Active Learning and Flipped Classroom, Hand in Hand Approach to Improve Students Learning in Human Anatomy and Physiology
Bibliographic record
Abstract
Because Human Anatomy and Physiology (A&P), a gateway course for allied health majors, has high dropout rates nationally, it is challenging to find a successful pedagogical intervention. Reports on the effect of integration of flipped classrooms and whether it improves learning are contradictory for different disciplines. Thus many educators are reluctant to explore the value of flipped classrooms. Therefore, in the present study we compare incorporating flipped classroom and minimal class discussion (control group) with flipped classroom and active learning activities (experimental group) in A&P and their impacts on both students’ exam performance and their satisfaction with the course. Assessments consisted of a survey of students’ attitudes and a comparison of exam performance in experimental and control groups. Exam performance among the students in flipped-classroom and active learning activities improved significantly relative to the control group [Mean ± SD: (76.93±18.33 vs 67.8±18.81), p<0.001. Student attitude, in which students rated the efficiency of pedagogical learning on a five-point Likert scale, was positive: the majority of students strongly preferred active-learning activities that were incorporated in the flipped-classroom. Students indicated that these activities helped them learn better and to connect the materials to the goals of their future careers (73.88% and 79.77% respectively). Therefore, we conclude that flipped classroom coupled with active learning strategies can improve students’ performance and attitude in the introductory A&P course.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".