Mixed-Mode Learning: Adoption of Cognitive, Social, and Teaching Presence Elements on Clinical Rotations
Bibliographic record
Abstract
This study aimed to evaluate the impact of mixed-mode (hybrid and blended) learning on the inquiry process for veterinary students on clinical rotations. An exploratory sequential mixed methods design combining qualitative (focus group) and quantitative (questionnaire) data gathering was used. Deductive qualitative analysis was performed to evaluate ideas confirming the community of inquiry process as indicated by teaching, social, and cognitive presence. Inductive analysis was performed to evaluate ideas that did not fall under the community of inquiry presence. Likert scores and the proportions of different responses from the questionnaire were summarized. Seven students participated in the focus groups, whereas 60 completed the questionnaire. Thirty-one and 49 faculty members participated in the focus groups and completed the questionnaire, respectively. The components of community inquiry were present in the mixed-mode learning approach for students on clinical rotations. Emergent ideas that did not fit under cognitive, teaching, and social presences but directly or indirectly affected the inquiry process in mixed-mode learning included co-participation by students, flexibility for faculty, faculty well-being, and technical, administrative, and peer faculty support. Barriers to effective mixed-mode design of learning activities include a lack of training of clinical faculty, design misalignment between learning activities and rotation learning outcomes, and assessment of students. Implementing a well-designed institutional continuous education training program for clinical faculty on approaches to mixed-mode learning activities in clinical rotations, followed by an assessment of the training program's access, adoption, and quality, is required.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".