e-Learning in K-12 classes in school district digital intranets. Paper presented at the Virtual Educa
Bibliographic record
Abstract
As rural communities and schools decline in size educational policy makers often question their viability. In the Canadian province of Newfoundland and Labrador and in other areas of the world, new educational structures based on digital networking, using the Internet, are being developed for the delivery of education to rural schools (Stevens, 1999). In Newfoundland and Labrador school district No. 8, (The Vista School District), there are 5165 students enrolled in 18 schools. The schools range in size from 650 students down to 40 students. In eight schools in this district there were senior students who wished to study “Advanced Placement ” (AP) courses in four subjects: Biology, Chemistry, Mathematics and Physics. (Advanced Placement courses enable students in their final year of High School throughout North America to begin undergraduate degrees if these courses are passed at grade levels specified by the university of their choice). The challenge that this presented for the eight small rural schools in the Vista School District was to provide University-level instruction for small numbers of students. Teaching and Learning in a School-District Digital Intranet Although AP courses are a well-established feature of senior secondary education in the United States and Canada, it is unusual for students to be able to enroll for instruction at this level in
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".