The Effect of Video-Based Flipped Classroom Strategy on Learning Outcomes and Students’ Active Participation
Bibliographic record
Abstract
The flipped classroom model has gained significant attention as an innovative pedagogical approach, particularly in enhancing student engagement and learning outcomes. This study investigates the effectiveness of a video-based flipped classroom in improving students' academic performance and active participation during in-person classes. The research employed a quasi-experimental design, with two groups: one experiencing the flipped classroom approach, and the other following traditional lecture-based instruction. Data were collected through pre- and post-tests to assess learning outcomes, as well as an observation rubric to measure student participation. Results indicated that the flipped classroom group showed a 17% improvement in learning outcomes compared to the traditional group. Furthermore, the flipped classroom group exhibited twice the level of active participation, as measured by the rubric. These findings suggest that the flipped classroom model is effective in fostering a more interactive and participatory learning environment, where students engage with content before class and apply their knowledge through discussions and problem-solving activities during in-class sessions. The study also highlights the importance of video-based learning in preparing students for active participation and the role of the teacher as a facilitator in flipped classrooms. Despite the promising results, the study acknowledges several limitations, including reliance on technology and students' readiness for independent learning. The study concludes with recommendations for teacher training and future research to further explore the effectiveness of flipped classrooms across diverse educational contexts.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".