Bibliographic record
Abstract
As Canadian universities work to increase access to graduate education, many are adopting blended modes of delivery for courses and programs. Within this changing landscape of higher education, The Finest Blend answers the call for rigorous research into these methods to ensure quality learning and teaching experience and presents case studies of French and English universities across Canada that are experimenting with blended learning models in graduate programs. \n \nDrawing on various research methods, the contributors to the volume investigate the sustainability of blended learning, shifts in pedagogical practices, and the role of instructional designers. They share key practices for both graduate students and instructors and emphasize the importance of institutional and departmental support for both students and faculty transitioning to blended delivery modes. Touching on theory, design, delivery, facilitation, administration, and evaluation, this book provides a comprehensive overview of current practices and opportunities for blended learning success. \n \nWith contributions by Alicia Adlington, Shaily Bhola, Denise Carew, Jane Costello, Daph Crane, Jane Hanson, Michael Fairbrother, Wendy Kraglund-Gauthier, Shehzad Ghani, Michele Jacobsen, Carol Johnson, Sawsen Lakhal, Yang (Flora) Liu, Dorothea Nelson, Pam Phillips, Marlon Simmons, Kathy Snow, Maurice Taylor, and Jay Wilson.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.315 | 0.000 |
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 teacher head, 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".