Bibliographic record
Abstract
This paper develops an integrated model of playfulness and flow in virtual reality (VR) computer interactions using constructs drawn from the fields of occupational therapy, social psychology, applied psychology, and cultural anthropology. The purpose of this paper is to describe some ongoing research in our lab with disabled children and adults. We propose some testable hypotheses linked to constructs in the model that we have done some preliminary research in, and we suggest other hypothesis testing for future research. Key elements in the model that have been tested in our lab include self-efficacy and volition. They are seen as necessary characteristics of an individual that will influence his or her playfulness with the activity of VR. Flow and playfulness were two other constructs in the model that we hypothesize are related to volition and self-efficacy. We studied how flow is related to playfulness. The findings supported previous research on self-efficacy and volition as children and adults reported that being in control was important and stressed the value of being able to do new things. Playfulness was expressed as being able to feel presence with the activity. We looked at how playfulness was related to creativity, another construct in the model believed to be an outcome of playfulness. These studies will be reported in more detail in the paper as well as recommendations for further research.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".