The Potential of Made-for-School History-Oriented Videogames in the Classroom
Bibliographic record
Abstract
This study examines the potential of Mission US, a history-oriented videogame, to foster historical thinking and reasoning skills in K-12 students. Historical thinking, which involves reasoning like historians, is a critical component of disciplinary literacy in history education. The game offers students interactive experiences by placing them in historical scenarios like the American Revolutionary War, the Great Depression, and the Civil Rights Movement, allowing them to engage in historical perspective-taking, cause and consequence analysis, and use of primary source evidence. Through a combination of content analysis and discourse analysis of gameplay, the study assessed how effectively the game mechanics support students in practicing historical thinking. Findings suggest Mission US provides limited but meaningful opportunities for deep engagement in historical thinking. The game includes numerous historical facts and scenarios but often falls short of requiring complex analysis or impactful decision-making. While mechanics such as dialogue selection and map navigation encourage perspective-taking, many interactions remain surface-level. The study concludes that the game, when used in conjunction with broader educational strategies, can enhance students' historical thinking skills but is less effective as a stand-alone teaching tool. The research highlights the importance of teacher involvement in guiding students through the game's content. Effective integration of the game into classrooms should include guided play sessions, collaborative learning, and reflective activities that bridge the gap between gameplay and real-world historical analysis. This study emphasizes the need for supplementary resources and careful handling of sensitive historical topics to fully realize the educational potential of history-oriented videogames.
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.
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 | 0.004 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".