Panel description: Computer-supported collaborative learning and ODL for informal groups
Bibliographic record
Abstract
The Lifelong Learning for Farmers (L3 Farmers) Project of COL in Tamil Nadu, India, focuses on enhancing the self-directed personal strategic learning as an important strategy in ODL for informal groups. ICT plays a key role. The project has emphasized the importance of mobilization phase, manifesting the principle of participation of various stakeholders through organized and systematic actions. Mobilization involves mutual conscientization in which various stakeholders understand each other’s agenda. Collaboration assumes importance and L3 Farmers shows that Computer-Supported Collaborative Learning is a key factor in the ODL for Informal Groups. Collaboration, which is a coordinated, synchronous activity that is the result of a continued attempt to construct and maintain a shared conception of a problem, is the characteristic of a learning community. Other major social and structural differences impede collaboration and diffuse the learning community. Hence development programmes have to lay major emphasis on the process of mobilization. Computers can enhance collaboration through four types of interactions: - at the computers - around computers - related to computer applications, and - through computers. This study analyses the role of synchronous and asynchronous computer-mediated collaborations in five villages. The study throws new light on the theoretical perspectives of ODL for informal groups and offers a roadmap for building alternative strategies for reaching the unreached. Panel Members: Professor Surabhi Banerjee, Vice-Chancellor, Netaji Subhas Open University (NSOU), India Mr. Peter Fenrich, Project Leader/Instructional Multimedia Designer, British Columbia Institute of Technology (BCIT), Canada Dr. Terrence Philips, Fisheries Management and Development Manager, Caribbean Regional Fisheries Mechanism (CRFM), Guyana Mr. John Dada, Executive Director, FANTSUAM Foundation, Nigeria Ms Jennipher Kere, Managing Trustee, Women In Fishing Industry Programme (WIFIP), Kenya Dr. Collins Osei, Crops Research Institute, Ghana
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.171 | 0.052 |
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".