An Investigation of the Relationship between Language Learning Strategies and Learning Styles in Turkish Freshman Students
Bibliographic record
Abstract
The purpose of this study is to determine the relationship between the language learning strategies of freshman students and their learning styles. This study is a descriptive research and employs a relational screening model. Participants of the study were 328 freshman students majoring in different fields at Necmettin Erbakan University Ahmet Keleşoğlu Faculty of Education in Turkey. Data were collected via Turkish version of “Strategy Inventory of Language Learning (SILL)”, originally developed by Oxford (1990) and adapted into Turkish by Cesur and Fer (2007) and “Big 16 Learning Modality Inventory” by Şimşek (2002). Data were analyzed by using descriptive statistics and the Pearson’s correlation coefficient. The research results revealed that learning styles have a significant effect on language learning strategy use. The results and implications of the study are discussed and suggestions for future research are offered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".