Looking Back and Looking Forward
Bibliographic record
Abstract
Using an engagement in research approach this article explores the landscape of blended learning in higher education over the last decade by comparing the results of a critical literature review by Vaughan to an instrumental case study that identified key factors that led to the implementation of a blended learning initiative in one medium sized Canadian university. Findings indicate that although students still prefers the time flexibility of blended learning, there are major differences between undergraduate and graduate students and their motivation for choosing this pedagogy. Professors also find increased teacher-student interactions using a blended learning format but acknowledge more support for course redesign and better professional development and training. From an administrator's viewpoint, one of the main challenges occurred at the individual faculty level in trying to communicate the definition of blended learning to professors. As a way of looking forward, interviews with experts from various Ontario universities and a survey of university personnel from across the country provided some initial insights. A discussion situates the findings using the theoretical lens of andragogy, self-directed learning, the community of inquiry framework, and points to a possible range of additional research questions for blended learning.
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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".