The Development of Virtual Educational Environments to Support Inter-School Collaboration.
Bibliographic record
Abstract
The introduction of inter-school electronic networks has added a new dimension to education in Canada that has many implications for students who attend schools in rural communities. Collaborative internet-based teaching and learning and the creation of virtual classes within regional intranets now complement traditional on-site instruction in many schools that are located beyond major centres of population. Five stages in the advent of virtual educational environments can be identified starting with the introduction of computers in schools as a foundation for the development of collaborative teaching and learning environments. Inter-school collaboration in rural Canada and the extension of curriculum options for senior students has, in turn, provided a basis for the integration of virtual classes and traditional face to face instruction. Instruction in classes that are electronically-linked to other classes requires different skills from traditional face to face teaching and the development of new strategies and protocols. The implications of open and flexible teaching and learning for the future organization of classes, the preparation of teachers, articulation with higher education and in particular, regional economic development are now significant educational policy issues. The linking of virtual and face to face classes through cybercells is a possible next step in the development of virtual educational environments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".