Bibliographic record
Abstract
Affective learning is an important aspect of education that can be supported through games. This paper focuses on how games can and do address affective learning, especially in light of the growing trend of educational and serious games aiming at changing behavior and attitudes. To support affective learning though games, player emotions need to be recognized and interpreted, and an emotional experience needs to be created that motivates players and deepens learning. Moreover, there is also a need to understand affective representations and mechanisms that games support. The paper begins with a presentation of the different perspectives on affective learning, and then takes a focus on the socio-emotional component of the affective domain. An "affective walkthrough" technique is then introduced to understand and analyze affective strategies in games. This technique is then applied to the game Ico, showing its affective strategies and how these strategies can be leveraged for designing socio-emotional learning. The paper is concluded with an outline of an approach to designing games especially for affective learning, by identifying the key principles, creating a repertory of affective learning game patterns, and using methods to contextualize gameplay and facilitate learning.
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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".