Influence of Self-Confidence on Saudi Primary Students' English Vocabulary Ability Through Differentiated Instruction Strategies
Bibliographic record
Abstract
This study investigated the influence of self-confidence on Saudi primary students' English vocabulary ability through differentiated instruction strategies. The study took place in Al Jouf province during the 2023-2024 school year, with data collected through a survey questionnaire. The sample consisted of 60 primary school students. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the data. The results revealed a significant relationship between self-confidence, differentiated instruction strategies, and English vocabulary ability. Specifically, differentiated instruction strategies were found to mediate the relationship between self-confidence and vocabulary ability. Higher self-confidence levels encouraged students to adopt these strategies, thereby enhancing their vocabulary skills. Based on these findings, the study recommends that teachers implement differentiated instruction strategies, such as flexible grouping, collaborative learning, and brainstorming, to increase student engagement and interest in learning. Furthermore, it emphasizes the importance of fostering self-confidence in students, in alignment with self-efficacy theory, to support their vocabulary learning and academic growth.
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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 | 0.004 |
| 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 | 0.001 |
| 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".