Measuring Mind Wandering During Online Lectures Assessed With EEG
Bibliographic record
Abstract
Mind wandering can inhibit learning in multimedia classrooms, such as when watching online lectures. One explanation for this effect is that periods of mind wandering cause learners' attention to be redirected from the learning material toward task-unrelated thoughts. The present study explored the relationship between mind wandering and online education using electroencephalography (EEG). Participants were asked to attend to a 75 minute educational video lecture, while task-irrelevant auditory tones played at random intervals. The tones were of two distinct pitches, with one occurring frequently (80%) and the other infrequently (20%). Participants were prompted at pseudo-random intervals during the lecture to report their degree of experienced mind wandering. EEG spectral power and event-related potentials (ERP) were compared between states of high and low degrees of self-reported mind wandering. Participants also performed pre/post quizzes based on the lecture material. Results revealed significantly higher delta, theta and alpha band activity during mind wandering, as well as a decreased P2 ERP amplitude. Further, learning scores (improvement on quizzes pre to post) were lower among participants who reported higher degrees of mind wandering throughout the video. The results are consistent with a view that mind wandering during e-learning is characterized by a shift in attention away from the external world and toward internal thoughts, which may be a cause of reduced learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".