The Role of Traditional and Virtual Scaffolding in Developing Speaking Ability of Iranian EFL Learners
Bibliographic record
Abstract
The present study attempted to investigate the effect of the four scaffolding techniques, namely Hard, Soft (Saye & Brush, 2002), Reciprocal (Holton & Clarke, 2006), and Virtual (Yelland & Masters, 2007), on the speaking ability of the Iranian EFL language learners and their fluency, lexicon, grammar and pronunciation. To this end, the four scaffolding techniques were classified into the two groups of Traditional (Hard, Soft and Reciprocal) and technology-mediated (Virtual). 120 Maritime students at Kharg Azad University (IAU-Kharg) were selected as participants based on convenience sampling. At the onset, an Oxford Placement Test was given to the students to place them in the same proficiency level, Intermediate. 10 students were found as outliers who remained as intact members of the groups throughout the study. Eventually, the 110 homogeneous students were randomly assigned to the four scaffolding groups. A pretest of speaking ability was run to the students prior to the scaffolding treatments lasting for 8 weeks (16 sessions). After the treatments, the students completed a posttest of speaking. Having analyzed the data through SPSS software, it was found that under the influence of the four scaffoldings, not only did the Iranian EFL students outperformed in the posttest of speaking, but they also showed a significant improvement in their fluency, grammar, lexicon, and pronunciation. Thus, the findings of this current study extended earlier understandings of scaffolding in an EFL environment and will contribute to the advancement of future courses in terms of their scaffolding pedagogical aspects.
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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 | 0.005 |
| 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 | 0.000 |
| 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".