Integrating Divergent Assessment (DA) and Task Based Language Teaching (TBLT) in Argumentative Essay Writing Classrooms: An Experimental Study to Develop Iraqi EFL Learners’ Writing Skills
Bibliographic record
Abstract
The researchers delved into the practice of divergent assessment (DA) and participation in assessment while emphasizing task-based language learning (TBLT) as instruction in argumentative essay courses in an Iraqi EFL context. In doing so, they formed 3 classes, namely a traditional class, a DA exercise with a traditional class, and a TBLT exercise with a traditional class, to investigate the differences that such practices can induce in the writing quality of EFL learners. performance Post-test analysis and one-way ANOVA show that the CONV TBLT class (M = 3.97) significantly outperformed the CONV DA group (M = 2.98) in terms of better argumentative writing. This study shows how EFL learners respond to the integration of DA and TBLT with compliant writing instruction. The researchers used two parallel argumentative writing tasks before and after the DA and TBLT treatment. The results of the study rejected the researchers' null hypotheses, showing that both DA and TBLT were reliably effective in increasing Iraqi EFL students' argumentative essay excellence, with TBLT having a slightly higher neck than DA. The final result of the study clearly shows that the CONV TBLT group showed a significantly higher mean score than the CONV DA and CONV groups. The CONV DA group showed a significantly higher mean score than the CONV group.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".