Using Original Methods in Teaching English Language to Foreign Students (Chinese) in Indian Classroom
Bibliographic record
Abstract
The article gives information on English language teaching schemes in Indian classrooms for foreign students. The teacher monitors as facilitator and instructor. The trainees were trained in the four macro skills, LSRW. I taught some topics in three skills, namely, writing, listening and reading (just three, not speaking skills) to Chinese students in VIT University. The other skill speaking was trained by other teachers among the four. Students were trained to listen to English words and passages, to read the comprehension passages and answer the questions, and to coach basic grammar and revising it. More over, beginners were also guided to learn technical words related to their respective disciplines (major subjects) other than English words. For example, Chinese students posed a query to the faculty to explain on technical words and terms of their main subjects in English, for instance, B.Sc Computer Science (under graduate programme) students wished to learn about the word data. Since, the English Oxford Dictionary meaning is ‘facts or statistics used for reference or analysis’, but in the field of Computer Science, the word means “information processed by a computer”. So, there arouse a need to help them in distinguishing the different meanings of the word. In addition to, many students were not familiar with English. Thus through the above said way of facilitating, they acquired a good knowledge by varied types of expressions to master their particular subjects. It was a moment to state that they had come from China to India to obtain the nuances of English language. They undertook and were gradually expertised at specific courses in English medium of instruction, perhaps to get degree. Teacher’s a few lesson plans (how the practices are conducted in listening, reading and writing skills) as well as some parts in allotted syllabus (listening to songs, passages, writing a paragraph and essay, picture-story writing and write about yourself, reading the passage and writing) were discussed in the current paper. Role of the teacher and student were explained in detail. Therefore, the abstract would portray how the beginners were trained, taught, convinced, persuaded and managed by a tutor to reach the goal of English language teaching to Chinese students.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".