Blended Learning using agMOOCs as a Tool for Professional Development: A Case of Students of Agriculture in India
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Notice bibliographique
Résumé
According to University Grants Commission (a body of Government of India) Blended learning is an instructional methodology, a teaching and learning approach that combines face-to-face classroom methods with computer mediated activities to deliver instruction. agMOOCs a learning platform for students of agriculture and allied sciences has developed 22 MOOCs so far on agriculture and allied sciences since 2015. The platform was developed by Indian Institute of Technology, Kanpur (India) in collaboration with Commonwealth of Learning, Vancouver. Of which the author has offered three courses on agricultural extension. More than two million students have accessed the courses on agMOOCs platform and benefitted in their learning activities. In the last couple of years during the global pandemic period the educational activities were also facing difficulties. An effort was made to adopt the blended learning methodology for masters’ students of agriculture at Institute of Agricultural Sciences, Banaras Hindu University, Varanasi. The method of participant observation and discussion with learners were used to collect the data. Whole enumeration was the sample size. The data was analysed using descriptive qualitative methods by adopting steps viz., i. quick data, ii. Coding data, iii. Qualitative analysis and Quantitative analysis iv. Interpretation of results. Students were asked to go through the videos, PPTs and transcripts available on the platform before coming to the class. The classes were organised in hybrid mode (online as well as offline). The respective topics scheduled for the day were discussed in the class instead of explaining the contents as in case of regular classes. The results of the study reveal that 1. Enhancement in the grasping ability of students 2. Improvement in analysing the concepts and contents of the course 3. Enhanced interaction with course instructor 4. Surge in academic discussion abilities of learners 5. Augmentation in framing questions to be asked in the classroom. The challenges while using the methodology include maintaining learners interest over a period of time, preparation of contents for circulation before to be brief enough and providing exhaustive resources for the learners.
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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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 découle