Bibliographic record
Abstract
Educators are frequently looking for new ways to expand distance education opportunities to students in rural and remote locations. Videoconferencing is rapidly growing as a premier tool to minimize distance barriers and increase opportunities for continuing education. For more than a decade the Faculty of Education at the University of Lethbridge has offered blended learning courses through a cohort implementation strategy. While creating a cohort of students in one location facilitates the face-to-face component of the blended learning environment by allowing instructors to conduct classes occasionally during the semester by traveling to the remote location, the cost of travel for face-to-face visits to many rural and remote school districts, such as Peace River, adds another barrier to establishing life long learning opportunities. In an effort to increase access throughout the province to graduate level programming the Faculty of Education is investigating the use ofvideoconferencing to replace some of the face-to-face site visits. In January 2005, the Faculty of Education at the University of Lethbridge enrolled a cohort of students from the Peace River School District in the University's ftrst graduate level videoconferencing blended learning environment. These instructors, administrators, and students were the participants in this multi-methodological study to evaluate student and instructor perceptions of using videoconferencing in a blended learning environment, and establish best practices for future course offerings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.025 | 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".