Toward avatar models to enhance performance and engagement in educational games
Bibliographic record
Abstract
This paper presents work toward better understanding the roles that avatars can play in supporting learning in educational games. Specifically, the paper presents results of empirical studies on the impact of avatar type on learner/player performance and engagement. These results constitute work establishing baseline understandings to inform our longer term goal of developing models that use dynamic avatars to best support learners in educational games. Our aim is motivated by a convergence of research in the social sciences establishing that identity plays an important role in learning. Of note, aspects of social identity (e.g., race, ethnicity, and gender) have been shown to impact student performance [1] via triggering stereotypes [2]. Recently, performance and engagement studies in our educational game for Science, Technology, Engineering and Mathematics (STEM) learning suggest these same phenomena can be activated through virtual avatars [3], [4]. Here, we present results of a comparative study between avatars in the likeness of players and avatars as geometric shapes. In our STEM learning game, results show that players that had selected and used a shape avatar had significantly higher performance than players that had customized and used a likeness avatar. Players using the shape avatar also had significantly higher self-reported engagement, despite having lower self-reported affect towards the avatar.
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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".