The Impact of the Flipped Classroom Model on Students' Academic Achievement
Bibliographic record
Abstract
The aim of this study is twofold. First, it aims to investigate the impact of the Flipped Classroom (FC) Model on students’ academic achievement. Second, it reveals the students’ opinions about the model itself. For four weeks, the students in the experimental group were taught in a blended learning context where the FC Model was applied, while the lessons in the control group were carried out through traditional blended learning. Both groups were administered a test before and after the Flipped Classroom sessions. To analyze the data, a two-way ANOVA for Mixed Measures was conducted to compare the means of test scores of each group. The results showed that there were no statistically significant differences between the scores of the two groups. Coming to classes prepared and completing the assignments in class, so that students did not need to do assignments at home, were among the positive aspects of the FC Model. The problems encountered in this model, however, are categorized under three main titles: Motivation, Content, and Learning. At the end of the study, the advantages and disadvantages of the FC Model are identified in accordance with the participants’ opinions, and necessary suggestions made.
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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 | 0.008 |
| 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 | 0.001 |
| 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 | 0.001 |
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".