Media-Support Teaching and Learning of English Language as a Second Language: Eliminating Stereotypes
Bibliographic record
Abstract
Second language learners especially in English language need further language support in view of the fact that they operate on the performance level of language use as against competence. Achieving success in the teaching and learning of a second language such as English is determined by a number of linguistic and nonlinguistic factors such as the attitude and language skills of the learners, the teacher’s innovativeness and competence, effective teaching methods and materials such as visual, audio-visual aids and media aids to language learning. This research is motivated by the problem inherent in the traditional teaching methods which is stereotypical, boring with little active students’ engagement in the learning process, which makes knowledge transfer an arduous task. The research represents a shift in language teaching and learning - from the known traditional to a more technological mode of learning- giving way to new technologies in which the media plays a prominent role. The work adopts a qualitative methodology in assessing the role of the media in language teaching and learning both on the part of the students as well the teacher, especially in terms of self-development and innovations. It was discovered that media aids in language learning, facilitates the overall learning process and helps the teacher to transcend his limitations in areas such as pronunciation, vocabulary to be able to guide the students aright. This makes learning an ongoing process rather than a product.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".