A Comparative Study of the Error-Detection Accuracy of Grammarly and Microsoft Word Editor in Formal English Writing
Bibliographic record
Abstract
This study attempts a comparative overview of Grammarly and Microsoft Word Editor, two software programs used extensively to check and improve English language mistakes, to determine their respective effectiveness across various types of writing. Both of these tools are used across documents ranging from blogs to articles, research reports, and newspapers; samples of these documents are part of the representative corpus. Based on this corpus, the study carries out a detailed analysis to establish the exactness and efficiency of these tools in determining and improving errors in the English language. The findings of this research align with those of other studies, establishing that both tools have different levels of effectiveness vis-à-vis the categories of errors and types of texts. Grammarly, for example, outperforms Microsoft Word Editor in several error categories, such as subject-verb agreement, prepositions, and pronouns, whereas Microsoft Word Editor excels in detecting errors in English tenses. As a result, the research recommends using both tools as complementary resources to ensure comprehensive error detection and correction in formal English writing. Also, the findings provide valuable insights for educators, students, and professionals seeking to advance their writing quality. They also offer a basis for further research into integrating these tools in writing instruction and developing more comprehensive language editing tools.
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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 | 0.025 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".