Introducing and Evaluating the Behavior of Non-verbal Features in the Virtual Learning
Bibliographic record
Abstract
The objective of this research is to introduce the behavior of non-verbal features of e-Learners in the virtual learning environment to establish a fair representation of the real user by an avatar who represents the e-Learner in the virtual environment and to distinguish the deportment of the non-verbal features during the virtual learning session. The eye blink and the head pose, which can be considered as important non-verbal cues are detected by using a web-camera utilizing mainly the geometric method. Then the detected non-verbal cues are transferred to the virtual environment. When the e-Learner blinks eyes or/and moves his/her head, the movements are appeared in the virtual environment through an identical avatar. The result of the experiment depicts that the avatar appearance which can detect the real user non-verbal behavior has got a notable impact on the viewers’ impression. In addition to that, the successful establishment of the real user’s non-verbal behavior in the virtual environment enhances the effectiveness of the communication since avatar is able to facilitate the fair representation to the real user with visualizing the real user non-verbal behavior in the virtual environment. The analysis of the non-verbal behavior during the virtual learning activity specified that the eye blinking rate has decreased by 35% during the problem based learning session than the relaxing time in virtual learning due to low stress level, attractive environment and visual fixation. Furthermore, results demonstrate that there is a relationship between the internal statuses of an e- Lerner with the rate of eye blinking.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".