An educational neuroscience perspective on tutoring: To what extent can electrophysiological measures improve the contingency of tutor scaffolding and feedback?
Bibliographic record
Abstract
The efficacy of tutoring as an instructional strategy mainly lies on the moment-by-moment correspondence between the help provided by a tutor and the tutee’s learning needs. The model presented in this paper emphasizes the pivotal role of monitoring and regulation, both by the tutor and the tutee, in attaining and maintaining affective and cognitive states conducive to student’s learning. This perspective highlights the hypothesis that the scarcity of the information that the tutor and tutee have access to during natural interaction leads to suboptimal learning interactions. As a potential response to this lack of information, it is argued that methodologies from cognitive and affective neuroscience can provide pertinent information during or after a learning interaction, and that this information can significantly empower students and tutors. Projected empirical research could lead to a dramatic reinterpretation of 35 years of already fruitful tutoring research.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".