Predicting Learner Answers Correctness through Brainwaves Assesment and Emotional Dimensions
Bibliographic record
Abstract
We want to explore the relation between affective states, brainwaves and the learner answers during a multi-choice test questions. 24 participants were used in our experiment. While we were measuring their brainwaves, we asked them to answer 35 questions related to the 7 texts they read, for the first time, the day before. During the experiment, the participants can rate, at any time, their emotional dimensions (pleasure, arousal and dominance) on the Self-Assessment Manikin scale (SAM). Measuring the brainwaves determines the learner mental state and the emotional dimensions indicate the learner affective state. When a participant answers, he mentions if he knows the answer or not. Each answer can be either Right or False. The hypothesis of this paper is: “We can predict the learner's answers from his emotional dimensions and his brainwaves”. By using some machine learning techniques, we reached 90.49% accuracy. In a future work, these results will be implemented in an agent to improve the pedagogical strategies and the adaptation of the content within an Intelligent Tutoring System (STI).
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".