An Examination of the Experiences of Turkish ELLs about the Chatbot Apps to Learn English
Why this work is in the frame
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Bibliographic record
Abstract
In education, chatbots have been used as learning environments that provide feedback for students to repeat their knowledge. The aim of this study is to examine Turkish students' experiences about the chatbot apps they use to learn English. The study was carried out with 21 ELLs using a chatbot app at different ages. In this study, data were collected through semi structured interviews and analyzed using content analysis. The data obtained from the participants were coded and digitized into themes. The research results show that although chatbots have some shortcomings, they are seen as a sincere friend who teaches English. Moreover, participants who stated that they were embarrassed to communicate with live people expressed that learning English with chatbot applications was more convenient for them.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it