The Challenge of Designing Blended Courses: From Structured Design to Creative Faculty Support! | Les beaux défis du design de cours hybrides : du design structuré à l’accompagnement créatif !
Bibliographic record
Abstract
This case study deals with the implementation of an e-learning program in a business school in Canada. Cabot Business School decided to offer the program in a blended format so as to increase the flexibility of the program for clientele enrolled in the undergraduate certificate program. A pilot was initiated in 2009 starting with four hybrid courses. Now, three years later, 35 courses are being offered in blended mode by lecturers and a handful of professors who, for the most part, had no previous experience teaching online. Given the rapid development of this program, this case deals with how the instructional designer, without the benefit of any additional resources, managed to juggle both the development of the certificate program as well as parallel projects. The issues encountered deal with the extent to which the instructional designer can support faculty who are converting their courses from in-class to online, one of the main design challenges encountered by faculty. This case describes training strategies and implemented solutions provided by the instructional designer as well as the results obtained, faculty perceptions, and food for thought on the possible evolution of the role of the instructional designer.
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".