The Investigation on College Students' English Writing Achievements and Perceptions towards a Flipped Instructional Model
Bibliographic record
Abstract
Many educators are seeking new methods to motivate and encourage student learning, and the flipped classroom model is increasingly being used by language teachers to engage students. This approach involves students watching instructional videos prior to class, then participating in practical activities during class time. This study aimed to evaluate the effectiveness of the flipped instructional model on the writing achievements, attitudes, and stress levels of first-year college students. The study was conducted in an intact writing classroom from 2020 to 2021 at a central university in Taiwan, with the researcher serving as the instructor. Students were given the option of an 8-week traditional classroom or an 8-week flipped classroom. The study used quantitative research methods to assess students' writing skills and attitudes towards the flipped classroom model, including an EFL writing test and a questionnaire. The study found that the flipped classroom methodology improved students' writing motivation and quality, with students being more comfortable in this type of environment and engaging in more group discussions about video content. Additionally, the flipped model led to significant improvements in students' writing abilities on pretest and posttest assessments. Both quantitative and qualitative data support the positive impact of the flipped classroom model on students' writing performance and attitudes. This research study offers significant insights into the utilization of the flipped instruction model, which can assist educators in enhancing the efficiency of their teaching approaches, particularly in improving students' writing abilities and fostering self-directed 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".