Analysis of Learning Effect through Voice Signal Analysis in Online Education Environment
Bibliographic record
Abstract
The new coronavirus infection (COVID-19) that appeared suddenly has permeated our daily lives and changed our way of life. In the field of education, education is being conducted in a non-face-to-face teaching method to prevent the spread of coronavirus. In the end, e-Learning, a new educational and training system that can provide a lifelong education environment in the 21st century information society, is increasing in use in the field of education. The biggest advantage of the online education system is that it provides an environment in which you can learn the necessary contents anytime, anywhere. However, there are cases in which the learning effect is reduced because various learning support is not available in the online space due to the sudden change of the educational environment due to the covid-19. Therefore, in this thesis, a study was conducted to analyze the learning effect of online education with voice signals for college students who are receiving education through an online education environment. To this end, the learning effect was classified into a group saying that the learning effect increased and a group that decreased due to online education, and the voices of the subjects in each group were collected. As a result of the experiment, the results of the vocal cord vibration (Pitch), Degree of voice breaks, Jitter and Shimmer were consistent among the elements of voice signal analysis between two groups.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".