The Effect of Using Flipped Classroom with CLZ Platform on Students' English Achievements in China
Bibliographic record
Abstract
English has a very important place as a major subject in our teaching. How to enable students to quickly master English vocabulary and improve the use of English skills is a problem worth thinking about by teachers in China. This study was conducted to develop a lesson plan of flipped classroom with CLZ (as an instructional tool) and to study the effects on English achievements of students. In this case, the total population was 300 and a random sampling was used to select 30 8th grade students from Shuyuan Middle School in Henan Province, China for this study. The research tools used in the study were the flipped classroom lesson plan, CLZ platform and English test papers, which were used to conduct pre-tests and post-tests for these 30 students. This study uses statistics with x, S.D and t-test to arrive at the significance of the change in the students' English achievement. The final results of the study showed that the x1 of the pre-test is 74 and the x2 of the post-test is 84. The pre-test S.D is 16.71, the post-test S.D. is 12.26. Since the S.D of the post-test is smaller than the standard deviation of the pre-test, it can be seen that the students' performance after four weeks of flipped classroom teaching has significantly improved compared to the pre-test, and the value of the achievement gap between the students has been reduced. Moreover, the students' post-test scores were significantly higher than their pre-test scores (P>0.05).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".