The Effects of Using Genre-Based Approach via Miro Platform on English Composition Writing Skills
Bibliographic record
Abstract
This study aimed to develop characteristics of the Genre-Based approach (GBA) via the Miro platform and to discover results on using this approach and the Gathering, Processing, Applying, and Self-Regulating (GPAS) process. The study employed experimental research using a true group pretest-posttest control group design with Grade 8 students divided into an experimental and a control group of 38 students per group by using cluster random sampling. The instruments were the lesson plans and the pre-and post-tests on English composition writing skills. The descriptive data analysis used to assess the results of the students’ English composition writing skills were mean, standard deviation, and Repeated Measures MANOVA. The results from the study revealed that the researcher could develop a characteristic of GBA via the Miro platform for teaching because the statistical analysis results indicated that the experts approved the implementation of the lesson plans in this study using 5 levels of Likert Scale (mean = 4.72). Students who received teaching through the GBA via the Miro platform had significantly higher English composition writing skills than those taught through the GPAS process at the significant level of .05. These results confirm that using GBA via the Miro platform improved the students’ composition writing skills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".