Integrating a Video Game Recording Into a Qualitative Research Methods Course to Overcome COVID-19 Barriers to Teaching: Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Because of the COVID-19 pandemic, a doctoral-level public health qualitative research methods course was moved to a web-based format. One module originally required students to conduct in-person observations within the community, but the curriculum was adapted using a web-based video game exercise. OBJECTIVE: This study sought to evaluate students' perceptions of this adaptation and determine whether the new pilot format successfully met the module's original learning objectives. METHODS: Recorded footage of a video game session was used for students to observe, take field notes, and compare the results. Qualitative methods were used to evaluate student feedback on the curriculum and determine whether the original learning objectives were met. Data were analyzed using a directed content analysis. RESULTS: The findings demonstrate that all the learning objectives of this adapted qualitative observational research assignment using a web-based video game exercise were successfully met; namely, the students learned how to compare and contrast the observational notes of peers and to evaluate how personal bias and environmental factors can affect qualitative data collection. The assignment was also positively received by the students. CONCLUSIONS: The results align with the constructivist learning theory and other successful COVID-19 implementations. Our study demonstrates that the learning objectives of a qualitative observational assignment can be addressed given that there are proper forethought and delivery when the assignment is adapted to a web-based context using a video game exercise.
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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 | 0.066 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".