Analyzing the Impact of CALL Tools on English Learners' Writing Skills: A Comparative Study of Errors Correction
Bibliographic record
Abstract
The study seeks to compare the effectiveness of Computer-Assisted Language Learning Tools (ChatGPT, Grammarly, and Google Translate) in correcting the occurrences of common errors in English as a second/foreign language (ESL/EFL) learners’ writing. An experimental design offered instructions for Class A with ChatGPT, Class B with Grammarly, Class C with Google Translate, and Class D was the control group with no exposure to any of these tools. Data were collected from texts created by the students based on pictures, both before and after the intervention. It was found that ESL/EFL learners frequently made eight distinct types of errors in their writing: spelling, punctuation, capitalization, possessive words, verb forms, subject-verb agreement, articles, and prepositions. The interventions across different classes showed that ChatGPT in Class A significantly corrected spelling, verb form, and subject-verb agreement-related errors, Grammarly in Class B excelled in correcting prepositional errors, and Google Translate in Class C effectively addressed errors in article use, capitalization, and possessives. In contrast, the control group in Class D surpassed the others in enhancing punctuation. In Class A, ChatGPT, and in Class B, Google Translate displayed enhanced effectiveness in rectifying common writing errors of ESL learners compared to Grammarly in Class B and the control group. The findings suggest that employing targeted digital tools for distinct grammatical challenges can substantially enhance learning outcomes in a language learning context.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".