Treating of Content-Based Instruction to Teach Writing Viewed from EFL Learners’ Creativity
Bibliographic record
Abstract
The objectives of the research are to examine: (1) whether Content-Based Instruction is more effective than Problem-based learning to teach writing to the EFL Learners; (2) whether the EFL Learners having high creativity have better writing than those having low creativity; and (3) whether there is an interaction between teaching methods and EFL Learners’ creativity in teaching writing.The research method of this research was quasi-experimental research. The techniques of collecting data were creativity test and writing test given to the both classes. The data were analyzed by using Multifactor Analysis of Variance (ANOVA) test of 2 × 2 and Tukey test.The result of data analysis showed that: (1) Content-Based Instruction was more effective than Problem-based learning to teach writing (2) the EFL Learners having high creativity have better writing than the EFL Learners having low creativity and (3) there was an interaction between teaching methods and EFL Learners’ creativity in teaching writing. Based on the finding, it can be concluded that Content-Based Instruction was an effective method to teach writing.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".