Towards Emotional Regulation in Intelligent Tutoring Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: The emotional factor has been never taken into account in Intelligent Tutoring Systems (ITS) until recently. However, emotions play a crucial role in cognitive processes particularly in learning tasks (Isen, 2000)0. Thus, our purpose in this research study was to analyse the effect of some tutoring actions on the learner’s emotional state in order to ascertain the feasibility of positive emotions inducing in ITSs. To achieve this aim, we developed a data structure web course and a virtual tutor using different pedagogical actions to induce positive emotions in the learner. We have conducted an experiment to collect, among other, participants ’ physiological responses after the tutoring actions. The results of this experimental study showed that certain actions have significant positive effects on the learner’s emotional state.
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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