Escape Room Game Design Has Teaching Potential for Engaging With Misinformation Behaviors
Bibliographic record
Abstract
A Review of: Cho, Y., Coward, C., Lackner, J., Windleharth, T. W., & Lee, J. H. (2023). The use of an escape room as an immersive learning environment for building resilience to misinformation. Journal of Librarianship and Information Science, 57(2), 524-538. https://doi.org/10.1177/09610006231208027 Objective – To evaluate the efficacy of game-based learning as a tool to teach misinformation recognition strategies, with an additional focus on cognitive bias, emotion, and attitudes related to misinformation. The authors also explore librarians’ responses to the use of the game as library educational programming to identify strengths and areas of concern in the game design process. Design – Mixed-methods user study combining participant survey data with inductive and deductive coding of qualitative data extracted from video recordings and open-ended questions. Setting – Washington State public libraries, primarily city and suburban locations. Subjects – 80 public library patrons and 6 public librarians in gameplay; 50 patrons additionally completed the optional survey. Methods – For this exploratory study, authors designed a misinformation escape room game based on interviews with librarians, college student input, and escape room developer collaboration. The authors recruited and trained public librarians to host the game and facilitate follow-up discussions, then recruited 80 participants via communication channels chosen by the public librarians, including newsletters, websites, and social media. The game was run 17 times across six locations. Game participation and discussions were recorded and transcribed. Following the game, participants were asked to complete a survey that included quantitative and qualitative responses, and librarians participated in a focus group after completing all of their game sessions. Researchers then coded the responses with both predefined and emergent codes. Main Results – Researchers found that participants were exposed to new misinformation techniques during the game, especially deepfake images and videos. Participants stated in the follow-up discussion that the use of misinformation created a sense of vulnerability, and they reflected on their individual responsibility regarding the spread of misinformation, including that once misinformation is shared, it cannot truly be unshared. As a result of the game, many participants highlighted the need for greater caution and critical thinking when engaging with information. Participants appreciated that the game was both fun and cooperative while affirming that it improved their awareness of misinformation techniques. Conclusion – The combination of immersive experience, collaborative play, and the post-game discussion led to better awareness of modern misinformation techniques and a willingness to reflect on the experience of engaging in misinformation. The post-game debrief is particularly important as it allows participants to form connections between the game and real-world misinformation experiences. Further research could pursue more conclusive evidence regarding patterns in misinformation experiences, or a longitudinal study could explore the game’s long-term effects on participants’ attitudes and behaviors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".