Language Learning Strategies Use and Challenges Faced by Adult Arab Learners of Finnish as a Second Language in Finland
Bibliographic record
Abstract
Adult Arab learners of Finnish as second language (FSL) often encounter communication difficulty when dealing with official documents. They also cannot help their children in their school homework. FSL proficiency is an essential requirement to get an employment and to obtain the Finnish citizenship. The aim of this paper is to explore the use of the language learning strategies by a number of adult Arabs learning FSL in Finland. In addition to issues and difficulties related to the learning process encountered by this category of learners. Oxford’s Strategy Inventory for language learning was used for the purpose of data collection and SPSS programme was employed to analyse data collected from the questionnaire, however, interview data were analysed manually. 30 (20 male and 10 female) adult Arab FSL learners taking beginning level course in Finnish at Helsinki School for Adult Learners participated in the current study. The results showed that adult Arab learners of Finnish used the language learning strategies at medium level with the average of (m=3.25). The results also showed a number of challenges that impede their second language learning process like the low literacy level of the learners, lack of communication with the Finnish society, and difficulties in reading and writing in Finnish.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".