The Impact of Scaffolding Techniques on Saudi English-Language Learners’ Speaking Abilities
Bibliographic record
Abstract
This study investigated the extent to which scaffolding techniques improve Saudi English-language students’ speaking abilities. The study’s main aims involved determining why most Saudi students do not want to participate in communication tasks and activities and identifying other ways to encourage teachers and students to be more active during speaking classes. A mixed-methods technique, a special rubric, and an attitude questionnaire to collect this study’s data were used. The participants included 50 students from Level 3 in the Department of English Language and Literature at the College of Languages and Translation at Al-Imam Mohammed Bin Saud Islamic University. The experiment lasted for 7 weeks. A teacher met with each group for 2 hours per week. The participants were divided into two groups and experimental and a control group of 25 students each. The experimental group used various scaffolding techniques in each session—which allowed the learners to use their existing knowledge, skills, and strategies in several contexts and for many purposes when speaking. The control group received standard speaking instruction, in which the teacher gave the students time to speak freely without intervention. An independent-sample t test for was used of the analysis. The posttest results showed that the experimental group’s speaking ability improved after the pretest. Moreover, the posttests’ overall results indicated that the experimental group outperformed the control group. This result emphasized the usefulness of using new techniques to teach speaking to nonnative speakers.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".