Enhancing the Learning Experience Using Real-Time Cognitive Evaluation
Bibliographic record
Abstract
There is increasing evidence that learners' affective and cognitive states play a key role in the learning process.This suggests that systems which are able to detect these states can dynamically use adapted strategies to increase the pace of the learners' skill acquisition and improve their learning experience.In this work, we present a novel approach for automatically adapting the learning strategy in real-time according to the learner's detected mental state.The main goal of the approach is to maintain the learner in a positive state during a lesson by adaptively selecting the best interaction strategy between either using problem solving or worked examples.Two mental indexes, namely, cognitive load and mental engagement were extracted from electroencephalogram (EEG) signals, and used to adapt the system's interaction.The cognitive load index was developped by training and validating a prediction model on various types of memory and logical tasks.The engagement index was directly computed from the EEG signal frequency bands.An experiment with 14 learners was performed in order to evaluate this approach.The obtained results showed that using the learner's mental state to adapt the system's interaction has a positive impact on the learning outcomes, the learning experience and the learners' reported emotional states.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".