material, observed classes (1,638 students), and interviewed 30 teachers and students. Our analysis shows that there is a great diversity of uses made in class by teachers, with very different results in terms of historical thinking development. The most promising practices involve some investigation and corpus analysis, but most of the time, students only have to look for information to transcribe.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".