Common Errors of Using Gerundial and Infinitival Forms in EFL Learners’ Writing
Bibliographic record
Abstract
This research focused on the common errors that EFL learners included in their writing. The frequent errors that the study focused on were at the micro level, specifically the structures that related to gerund and infinitive forms. The participants were selected from the English language department at one of the Saudi universities. They were undergraduates and passed the English Grammar 1 course in the program and were ready for the English Grammar 2 course. In this study, the participants passed through two phases. In the first phase, they wrote one paragraph individually in the midterm examination. In the following phase, they chose their group and wrote a paragraph during class time. In both phases, students received clear instruction including the topic, the grammatical rules, and the minimum number of sentences. The only difference was that students were allowed to discuss for ten minutes before writing their paragraphs in the second phase. To analyze the data, common errors were identified and classified from writing as groups and individual writing based on gerunds and infinitives. Then, the comparison between common errors was employed to understand learners’ written production when working individually and in groups. The results indicated that learners easily used infinitives better than gerunds. Learners also overused specific words to ensure that they used gerund and infinitive in the correct form. Finally, the same common errors were found and identified in their individual writing and as groups.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".