Challenges with Measuring Learning through Digital Gameplay in K-12 Classrooms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Videogames have long been lauded for their potential to increase engagement and enhance learning when used in classrooms. At the same time, how to best evaluate learning presents challenges, especially when the game does not have standardized assessments built-into it and when games are taken up in a wide variety of ways in quite diverse contexts. This article details the use of a geography game to support learning in 32 diverse classrooms in Ontario, Canada, alongside challenges with evaluating student learning using a game that did not have a built-in assessment system. In total, 795 students participated in the study. Classroom observations and interviews with teachers were triangulated with student pre and post evaluations. Results demonstrated that students did learn from gameplay, as demonstrated through multiple choice and short answer change scores in the pre to post evaluation, despite variations in duration of play and how the game was integrated in the classroom more generally.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it