Educational AR Board Game: Teach History and Spread Awareness About an Immoral Social Practice Through the Medium of an Augmented Reality Board Game
Bibliographic record
Abstract
Purpose. This thesis investigates the potential of a custom-built hybrid educational board game as a tool to teach history and spread awareness on a social tradition called Sati, where a widow burns on the funeral pyre with her dead husband. Furthermore, it investigates if a board game is an effective tool to teach an academic subject such as History in a school setting. Method. Participant behaviour and emotional connection regarding the social tradition as well as the amount of historical knowledge gained were observed during a pilot study of SUTTEE. A total of 12 students participated in the pilot study: 6 Indian students who were aware of Sati, and 6 non-Indian students who were unaware of the tradition. Results. Student participation and understanding of historical context improved after playing the game twice. Those unaware of the tradition accepted that they had learned a lot about it after playing the game, and the group that was aware of such a tradition acknowledged that the game provided a deep understanding of the social tradition and the history of India. Conclusion. Participant feedback and observational analysis indicate that a group of people playing an educational game can result in gaining extra knowledge than an individual playing and learning by himself. Furthermore, time constrained decision-making in a game environment helps to maintain student engagement, motivation to play and learn facts for the purpose of winning game. However, an educational game cannot replace books, but it can be used as a pedagogical approach to enhance learning and teaching methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".