The Effect of Online Education on the Teachers’ Working Time Efficiency
Bibliographic record
Abstract
The aim of this work was to study how the teachers’ working time efficiency changed with the transition to an online education. This work is the first to compare the teachers’ working time effectiveness for two forms of teaching. We used the evaluation of the teacher’s self-efficacy and the criteria for evaluating the educational course quality for this purpose. Teachers with the same efficiency and quality indicators were selected. The time spent by teachers on preparing and conducting offline (control group) and online (experimental group) classes was measured. Since the quality of the courses and the teachers’ effectiveness were the same, it was sufficient to compare only the time spent by teachers to achieve the same educational goals in order to compare working time efficiency. The research also involved a questionnaire survey. This study found that the current working time efficiency of teachers who work online is less than those who teach traditionally. However, the efficiency of the efficiency of teacher’s working time spent on the presentation of new material is higher with online education. This result can be achieved through using self-made video recordings of lectures. The results of the conducted research are of practical importance for teachers who work online. It allows finding the optimal ratio of time spent and results achieved. It is reasonable to further study the influence of a teacher’s age, gender, and pedagogical experience on the working time efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".