The effect of using flipped learning on student achievement and measuring their attitudes towards learning through it during the corona pandemic period
Bibliographic record
Abstract
The COVID-19 pandemic has led to the end of in-person classes at universities and schools and the beginning of digital advancements in higher education. Flipped learning is very different from traditional teaching methods and necessitates some shifts in the roles of the teacher and the student. The purpose of this study is to investigate the effect of using flipped learning on students' achievement and measure their attitudes towards learning through it during the Corona pandemic period. A quasi-experimental study design was adopted through the pretest and posttest measurements. Two groups were randomly assigned one to be experimental, and the other a control. The present study showed that there was no statistically significant difference between the mean of the pre-measurement and post-measurement tests of the experimental group’s motivation towards learning using the flipped learning strategy. The findings from the quantitative data revealed that flipped learning contributed to the academic success of students and their attitudes toward learning during the corona pandemic period. Hence, further studies with more extended periods are recommended to examine the effect of Flipped learning on self-directed learning and other related variables.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".