Dictionary Culture of University Students Learning English as a Foreign Language in Turkey
Bibliographic record
Abstract
Dictionaries, one of the oldest tools of language education, have continued to be a part of education although information technologies and concept of education has changed over time. Until today, with the help of the developments in technology both types of dictionaries have increased, and usage areas have expanded. Therefore, it is possible to find a dictionary of different types that are applicable to each situation, rather than a single dictionary for every situation. Determining this diversity and the preferences of users is very important in terms of the quality of the education to be given and the new dictionaries to be written.In this study, dictionary preferences of students learning English as a foreign language in Turkey, factors affecting these preferences, past dictionary experiences and trainings were discussed. For this purpose, a survey with 25 questions was collected from 83 students who were learning English in the preparatory classes of Gaziosmanpasa University.The data obtained from the surveys was transferred to the SPSS program and frequency analyses were made. Numerical breakdowns and descriptive analysis of students’ dictionary preferences and factors affecting these preferences were realized. The results revealed that the majority of the students learning English as a foreign language in Turkey did not receive any training on using dictionaries although they bought and used their first dictionaries at primary school. It was also found that language level had an important effect on dictionary usage and as students’ level of language increased they considered dictionaries as easy tools. Besides, students with lower language skills found dictionaries as more informative sources than other students.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".