A Case Study on Mobile-Blended Collaborative Learning in an English as a Foreign Language (EFL) Context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As learning a foreign language poses a number of challenges for the students, it has become indispensable to search for “optimal” conditions to enhance opportunities of engaging in the target language. Within this context, the Mobile-Blended Collaborative Learning model has been integrated in and out of the classroom learning in order to enable language learners to practice English by means of collaborative, authentic language activities based on project-based learning approach. The purpose of this study was to explore the effects of using mobile instant messaging application, WhatsApp on the language proficiency of EFL students. Participants (N = 85) were enrolled in five upper-intermediate prep classes at a foundation university in Istanbul and took part in a seven-week Project Work. Data were collected through semi-structured interviews and focus group discussions, self- and peer evaluations of group work, a rubric for assessing project work, and log files of WhatsApp conversations. Results revealed practicing English in an authentic setting where the students used the target language for a real purpose facilitated their language learning, improved their communication skills and vocabulary knowledge, and made them recognize colloquial English. Additionally, instant messaging in an informal platform for educational purposes had positive effects on their performance and the quality of their work.
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.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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