Perceptions of Turkish EFL Students on Online Language Learning Platforms and Blended Language Learning
Bibliographic record
Abstract
The purpose of this study is to examine the perceptions of EFL students studying English at the School of Foreign Languages, Anadolu University (AUSFL) on blended language learning and online learning platforms. The participants of the study consisted of 167 students whose English language proficiency level was B2 according to the Common European Framework of Reference (CEFR). A questionnaire adapted from Owston, York and Murtha (2013) was used in the study.After application of the questionnaire, ten randomly selected students were interviewed about their perceptions of blended learning. Applying statistical and content analysis of the interviews provided a deeper understanding of students’ perceptions. Statistical analysis showed that students liked the idea of blended learning in terms of course format and attendance. Analysis of the interviews in terms of content revealed that students liked the flexibility of online learning, but preferred face-to-face communication with a teacher and classmates. In terms of their ideas about the online platforms of course books, their ideas varied. The students were mostly positive about using online language learning platforms. Even though the aim of the study was to get the perceptions of students, interviews were carried out with 5 teachers about students’ mid-term and final exam scores to get an idea if engaging in blended learning helped them learn better. Based on the results, certain implications were drawn from the study in order to organize future teaching at the AUSFL and implement a teaching environment utilizing blended language learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".