Eye-tracking as a source of information for automatically predicting user learning with MetaTutor, an intelligent tutoring system to support self-regulated learning
Bibliographic record
Abstract
Student modeling has been gaining interest among researchers recently. A lot of work has been done on exploring value of interface actions on predicting learning. The focus of this thesis is on using eye-tracking data and action logs for building classifies to infer a student’s learning performance during interaction with MetaTutor, an Intelligent Tutoring System( ITS) that scaffolds self-regulated learning (SRL). Research has shown that eye tracking can be a valuable source for predicting learning for certain learning environments. In this thesis we extend these results by showing that modeling based on eye-tracking data is a valuable approach to predicting learning for another type of ITS, a hypermedia learning environment. We use data from 50 students (collected by a research team at McGill University, which also designed MetaTutor) to compare the performance of actions and eye-tracking data (1) after a complete interaction, and (2) during interaction when different amounts of gaze and action data are available. We built several classifiers using common machine learning algorithms and techniques, with feature sets that are based on (1) eye-tracking data only, (2) actions data only and (3) eye-tracking and actions data combined. The results we found show that eye-tracking data brings important information in predicting student’s performance for an ITS supporting SRL in both overall and over time analysis. The features used for training classifiers suggest that usage of SRL tools available in MetaTutor can be a good predictor of 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".