Effective blended learning for post-secondary learners: instructor perspectives
Bibliographic record
Abstract
This qualitative study examined instructors’ perceptions of teaching practices and their experiences teaching blended learning at the University of Manitoba. Using in-depth interviews,this study (a) explored instructors’ teaching practices and their experiences teaching using blended learning in higher education, (b) examined the extent to which elements of the community of inquiry framework (designed along social constructivist learning principles) were incorporated into instructors’ approaches, and (c) examined which learning theories influenced the teaching of blended learning courses at the university of Manitoba as well as their impact on effective instruction and learning in higher education contexts. The study revealed that instructors found convenience, accessibility, and cognitive flexibility to be some of the main benefits of blended learning for learners. Instructors adopted the underlying principles of social constructivism. In their teaching, they focused on several issues, including their complex role as instructors. This role included enhancing the learning experience through the use of the online component of the course, understanding the learner and appreciating their experience, being present, and creating a collaborative and engaging learning environment. The instructors expressed the need for institutional and technological support, as well as professional development. Suggestions for university instructors included pre-planning, considering learners and their experiences, creativity, flexibility and perseverance, and attending training sessions/workshops. Students were advised to put more effort into being open and self-directed,investing in their learning experience, and adopting a positive attitude.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".