A Gamification Framework for Cognitive Assessment and Cognitive Training: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Cognitive tasks designed to measure or train cognition are often repetitive and presented in a monotonous manner, features that lead to participant boredom and disengagement. In this situation, participants do not put forth their best effort to do these tasks well. As a result, neuropsychologists cannot draw accurate conclusions about the data collected, and intervention effects are reduced. It is assumed that greater engagement and motivation will manifest as improved data quality. Gamification, the use of game elements in nongame settings, has been heralded as a potential mechanism for increasing participant engagement in cognitive tasks. Some studies have reported a positive effect of gamification on participant performance, although most studies have shown mixed results. One reason for these contrasting findings is that most studies have applied poor and heterogeneous design techniques to gamify cognitive tasks. Therefore, an appropriate gamification design framework is needed in these tasks. OBJECTIVE: This study aimed to propose a framework to guide the design of gamification in cognitive tasks. METHODS: We employed a design science research (DSR) approach to provide a framework for gamifying cognitive assessments and training by synthesizing current gamification design frameworks and gamification works in cognitive assessment and training, as well as incorporating field experiences. The prototypes of the framework were iteratively evaluated with 17 relevant experts. RESULTS: We proposed a framework consisting of 7 phases: (1) preparation; (2) knowing users; (3) exploring existing tools for assessing or training a targeted cognitive context and determining the suitability of game-up and mapping techniques; (4) ideation; (5) prototyping using the Objects, Mechanics, Dynamics, Emotions (OMDE) design guideline; (6) development; and (7) disseminating and monitoring. CONCLUSIONS: We found that (1) an intermediate design framework is needed to gamify cognitive tasks, which means that game elements should be selected by considering current cognitive assessment or training context characteristics since game elements may impose an irrelevant cognitive load that, in turn, can jeopardize data quality; (2) in addition to developing a new gamified cognitive task from scratch, 2 gamification techniques are widely used (first, adding game elements to an existing cognitive task and second, mapping an existing game to a cognitive function or impairment to assess or train it); and (3) further research is required to investigate the interplay of cognitive processes and game mechanics.
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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 | 0.048 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".