Student characteristics, teaching methodology and satisfaction with adult english as a second language programs in the Thunder Bay area / by Wenjie Li. --
Bibliographic record
Abstract
This research suggests that adult ESL programs are quite \nsuccessful. Most students have positive attitudes towards \nEnglish study and share positive feelings in class. They take \nan active part in classroom activities and have made progress \nin their English acquisition. \nThe research was conducted among 94 adult ESL students \nand their ESL teachers in the Thunder Bay Area. Most of the \nparticipants are immigrants from Europe, Asia and South \nAmerica. They are presently studying in adult ESL programs \nprovided in schools, an adult education centre and a college. \nA questionnaire survey was used to gather information for \nthis correlational study. The expectations were that student \nachievement is related to their education, age, pre-course \nEnglish proficiency, origin, classroom behaviours, feelings \nin class, motivation, confidence or anxiety level. \nResults indicate that higher pre-course English \nproficiency, younger age, low anxiety level, active \nparticipation and positive feelings in class are closely \nrelated to higher achievement level. Students who had high \neducation levels tend to have higher pre-course English \nproficiency and motivation levels, and spend more time in ESL \nclass. No significant differences are found between different \norigins in motivation, attitude, classroom behaviour and \nachievement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".