Analyses of Sentence Types and Errors in EFL Students’ Paragraphs
Bibliographic record
Abstract
Writing paragraphs is an important part of academic writing courses required at the university level, as mastering paragraph writing is considered a fundamental step toward producing advanced formal and academic papers in the future. To become proficient in this skill, students, especially non-native English speakers, must make a special effort. This is because a good paragraph contains not only information, but also a variety of sentence types to engage the reader. With these qualities, paragraphs will capture the reader’s attention and make the text easier to read. To find effective ways to help students write their paragraphs successfully and correctly, many research studies have analyzed paragraphs in terms of errors, grammar, sentence types, and sentence structures. The present study focuses on sentence-level analysis by examining paragraphs written by EFL students. Forty paragraphs written in English by second-year students majoring in English were analyzed for sentence types, as well as errors of grammar and mechanics. It was found that simple sentences were the dominant type, followed by complex sentences. Punctuation was the most common mechanical problem. The findings contribute to pedagogical implications, highlighting the necessity of raising awareness about the importance of sentence variety in paragraph writing. The correctness of grammar and the proper use of mechanics were also key issues for writing a good paragraph.
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 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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".