Rapid Deployment and Evaluation of Mobile Serious Games: A Cognitive Assessment Case Study
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
Serious games are proposed as a more efficient and enjoyable way to carry out cognitive assessment. We compare prediction of cognitive ability with a purpose-built serious game and with a similar game built using a game engine. In an experiment conducted with 28 participants, performance on the two games is assessed relative to three cognitive abilities, using two different tablet sizes and two different input methods. The results for the game-engine variant were similar to the purpose-built game, where both games significantly predicted performance on the three cognitive abilities, and were sensitive to the effects of age. Performance on both games was not significantly affected by tablet size or input method. These results support earlier findings that serious games can provide valid cognitive assessment, and they show that game engines can be used to develop serious games for cognitive assessment, cost effectively and without loss of predictive validity.
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 | 0.003 | 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