The Impact of the Pandemic on Teachers' Attitudes toward Online Teaching
Bibliographic record
Abstract
The pandemic affected the most on the student population in the shortest time. The number of students whose studies were discontinued in March 2020 was about 300 million. The number reached to 1.6 billion on April 2020. To provide basic education for the students during the pandemic, many countries transferred to a mandate of distance learning for the education system. Use of different platforms for distance learning has helped reduce learning gaps. The Corona virus has forced educational systems to enter in a mode of digital transformation and to leave physical classrooms. The impact of this situation was felt at every level of the education system, from kindergartens to universities. This situation creates not only challenges, but many opportunities. Learning in the global open space creates new learning environments and the use of new learning materials.A case study was conducted in Israel. Self-prepared questionnaires were given to 123 educators who teach in elementary schools, middle schools and high schools Teachers who participated in case study teach exact sciences (mathematics, physics, science, and technology), multi-text subjects (language, literature and history) and foreign languages (Arabic and English). The purpose of the case study is to examine the habits of using tools for distance learning, to examine whether there is a difference in the habits of using technological tools between teachers at different age groups, to examine teachers' attitudes to distance learning assessment tools and to examine teachers' recommendations for different subjects and different age groups.The findings indicate that middle school and high school teachers prefer close help and support during online learning. High school and middle school teachers would prefer to continue distance learning even when face-to-face teaching is possible, unlike teachers who teach in elementary schools who prefer face-to-face teaching. The recommendations of high school teachers also indicated that it is necessary to increase the support system during online learning. When we examined the differences between the different subjects, we saw that teachers of science and mathematics subjects feel that most students do not take an active part during the lesson. Despite this, teachers that teach humanities subjects report that they feel that students are actively participating in online learning processes. Teachers report that changes must be done in assessment's methods. Teachers also report that during distance learning it is more difficult to follow students' progress.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".