USE OF ELECTRODERMAL WRISTBANDS TO MEASURE STUDENTS' COGNITIVE ENGAGEMENT IN THE CLASSROOM
Bibliographic record
Abstract
A pilot project was conducted to study the feasibility of using electrodermal activity sensors embedded in a watch-like device to measure skin conductivity in real time. In the field of education, it may be interesting to use this technology to assess the students' cognitive engagement in the classroom. A few volunteer students as well as the professor were wearing an Empatica E4 wristband during some class periods where different activities were organized such as lectures, workshops and exams. Monitoring several individuals simultaneously makes possible to compare the collected data among students and between the students and the professor. Also, since the activities were weekly repeated, it was possible to assess to which extent the observed patterns were similar from one group to the other. In brief, the collected data is very difficult to interpret, since some external factors seem to have a significant effect on the measurements. Indeed, discrepancies are observed in the data curves representing the students’ electrodermal activity. Also, the data generated by the professor is quite different from one group to the other, even if he repeated the exact same activities at two different times of the week. It is suggested to improve the understanding of all the phenomena that could affect the electrodermal activity measurements before trying to draw conclusions related to the students’ cognitive engagement in the classroom.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".