A Needs Analysis for Developing a Blended Learning–Driven Communicative Language Teaching Instructional Model to Enhance Primary Students’ English Communication Skills
Bibliographic record
Abstract
This study conducted a needs analysis to inform the development of a blended learning-driven communicative language teaching model aimed at enhancing English communication skills among primary school students. The research sought to examine current conditions, identify challenges, and determine key factors essential for designing an effective blended learning framework tailored to primary English education. The study employed a mixed methods research approach, integrating both quantitative and qualitative data collection and analysis to provide a comprehensive understanding of the educational context. A total of 250 primary English language teachers were purposively sampled to participate, alongside in-depth interviews with five experienced teachers and eight students. Data collection included a structured questionnaire and semi-structured interviews. Quantitative analysis showed that teachers perceived significant challenges across instructional domains, with the highest emphasis placed on learning materials and resources (M = 4.17, SD = 0.75), while blended learning was rated lower but still within a high range (M = 3.86, SD = 0.84), reflecting its underdeveloped status in practice. Qualitative thematic analysis revealed that, despite positive attitudes toward blended learning, teachers lacked adequate training to implement it effectively, while students favored interactive, technology-enhanced activities that support speaking and vocabulary development. Both groups emphasized the importance of authentic materials, engaging digital tools, and active learning strategies. These insights will guide the creation of a contextually relevant blended learning model designed to enhance communicative competence in English at the primary education level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".