Teaching graduate health policy via technology: A pilot study of engaged learning, social presence, and blended learning
Bibliographic record
Abstract
Purpose: This pilot study examined the relationship between engaged learning, social presence, and blended learning in a graduate nursing health policy course. The aims of the study were to: 1) determine the relationship between engaged learning, social presence and student satisfaction, and 2) investigate students’ perceived learning with online discussions and seminar blogs. Results: Twenty-one participants completed adapted versions of the Social Presence and Satisfaction Scales. Overall there was a strong relationship between engaged learning, social presence, and student satisfaction. Conclusions: Combining face-to-face classroom discussion with academically relevant assignments that engaged students in the health policy course was associated with an overall sense of satisfaction. The majority of participants reported that online discussions/blogs provided an opportunity to learn the “value of other points of view.” Respondents also reported “greater collaboration working with colleagues with-in the blended model.” Seventy-one percent of respondents reported they were “stimulated to do additional reading or research on topics discussed in the online portion of the class.” The majority of respondents stated they “felt actively engaged with the course content working with-in the blended model.” Instructor presence in the online component of the course was important for creating a sense of online community and student engagement.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".