Requirements-Based Design of Serious Games and Learning Software
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
A serious game can be entertaining and enjoyable, but it is designed to facilitate the acquisition of skills and knowledge performance in the workplace, classroom, or therapeutic context. Claims of improvement can be validated through assessments successful, measurable practice beyond the game experience, the targeted context of the workplace, classroom, or clinical using the same tools as multiple traits and multiple measure (MTMM) models. This chapter provides a post-mortem describing the development of the initial design and development of a measurable model to inform the design requirements for validation for a serious game. In this chapter, the reader will gain insight into the implementation of lean process, design thinking, and field observations for generative research. This data informs the assessments and measurement of performance, validated through the MTMM model criteria for requirements. The emphasis examines the role of research insights for onboarding and professional development of newly hired certified nursing assistants in a long-term care facility.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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