Implementing Blended Learning: Policy Implications for Universities
Bibliographic record
Abstract
The incorporation of new learning technologies into courses at Canadian universities has been largely undertaken at the initiative of individual instructors, rather than in response to explicit institutional direction or faculty initiatives. This appears to be particularly the case with the migration of individual courses that were formally entirely face-to-face to blended delivery. In this case study, the experience of one university is used to present the types of academic policy and process issues that arose during a pilot project to re-design a single graduate program in order to facilitate the use of blended delivery. Considerations included why and how blended learning was to be used; at what level decisions regarding blended delivery should be made; decision process for individual courses versus entire programs; policy precedents and need for policy modification or new policy. Specific areas examined include course and program approval, resources, and instructor responsibilities and workload. The findings suggest that the work involved in policy updating in a changing environment is important because it surfaces, and opens for review, existing, often taken-for-granted institutional values, norms, and protocols. In some cases, the articulation of these values and norms serves to highlight the importance of respecting them within this new learning context. In others it suggests the need to rethink accepted protocols that may be ill-suited to the educational opportunities that emerging technologies can present.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".