Analysis of Learner's Behavior in Online Video Course Based on Eye Movement Tracking
Bibliographic record
Abstract
The interdisciplinary course combines two or more academic disciplines from different fields, requiring learners to know many areas. In e-learning, the students may have different backgrounds and knowledge levels. It is critical for a teacher to understand the e-learners’ difficulties and interests of various course concepts and their preferred learning styles. As eye movement is essential to recognize the students' learning behavior during the learning process, this study proposed a knowledge unit personalized classification method based on eye movement tracking in order to assist teachers to measure knowledge units' difficulty and importance for the students of different knowledge levels. The course teaching content with knowledge units is prepared in the logical and pedagogical organization. Then, we calculate the time spent on each knowledge unit based on eye fixations. A classification approach based on student learning time analysis was adopted to categorize the difficulty and importance level of knowledge units for different students. Finally, we used learners' knowledge levels based on the questionnaire to evaluate this proposed approach. The evaluation results show the effectiveness of the approach.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".