Motivational Factors for Learning English as a Second Language Acquisition in Canada
Bibliographic record
Abstract
New immigrants' arrival in any country indeed brings new challenges to settle in the country. Learning the language of a new country is one of the major hassles in settling and starting a new life. In Canada, new immigrants must learn English as a second language because English is an official language and is also used day to day in almost all provinces except for a few states where the French language is more acceptable compared to English. Learning English requires motivation and there are some barriers in learning. Thus, this study addresses these barriers and assesses what motivational factors are there for new immigrants to learn English. It was a cross-sectional quantitative study conducted at a poly-cultural center, and Canada from June-2018 to December-2018. A total of 325 participants who registered and gave consent were included in this study. The results showed that factor 1 (Desire for career and economic enhancement) is significantly associated with age groups (P=0.001), gender (0.001), educational status (P=0.012), and time in Canada (P=0.027). Factor 2 (Desire to become a global citizen) is not significantly associated with all demographic data except for gender (P=0.027). Factor 3 (Desire to communicate and affiliate with foreigners) shows an association with all other demographic characteristics, except for gender (P=0.63), nationality (P=0.568), and educational status (P=0.091). In factor 4 (Desire for self-satisfaction), only educational status (P=0.046) has a significant association. Factor 5 (self-efficacy) and factor 6 (Desire to be integrated with other cultures) do not show any significant association with any demographic characteristics. In conclusion, the main motivating factors for new immigrants learning English as a second language acquisition is the desire for career and economic enhancement and the desire to communicate and affiliate with foreigners.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".