A novel integration of online and flipped classroom instructional models in public health higher education
Bibliographic record
Abstract
BACKGROUND: In 2013, a cohort of public health students participated in a 'flipped' Environmental and Occupational Health course. Content for the course was delivered through NextGenU.org and active learning activities were carried out during in-class time. This paper reports on the design, implementation, and evaluation of this novel approach. METHODS: Using mixed-methods, we examined learning experiences and perceptions of the flipped classroom model and assessed changes in students' self-perceived knowledge after participation in the course. We used pre- and post-course surveys to measure changes in self-perceived knowledge. The post-course survey also included items regarding learning experiences and perceptions of the flipped classroom model. We also compared standard course review and examination scores for the 2013 NextGenU/Flipped Classroom students to previous years when the course was taught with a lecture-based model. We conducted a focus group session to gain more in-depth understanding of student learning experiences and perceptions. RESULTS: Students reported an increase in knowledge and survey and focus group data revealed positive learning experiences and perceptions of the flipped classroom model. Mean examination scores for the 2013 NextGenU/Flipped classroom students were 88.8% compared to 86.4% for traditional students (2011). On a scale of 1-5 (1 = lowest rank, 5 = highest rank), the mean overall rating for the 2013 NextGenU/Flipped classroom students was 4.7/5 compared to prior years' overall ratings of 3.7 (2012), 4.3 (2011), 4.1 (2010), and 3.9 (2009). Two key themes emerged from the focus group data: 1) factors influencing positive learning experience (e.g., interactions with students and instructor); and 2) changes in attitudes towards environmental and occupation health (e.g., deepened interest in the field). CONCLUSION: Our results show that integration of the flipped classroom model with online NextGenU courses can be an effective innovation in public health higher education: students achieved similar examination scores, but NextGenU/Flipped classroom students rated their course experience more highly and reported positive learning experiences and an increase in self-perceived knowledge. These results are promising and suggest that this approach warrants further consideration and research.
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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 | 0.003 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".