Flipping your classroom: Is now the time? Lessons learned from a 2nd year research methods course
Bibliographic record
Abstract
A flipped classroom reverses traditional learning spaces such that foundational knowledge is acquired by students independently through recorded lectures and/or readings in advance of the lecture period and knowledge is consolidated through active learning activities in the classroom. A flipped classroom learning environment can promote critical skill development and knowledge application, and therefore, could enhance scientific literacy (SL) skill development, which is critical in the life sciences. SL is an individual’s ability to utilize and apply scientific knowledge in real-world settings. We recently published a study examining the impact of a flipped classroom on SL skill acquisition and retention in a second-year research methods course for kinesiology students. Specifically, we used the Test of Scientific Literacy Skills to assess students’ ability to evaluate the validity of sources, understand elements of research design, create and interpret graphical information, among others.We found that SL skills increased significantly during the flipped classroom semester and were positively correlated with students’ final grade. Interestingly, SL skill retention decreased after the summer break, however, the retention of SL skills was positively correlated to learning approach, with those using a deep approach retaining SL capabilities. In this session, we will share the results of our study and provide tips and tricks for implementing a flipped classroom based on the experiences of the instructor and students. Participants will be provided with some best practices for implementing a flipped classroom approach, regardless of subject matter. This study was approved by the University of Guelph Research Ethics Board (REB#22-07-001). Learning Outcomes: 1. Participants will understand the benefits and limitations of a flipped classroom approach. 2. Participants will leave with some strategies for implementing a flipped classroom that are applicable to any subject/discipline.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".