Students and instructors perspective on blended synchronous learning in a Canadian graduate program
Bibliographic record
Abstract
Abstract Blended synchronous learning (BSL) represents several contexts that enable to bring remote students into the classroom, in real time, by the means of videoconferencing, web conferencing and virtual world. As BSL seems to be more and more implemented in many higher education institutions, especially in the current context of the COVID‐19 pandemic, and given the recent interest and scarce published research in BSL, more studies are needed on this kind of learning. The purpose of this research was to explore students and instructors perspective regarding their experience in BSL, according to three dimensions: pedagogy, technology and organization/logistics. To meet the study objective, a qualitative methodology was adopted. The study participants were remote students ( n = 4) and face‐to‐face students ( n = 4) enrolled in a graduate program in education offering only blended synchronous courses, and instructors ( n = 5) in this program. Semi‐structured interviews were selected as the data collection method. Nine sub‐themes in reference to the three dimensions emerged from the study participants. They have also highlighted some challenges associated with BSL. The results reported in this study should provide faculties and higher education administrators with additional information and guidance, based on empirical data, on the use of BSL if they wish to implement it in academic programs. Moreover, in regard to the challenges revealed by the study participants, the results will permit to surpass the obstacles when implementing BSL successfully.
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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 | 0.002 | 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 | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".