Optical Character Recognition (OCR)-Based and Gaussian Mixture Modeling-OCR-Based Slide-Level “With-Me-Ness”: Automated Measurement and Feedback of Learners’ Attention State during Video Lectures
Bibliographic record
Abstract
As video lectures are gaining more popularity, determining their effectiveness and obtaining valuable feedback have become necessary. To measure the learners’ attention state during video lectures, we specified the conceptual “with-me-ness” (WMN) as slide-level WMN (SL-WMN). The content domain on each slide was automatically extracted via an optical character recognition (OCR)-based method, while the eye gazing behaviors were analyzed through a Gaussian mixture modeling (GMM) fixation clustering method. Both domain-specific WMN and behavior-enriched WMN were then computed via OCR- and GMM-OCR-based methods to measure the learners’ attention levels. We conducted an experiment to collect in-lecture eye-tracking data, video recordings, and post-lecture test scores from 50 Grade 8 students. The results demonstrated that both OCR- and GMM-OCR-based SL-WMNs are reliable and compatible automatic measurements of learners’ attention states during video lectures. A survey from participating learners and lecturers also revealed highly favorable feedback for the developed SL-WMNs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".