Using Psychophysiological Data to Facilitate Reflective Conversations with Children about their Player Experiences
Bibliographic record
Abstract
Given the ubiquity of videogame play by children, concerns about the impacts of videogames, and increasing evidence of the benefits associated with digital play, it is important to evaluate the player experience (pX) of children. Moreover, it is essential to include children's voices in research about technologies designed for their use. However, eliciting rich, first-hand data on their experiences and perspectives is challenging because it requires children to articulate opinions in a pre-established context of general positivity towards games, alongside potential social desirability bias. In this paper, we present a methodology which uses children's psychophysiology as a prompt for reflective interviews, with two commercial off-the-shelf computer games (Rocket League and LEGO Builder's Journey). We consider the utility, reproducibility, reliability and validity of the method. In assessing the method, we discuss children's experience of wearing the sensors, the insights generated from the psychophysiological data, and the understanding that emerged when prompting children to reflect and comment on their own psychophysiological data.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".