Designing Morphosyntax Material for EFL Students: Indonesian Case
Bibliographic record
Abstract
Students’ understanding about Morphosyntax is often low due to its complex processes. This study attempted to design a learning handout that would help students solve their writing problems particularly in Morphosyntax cases. This qualitative study was conducted at two Indonesian Universities, namely UNTAG Banyuwangi and IAIN Jember Indonesia. Six phases in Design Based Research (DBR) by Hoadley (2004) and four phases of DBR by Reeves (2012) were combined as the design of the research. Four classes with a total of 114 students participated and the lecturers of writing course and grammar course were involved. Following the mixed design of DBR by Hoadley and Reeves, this study produced a learning handout that, after tested at the second cycle, proved helpful to solve students’ Morphosyntax problems especially in Writing Class. The students facing writing problems decreased averagely 35-40% after they learnt writing using the produced learning handout. In this case, the needs to master more on Morphosyntax to support their English proficiency skills especially in writing has been solved.
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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 | 0.004 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".