A Brief Measure of Interpersonal Interaction for 2-Player Serious Games: Questionnaire Validation
Bibliographic record
Abstract
BACKGROUND: Competitive and cooperative serious games have become increasingly popular in areas such as rehabilitation and education and have several potential advantages over single-player games. However, they are not suitable for everyone, and the user experience in competitive and cooperative serious games depends on many factors. One important factor is the verbal interaction between players, but the effect of this factor has not been extensively studied because of the lack of a validated measurement tool. OBJECTIVE: This paper aimed to validate a brief questionnaire that measures the verbal interaction between 2 players of a serious game. The questionnaire consists of 8 questions pertaining to the amount of conversation, its valence (positive or negative emotion), and its game relatedness. METHODS: The questionnaire was validated with 30 pairs of participants who played a competitive serious game for 10 min while being recorded with cameras. The questionnaire was filled out by both participants, an in-person observer, and 2 members of our research group who watched the videos. Results from these raters were used to develop questionnaire instructions, and the finalized questionnaire was given to 2 additional raters who were trained on 5 videos and then rated the other 25 videos independently. RESULTS: The questionnaire's interrater reliability is excellent for the amount of conversation and its game relatedness (intraclass correlation coefficients [ICCs] above 0.9). Interrater reliability is fair to good for conversation valence (ICCs between 0.4 and 0.7). We believe that the lower interrater reliability for valence is primarily because of a limited spread of valence values in our sample. Furthermore, questionnaire ratings were significantly correlated with players' personality characteristics (eg, amount of conversation was correlated with extraversion) and pressure/tension experienced in the competitive game. CONCLUSIONS: The validated questionnaire has the potential to be a useful tool for studying user experience in competitive and cooperative serious games. Furthermore, it could be adapted for other applications such as entertainment games. However, it has only been validated with unimpaired university students in a 2-player competitive serious game and should next be validated with different target populations (eg, stroke survivors) and different game designs (eg, cooperative games).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".