Gamification of Life: Playing Computer Games to Learn, Train, and Improve Cognitively
Bibliographic record
Abstract
This paper describes various ways in which computer games may be used throughout life to achieve goals such as improved reaction time, reduced memory loss, or improved understanding of subject-related concepts. It also describes project conducted in our research lab, where we work on finding ways to measure and potentially improve children’s cognitive processing (e.g., visual, auditory, and conceptual) through playing computer games. Our goals are to find the kind of cognitive effects, both major and minor, that specific computer games in our repository may have on children; find ways to evaluate a child’s performance during play, taking into account the child’s demographics, the gaming scores achieved, and time spent playing; relate the characteristics of the games and the child’s performance in play to possible strengths and weaknesses in the child’s cognitive processing; and to recommend remediation, in terms of the types of games that may be useful for the child to play next. Present state of our work is described, together with our short term and long term plans. DOI: 10.5901/jesr.2013.v3n8p83
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".