Bibliographic record
Abstract
As technology rapidly advances, integrating new tools into teaching becomes increasingly essential. Mobile phone applications are widely used in education, yet WhatsApp remains underutilized in language instruction. This study aims to explore the impact of WhatsApp on university students' writing skills. Forty international students were selected through convenience sampling to participate in a single-group pre-test post-test design, where they responded to specific writing prompts. Their writing samples were evaluated using a standardized rubric, and the pre-test and post-test scores were analyzed with SPSS using a paired sample t -test. The findings indicate a significant positive correlation between students' frequency of WhatsApp use and their writing improvement. Additionally, providing examples of writing by the teacher or students in the WhatsApp group contributed to better writing outcomes. The study concludes that WhatsApp, through writing exercises and group vocabulary practice, positively influences students' writing abilities. Furthermore, the use of messaging apps enhances participation, interaction, collaboration, and overall language proficiency. These results underscore the potential of WhatsApp as an effective tool for language instruction in higher education. Ultimately, the study highlights the need for educators and curriculum designers to embrace mobile technologies like WhatsApp to foster improved writing skills and enhance student engagement in language learning contexts. • The research identified a significant correlation between frequent WhatsApp use and improvement in writing skills. • Providing examples and engaging in collaborative vocabulary practice through WhatsApp enhanced students' writing abilities. • WhatsApp facilitated collaborative learning, resource sharing and interaction which contributed to improved writing skills. • WhatsApp allowed students to practice writing and skills outside the conventional classroom, fostering ongoing learning. • WhatsApp enabled prompt and effective writing skills.
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.
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 | 0.000 | 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 | 0.000 | 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 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".