Improving the Usability and Effectiveness of Online Learning: How Can Avatars help?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes Empathic Tutoring System (ETS) which uses character agents for online learning. Eye movement tracking and other physiological measures are used to personalize character agent behaviors (affective and instruction) in an e-learning environment. A prototype system reacts to learner's eye information in real-time, recording eye gaze and pupil dilation data (plus heart rate and skin conductance) during learning. Based on these measures, character agents inferred the attentional and motivational status of the learner and responded accordingly with affective and instructional behaviors. Character agents engage and direct the learner's attention while providing both generalized system help and personalized advice about the learning content. Feedbacks from preliminary usability studies may suggest that e-learning character agents reacting to eye gaze and physiological measures may heighten l earner concentration and lead to more effective learning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it