Conditions in Which a Flipped Classroom can Successfully be Implemented: A Traditional Literature Review
Bibliographic record
Abstract
We live in a world where one size does not fit all. Educational settings and learning environments continue to change and educators need to adapt to these changes. A flipped classroom is one approach that can help support educators to adapt to these educational changes. The purpose of a flipped classroom is to maximize face-to-face time with teachers and students in class, so that when students are in the classroom, teachers and students spend their time applying higher-level thinking, learning, and application of knowledge. Through an extensive review of the literature, qualitative data was collected from peer-reviewed articles gathering patterns. Twenty-six articles from different countries and educational settings, such as law classrooms to nursing education, as well as language learning, international students' experiences, and inclusive education settings, supported the data collection. The patterns identified, revealed five evidence-based strategies necessary to the success of implementing a flipped classroom. The literature also reveals evidence-based interventions/strategies that do not contribute to the success of implementing a flipped classroom. Practical strategies are shared to support the implementation. More importantly, the strategies suggested will also benefit international student populations for utilizing the flipped-classroom method. Sharing these evidence-based strategies found in the literature will offer insights into the successful implementation of flipped classrooms for all levels of education, and would also benefit international students.
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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 | 0.018 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".