Blended Learning using agMOOCs as a Tool for Professional Development: A Case of Students of Agriculture in India
Bibliographic record
Abstract
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".