The Impact of the Pandemic on Teachers' Attitudes toward Online Teaching
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».