Identification Elements Symmetry in Teaching Informatics in Czech Secondary School during the Covid-19 Outbreak from the Perspective of Students
Bibliographic record
Abstract
This article describes the research results aimed at distance education during the Covid-19 pandemic and closing schools and its symmetry with the classical state in terms of time, difficulty, and the mental and physical condition of students. An important aspect is therefore to maintain the symmetry of attitudes to teaching in face-to-face form and distance form. In terms of the eight-year gymnasium in the Czech Republic, students’ attitudes to the teaching subject informatics were investigated. The main research questions in our study dealt with whether students felt equally balanced regarding the amount of tasks and time taken for home preparation during the Covid-19 outbreak compared with the time before the quarantine and their condition (both mental and physical) during the Covid-19 outbreak. The research was conducted using an anonymous questionnaire, which was answered by 110 out of 180 students. According to the results, it is evident that students felt that during the distance education, there are more tasks compared to face-to-face ones. Students also claimed to spend more time learning at distance education than at school. On the other hand, they agreed that the self-education schedule is suitable for them. In terms of the questionnaire, their condition (both mental and physical) was also evaluated, which was slightly above the average.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".