Undergraduate Teaching and<i>Assassin's Creed</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Digital tools are widely used in archaeology for excavation, research, and communication of results. Recently, due in large part to the COVID-19 pandemic, there has been a significant increase in the use of these resources in the classroom. The use of digital games for teaching undergraduate archaeology courses has been explored by a number of educators, but the majority of instructors continue to see this medium as lacking any particular educational merit. To combat this conclusion, in this article, the author explores some of the ways that unmodified digital games can be integrated into undergraduate archaeology courses to inspire critical discussions. She discusses two main types of games—conceptual simulations and realist simulations—to show how these can help students better understand theoretical approaches to archaeological interpretation and to consider the most effective form of archaeological reconstructions for different audiences. The author highlights her own experiences teaching with Assassin's Creed: Origins to show the benefits and challenges of working with this medium, and she includes student responses to the use of digital games in discussions. An example of a student assignment and an example of a project prompt are provided as supplemental materials to further encourage the use of digital games in the classroom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it