Did You Get That? Predicting Learners’ Comprehension of a Video Lecture from Visualizations of Their Gaze Data
Bibliographic record
Abstract
In online lectures, unlike in face-to-face lectures, teachers lack access to (nonverbal) cues to check if their students are still "with them" and comprehend the lecture. The increasing availability of low-cost eye-trackers provides a promising solution. These devices measure unobtrusively where students look and can visualize these data to teachers. These visualizations might inform teachers about students' level of "with-me-ness" (i.e., do students look at the information that the teacher is currently talking about) and comprehension of the lecture, provided that (1) gaze measures of "with-me-ness" are related to comprehension, (2) people not trained in eye-tracking can predict students' comprehension from gaze visualizations, (3) we understand how different visualization techniques impact this prediction. We addressed these issues in two studies. In Study 1, 36 students watched a video lecture while being eye-tracked. The extent to which students looked at relevant information and the extent to which they looked at the same location as the teacher both correlated with students' comprehension (score on an open question) of the lecture. In Study 2, 50 participants watched visualizations of students' gaze (from Study 1), using six visualization techniques (dynamic and static versions of scanpaths, heatmaps, and focus maps) and were asked to predict students' posttest performance and to rate their ease of prediction. We found that people can use gaze visualizations to predict learners' comprehension above chance level, with minor differences between visualization techniques. Further research should investigate if teachers can act on the information provided by gaze visualizations and thereby improve students' learning.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".