The Effect of Portfolio Assessment Technique on Writing Performance of EFL Learners
Bibliographic record
Abstract
Nowadays, writing has received a great degree of attention not only because it plays a significant role in transforming knowledge and learning but also in fostering creativity and when acquiring of a special language skill is seen as important, its assessment becomes important as well and writing is no exception. This study intended to investigate the effect of portfolio assessment technique as a teaching, learning and assessment tool on writing performance of EFL learners. Writing sub-skills has also been taken into account. To this end, forty Iranian EFL learners who were all English teaching majors were randomly divided into two groups: experimental (n=20) and control (n=20). The experimental group received the treatment i.e. portfolio assessment while the control group underwent the traditional approach of writing assessment. The result of statistical analysis indicated that the students in experimental group outperformed the students in control group in their writing performance and its sub-skills of focus, elaboration, organization, conventions and vocabulary. The findings suggest that portfolio assessment technique improves writing ability of the students. The results have also some implications for assessment, teaching and learning of L2 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".