“Thank you for the Excitement”: Pre-service Teachers’ Engagement in and Outcomes from Culture and Language Escape Room Experiences
Bibliographic record
Abstract
This article reports a case study of eight undergraduate pre-service English-language-learner (ELL) teachers who participated in four educational escape room (EER) experiences to learn about their future students’ languages and cultures. Using task engagement facilitators as a theoretical framework, the researchers designed the experiences and explored the pre-service teachers’ perceptions and knowledge gains. Following a design-based research methodology, the study describes the escape rooms, presents both numeric and descriptive data from observations, surveys, and interviews, and provides EER design principles arising from the results. The study found that all the pre-service ELL teacher participants were engaged in the experiences, and they noted that the use of escapes is a compelling way to study their future students’ backgrounds during their teacher education program. Participants had different outcomes, but they all described the experiences as raising their awareness of culture. Implications include that task engagement facilitators can be integrated usefully into ELL teacher education experiences, and that following specific EER design principles, including task engagement facilitators, can make the experience effective and engaging. This paper contributes to the literature by employing frameworks and methods not yet common in the language teacher education literature and by providing explicit guidelines for EER use for language teacher education programs.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".