Modeling the Learner Preferences for Embodied Agents: Experimenting with the Control of Humor
Bibliographic record
Abstract
This research presents a model and an experiment on the integration of personality preferences in support systems for learning. We will present briefly the context of the research on the access to learner and group models, than the theoretical background on personalization of interface and more specifically of the functions of humor in general and how it can be used to integrate affective dimensions in tutoring interaction. This research stems partly from the Reeves and Nass [1] postulate that people will react the same way to a computer mediated interaction, then they do to a real interaction, and thus that their reaction to an humoristic tutor would be generally more positive than to the non-humoristic tutor: the tutor attracts more attention, the perceived usability, social presence and personalization are improved. We will present the design and experimentation of an open model support system, where feedback is given to learners on their progression, but also where preferences for support may be defined. It was experimented under two conditions “with ” and “without humor ” and qualitative attitudes measures where taken. Though results are only preliminary, this study of the impact of humor suggests various considerations on how personality aspects can be integrated and their impact studied in ITS systems.
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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 | 0.001 | 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) | 0.003 | 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".