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Record W2603367688 · doi:10.5539/elt.v10n4p111

Language Learning Strategies Use and Challenges Faced by Adult Arab Learners of Finnish as a Second Language in Finland

2017· article· en· W2603367688 on OpenAlexvenueno aff
Ahmed H. Naif, Noor Saazai Mat Saad

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyLanguage learning strategiesReading (process)Language acquisitionLiteracyAdult educationMathematics educationPedagogyLinguisticsMetacognition

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.262
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2017
Admission routes1
Has abstractyes

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