Bibliographic record
Abstract
The use of animated pedagogical agents with emotional capabilities in an interactive learning environment has been found to have a positive impact on learners. The Greek philosopher Aristotle contended that three elements; emotion, logic, and character are crucial for successful persuasion, i.e. in winning others over to one's way of thinking. We have designed a pedagogical agent, which acts in an interactive learning environment, using the Cognitive Structure of Emotion model [Ortony, et al 1988] and the five-factor model of personality [McCrae and John 1992] thus taking into consideration Aristotel's elements of a good persuader. In this paper we investigate the persuasive impact of this emotional pedagogical agent on a group of learners. The results show that while not contributing any significant performance gain in learning, the incorporation of emotion changes the way students perceive the learning process, and makes it more engaging. We also found out that there are some gender-based and individual differences in the user perception of an emotional agent, which need to be taken into account when designing a more adaptive and "intelligent" emotional pedagogical agents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".