Error Analysis on EFL Students’ Thesis Proposal Writing
Bibliographic record
Abstract
The present research was intended to analyse the errors made by the university students, especially the students in the eight semesters who write thesis proposals. Hence, the main objective of the present research was to investigate types of errors as well as to know the dominant errors which were existed in students’ thesis proposal compositions. This research employed descriptive quantitative study by calculating the number of errors by percentage. There are 42 participants from English Education Department in one of public university in West Nusa Tenggara, Indonesia. The results showed that there were 195 errors consisting of 71 (36%) addition errors, 64 (33%) misformation errors, 48 (25%) omission errors, and 12 (6%) misordering errors. The most dominant errors as shown by the percentage were students tended to add more than the structure or grammar needs. Students overgeneralized to use affix –s in verb and to use double auxiliaries (be, are, is). These results suggest that the instructor needs to help students on understanding and practicing more to fix the errors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.144 | 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 teacher head, 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".