Flipping for success: evaluating the effectiveness of a novel teaching approach in a graduate level setting
Bibliographic record
Abstract
BACKGROUND: Flipped Classroom is a model that's quickly gaining recognition as a novel teaching approach among health science curricula. The purpose of this study was four-fold and aimed to compare Flipped Classroom effectiveness ratings with: 1) student socio-demographic characteristics, 2) student final grades, 3) student overall course satisfaction, and 4) course pre-Flipped Classroom effectiveness ratings. METHODS: The participants in the study consisted of 67 Masters-level graduate students in an introductory epidemiology class. Data was collected from students who completed surveys during three time points (beginning, middle and end) in each term. The Flipped Classroom was employed for the academic year 2012-2013 (two terms) using both pre-class activities and in-class activities. RESULTS: Among the 67 Masters-level graduate students, 80% found the Flipped Classroom model to be either somewhat effective or very effective (M = 4.1/5.0). International students rated the Flipped Classroom to be significantly more effective when compared to North American students (X(2) = 11.35, p < 0.05). Students' perceived effectiveness of the Flipped Classroom had no significant association to their academic performance in the course as measured by their final grades (r s = 0.70). However, students who found the Flipped Classroom to be effective were also more likely to be satisfied with their course experience. Additionally, it was found that the SEEQ variable scores for students enrolled in the Flipped Classroom were significantly higher than the ones for students enrolled prior to the implementation of the Flipped Classroom (p = 0.003). CONCLUSIONS: Overall, the format of the Flipped Classroom provided more opportunities for students to engage in critical thinking, independently facilitate their own learning, and more effectively interact with and learn from their peers. Additionally, the instructor was given more flexibility to cover a wider range and depth of material, provide in-class applied learning opportunities based on problem-solving activities and offer timely feedback/guidance to students. Yet in our study, this teaching style had its fair share of challenges, which were largely dependent on the use and management of technology. Despite these challenges, the Flipped Classroom proved to be a novel and effective teaching approach at the graduate level setting.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".