Prediction of Student Achievement Goals and Emotion Valence during Interaction with Pedagogical Agents
Why this work is in the frame
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Bibliographic record
Abstract
There is evidence that Pedagogical Agents (PA) can influence students' emotions while learning with Intelligent Tutoring Systems, and that this influence is modulated by the students' achievement goals for learning. This suggests that students may benefit from personalized PAs that could rectify episodes of negative affect depending on their achievement goals. To ascertain the possibility of devising such personalized PAs, this paper investigates the real-time prediction of both students' achievement goals and affective valence while interacting with MetaTutor, an agent-based intelligent tutoring system. We train classifiers using eye-tracking data to make such prediction, and show that these classifiers can outperform a majority-class baseline at predicting both achievement goals and emotion valence.
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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 | 0.000 |
| 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) | 0.000 | 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