Science teacher training programme in rural schools: An ODL lesson from Zimbabwe
Notice bibliographique
Résumé
<p>This case study looked at 76 randomly selected preservice science teachers from Mbire and Guruve districts who were learning at the Mushumbi Centre in Zimbabwe and assessed their motivations for enrolling under the Bindura University of Science Education (BUSE)’s Virtual and Open Distance Learning (VODL) programme. It also looked at the challenges they faced, their views on how instruction under the programme can be improved, and their deployment preferences after graduation. The districts are located in the remote Zambezi Valley, which is characterized by poor infrastructure, pests and diseases, frequent attacks by wild animals on people, domestic animals, and crops, harsh climatic conditions, and seasonal floods, which make it very difficult to attract and retain qualified teachers. Through targeted recruitment, BUSE’s VODL programme sought to train relief teachers already serving in the area in the hope that personal history and family connections would entice them to continue teaching in these areas after attaining their teacher certification. Data was collected using a questionnaire with closed and open-ended questions. Results obtained indicate that despite a lack of funding, a shortage of reading materials, and the nonavailability of e-learning facilities, the students were motivated to join the programme for personal and professional motives and that the students, the majority of whom had taught for two or more years in the districts, would prefer deployments in the area after graduation. The study therefore recommends that deliberate efforts be directed toward the targeted recruitment of school leavers and relief teachers from disadvantaged rural areas who possess the requisite minimum entry qualifications to train as science teachers in order to improve teacher retention in remote areas. Further research into the intrinsic problems in BUSE’s VODL programme and a close scrutiny of its course development techniques are also encouraged in order to overcome some of the problems encountered by both the students and the institution.</p>
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».