Timely adoption of Grammarly to cultivate autonomous learning culture
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.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".