An ESL Motivations Assessment for a Community-Based ESL Programme
Bibliographic record
Abstract
This paper concerns an ESL motivations assessment of adult Chinese learners at Chinese Information and Community Ser-vices (CICS). 512 ESL learners partici-pated in the survey. The findings of the survey are as follows: (I) The motives of adult Chinese immigrants attending ESL classes include linguistic needs, basic skills, cultural awareness, social interac-tion, and writing resumes. (2) There are no significant differences in perceived motivations according to age, education level, and length of stay in Canada; how-ever, there are slight differences among a few indicators. (3) The implications to ESL teaching are that a) teaching objec-tives at the CICS of Metropolitan Toronto should include both the teaching of English and Canadian culture; b) the teaching of English should focus on language needed for conducting everyday life and social interaction; c) all four language skills (speaking, listening, reading, and writing) should be taught at the same time with more emphasis on the first three skills; d) pronunciation and vocabulary teaching is also necessary. Recent developments in language teaching include interest in language teaching objectives, language content, and curriculum design (Stern 1983). Curriculum design, especially its content and objectives, should meet learners ' needs. Meeting learners ' needs is particularly important in the adult learning situation. Brundage and Mackeracher (1980: 106) write that "success in satisfying needs and reaching established objectives becomes a reinforcer for the changes already made and a motive for further learn-ing. " One method for eliciting learners ' needs is through a motivation assessment.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".