STUDENTS' PERCEPTIONS OF THE USE OF DIGITAL MEDIA IN ENGLISH LANGUAGE LEARNING
Bibliographic record
Abstract
This study examines students' perceptions of the use of digital media in English language learning at Teacher Training and Education College of Insan Madani. Using a qualitative research approach, data were collected through semi-structured interviews with students enrolled in English language courses. The findings indicate that students perceive digital media as an effective and flexible tool that enhances their language acquisition. Platforms such as YouTube, Duolingo, Zoom, Google Classroom, and WhatsApp were particularly valued for their ability to provide authentic English exposure, interactive learning experiences, and opportunities for autonomous learning. However, despite the benefits, students also identified several challenges, including limited internet access, digital literacy gaps, and distractions from non-educational content. The study suggests that a blended learning approach, combining digital media with structured classroom instruction, could help mitigate these challenges and maximize the benefits of technology in English language learning. Additionally, institutions should focus on enhancing digital literacy training and improving internet accessibility to support students in effectively utilizing digital tools. This research contributes to the ongoing discourse on technology-enhanced language learning, particularly within the context of Islamic teacher training institutions, and provides insights for future studies on optimizing digital media use in English 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".