New Architecture of a Multi Agent System which Measures the Learner Brainwaves to Predict his Stress Level Variation
Bibliographic record
Abstract
Abstract: The stress factor plays an important role in learning tasks, especially when the learner is in front of an exam. Studying the stress level variation can then be very useful in a learning situation. In this paper, we conducted an experiment with 21 participants over a two day period. We have two objectives; the first one is to predict the stress level variation of the learner in relation to his electrical brain activity. To attain that goal, three personal and non-personal characteristics were used: the gender, the usual mode of study and the dominant activity between the first and second day of the experiment. An accuracy of 71 % was obtained by using the ID3 machine learning algorithm. The second goal is to propose an extension of a Multi Agent System (MAS) by adding the Stress Prediction Agent. This MAS uses Brainwaves to predict certain learner characteristics.
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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 | 0.001 | 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 | 0.001 | 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 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".