Effectiveness of Flipped Learning: Improving Pre-Service Teachers’ Prowess in Producing Videos
Bibliographic record
Abstract
In the current study, the aim is to determine the effectiveness of flipped learning approach in developing pre-service teachers’ skills and knowledge in creating and editing digital videos. Furthermore, the approach was evaluated through the theoretical lens of constructivism and experiential learning. This research was conducted within a workshop course for six weeks period during the summer semester of the 2018-2019 academic year at a teacher training college in Kuwait. In the study, pre-test/post-test quasi-experimental design with control group was applied. The experiment involved applying a flipped learning approach to the experimental group while the courses were carried out using traditional lectures in the control group. A questionnaire was also administered to the experimental group to acquire feedback on the effectiveness of flipped learning activity. Descriptive statistics, Mann Whitney U Test and Wilcoxon Sign Test were used in the analysis of the quantitative data. The results obtained from Mann Whitney U Test and Wilcoxon Sign Test suggests that there is no significant difference between the pre-test and post-test scores of the experimental group and the control group. Descriptive data also demonstrated that the use of the flipped learning method in the curriculum had significantly increased the skill levels and knowledge of the experimental group pre-service teachers. The study recommends that care should be taken when structuring courses in pre-service teachers’ education when applying flipped 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".