Interaction with an Edu-Game: A Detailed Analysis of Student Emotions and Judges’ Perceptions
Bibliographic record
Abstract
We present the results of a study that explored the emotions experienced by students during interaction with an educational game for math (Heroes of Math Island). Starting from emotion frameworks in affective computing and education, we considered a larger set of emotions than in related research. For emotion labeling, we started from a standard methodology that relies on trained judges to report emotions over 20-s intervals, however, we asked judges to report all observed emotions in each interval, as opposed to only choosing one, as is standard practice. This variation allows us to discuss the appropriateness of this interval for emotion labeling. We present a detailed analysis of interrater reliability, both aggregated and over individual students, that considers not only labeling agreement among judges in terms of emotion type, but also with respect to the number of emotions detected. We also provide an analysis based on in-depth one-to-one interviews with judges, to gain insights on the challenges they encountered in labeling emotions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".