Investigation and Research on the Implementation Effect of Flipped Classroom With S Province as a Case
Bibliographic record
Abstract
At present, the research on the connotation extension, advantages and mode design of the newly emerging reverse teaching mode of the flip classroom is rich and thorough, but the research on its implementation effect is almost vacant. In view of this, taking 44 schools in S province that are conducting exploratory experiments of flipped classroom as the survey object, By means of questionnaire survey, this paper investigated and studied the learning effects of students in the three stage of pre-class learning, in-class activities and after-class consolidation of flipped classroom. By means of classroom observation and in-depth interview, this paper investigated and studied teachers’ burnout, teaching and research ability and teaching philosophy in flipped classroom, so as to observe the effectiveness of flipped classroom as a foreign product in the context of Chinese education from these two aspects. The survey results show that the implementation of flipped classroom has a positive impact on 13 secondary indicators of students in three stages to varying degrees, and can reduce teachers’ sense of burnout, improve teachers’ teaching and research ability, and improve teachers’ teaching philosophy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".