Flipped classrooms and student learning: not just surface gains
Bibliographic record
Abstract
The flipped classroom is a relatively new approach to undergraduate teaching in science. This approach repurposes class time to focus on application and discussion; the acquisition of basic concepts and principles is done on the students' own time before class. While current flipped classroom research has focused on student preferences and comparative learning outcomes, there remains a lack of understanding regarding its impact on students' approaches to learning. Focusing on a new flipped classroom-based course for basic medical sciences students, the purpose of the present study was to evaluate students' adjustments to the flipped classroom, their time on task compared with traditional lectures, and their deep and active learning strategies. Students in this course worked through interactive online learning modules before in-class sessions. Class time focused on knowledge application of online learning module content through active learning methods. Students completed surveys and optional prequiz questions throughout the term to provide data regarding their learning approaches. Our results showed that the majority of students completed their prework in one sitting just before class. Students reported performing less multitasking behavior in the flipped classroom compared with lecture-based courses. Students valued opportunities for peer-peer and peer-instructor interactions and also valued having multiple modes of assessment. Overall, this work suggests that there is the potential for greater educational gains from the flipped classroom than the modest improvements in grades previously demonstrated in the literature; in this implementation of the flipped classroom, students reported that they developed independent learning strategies, spent more time on task, and engaged in deep and active learning.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.008 | 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".