Enhancing Language Skills and Literacy: A Project-Based Approach for English Teachers in Multimodal Pedagogy
Bibliographic record
Abstract
To address the gap in English teachers’ ability to effectively integrate multimodal literacy in language teaching, especially in socio-culturally diverse and resource-limited contexts such as East Java, this study developed and implemented a project-based mentoring program. The aim was to enhance teachers’ competencies in creating and utilizing multimodal texts to improve students’ language skills and digital literacy. Grounded in a Research and Development (R&D) approach, the study began with a needs analysis involving 147 English teachers across various school types and regions in East Java. Findings revealed that while over 80% of teachers already employed multiple modes such as text, image, audio, and gesture in classroom practice, formal training on multimodal literacy was limited. The project-based mentoring program incorporated a sequence of stages, including conceptual workshops, exploration of digital tools, mentoring on lesson planning, classroom trials, and reflective sessions. Participants demonstrated improved confidence and capability in integrating multimodal elements, even when constrained by limited digital access. Teachers reported increased student engagement and participation, particularly when combining familiar digital applications with contextually relevant teaching materials. This study highlights the importance of capacity-building initiatives that support teachers in leveraging both digital and non-digital multimodal resources, contributing to more inclusive and effective language learning environments aligned with the evolving demands of 21st-century education.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".