Student Emotions with an Edu-game: A Detailed Analysis
Why this work is in the frame
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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 employed a standard method that relies on trained judges to report emotions over 20-second 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 inter-coder reliability, both aggregated and over individual students, that considers not only the matching by judges over emotion type, but also the number of emotions detected.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it