English Language Teacher’s Multimedia Knowledge in Teaching Using Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study investigates English as a Foreign Language (EFL) teacher’s multimedia knowledge and practices in remote teaching during the COVID-19 pandemic. Firstly, it focuses on determining whether some teachers' demographics (gender, nationality, academic qualification, type of institution, perceptions) correspond to teachers' multimedia knowledge. Secondly, it reveals the teacher’s practices in implementing multimedia in EFL classrooms. This present study surveyed 120 participants (Male=33 and Female=87) from Indonesia (N=108) and outside Indonesia (N=12). They answered a questionnaire to identify their demographic information and took a literacy test to examine their multimedia knowledge. The findings indicate that of the demographics, only gender correlates significantly to teacher’s multimedia knowledge. It was also found that teachers have worked hard to bridge the gaps of remote teaching by implementing multimedia regardless of the barriers they have met. It implies that multimedia should be applied not only in remote teaching but also in face-to-face and blended learning due to the power of multimedia in language learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it