Error Analysis of Passive Voice Employed by University Students’ in Writing Lab Reports: A Case Study of Sudan University of Science and Technology (SUST) Students’ at Faculty of Sciences, Chemistry Department
Bibliographic record
Abstract
The study aims at analyzing errors made by Sudan University of Science and Technology students’ at faculty of Sciences-Chemistry Department in employing passive voice in writing lab reports. The study focuses precisely on identifying the types of errors occurred in using passive voice and the reasons behind these errors. Descriptive qualitative method is adopted and applied to obtain and process the gathered data. To run this study and to collect reliable data, thirty chemical students are chosen randomly as the subject of the study. Samples of the students’ lab reports are collected and analyzed. The collected data is analyzed according to the Dulay et al. (1982) Surface Strategy Taxonomy model. Teachers’ questionnaire is also used to find out the sources of the students’ errors from the teachers’ point of view. The findings of the study reveals that the majority of the students’ errors are categorized as omission and misinformation whereas additions and misordering errors are fewer and unconsidered. According to the teachers, these errors are attributed to the interference of the mother tongue, lack of knowledge and carelessness of the students.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".