Instructional Efficacy of Portfolio for Assessing Iranian EFL Learners’ Speaking Ability
Bibliographic record
Abstract
<p>Regarding the fundamental role of speaking in language skills, this study intended to investigate the effects of speaking portfolio as an alternative form of assessment for assessing Iranian EFL learners’ speaking ability at the intermediate and advanced proficiency levels and also its impact on their attitudes. Accordingly, from the population of 72 students studying at Kowsar Language Institute in Esfahan, a sample of 64 male and female intermediate and advanced students were randomly selected based on their scores on an OPT test and they were assigned to 4 groups: intermediate and advanced experimental groups and intermediate and advanced control groups. In order to collect the data, a pretest and a posttest as well as a questionnaire were employed. To analyze the data, an ANOVA and a series of Chi-square were run in the study and the findings indicated that the experimental groups using speaking portfolios performed better than the control groups in terms of speaking ability. Moreover, the result shed light on the advantages of speaking portfolios such as self-assessment, peer-feedback, and improvement of speaking skill. This study provides instructors, administrators, and test developers with alternative ways to improve and assess speaking skill through speaking portfolios.</p>
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".