Timely adoption of Grammarly to cultivate autonomous learning culture
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
Incorporating technology with linguistics has created opportunities to explore the effectiveness of grammar checkers in cultivating an autonomous learning culture among English as a second language (ESL) and English as a foreign language (EFL) learner. Even though there have been numerous studies on grammar checkers to cultivate autonomous learning culture in higher-education contexts, there are still limited studies in school settings. Thus, this study aims to explore the efficiency of grammar checkers in cultivating an autonomous learning culture among ESL/EFL school students. For this purpose, a qualitative study was conducted, and 13 students aged 16 years from a private Chinese school participated and shared their experiences through a questionnaire. The grammar checker Grammarly has been employed. The findings of this study showed that students found Grammarly easy to use and can correct their writing errors besides improving their grammatical and vocabulary knowledge. Students generally stated that Grammarly helps them to write with less dependence on teachers and helps them to learn the language autonomously. However, 6 out of 13 participants disagreed that Grammarly helps language use. Thus, it is important to know the challenges before employing grammar checkers in the school setting to cultivate an autonomous learning culture.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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