Lexical Errors of Third Year Undergraduate Students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of the study was to examine the lexical errors made by EFL students. The technique for eliciting information employed was an achievement test. A sample of 30 Saudi female students was asked to write essays in English that were assessed by the researcher. The students were all majoring in English in the third year at King Khalid University. James (1998) taxonomy was selected as the most comprehensive framework for the analysis of the lexical errors in the students' writing. A total of 137 lexical errors were identified and analysed. These errors were divided into formal 117 (85.40) and semantic 20 (14.60). Formal mis- selection 54 (39.42) was the most frequent major category of lexical formal errors while mis-formation 15 (10.95) was the least frequent one. Confusion of sense relations 14 (10.22) was the most frequent among lexical semantic errors. At the individual level of lexical formal errors, the most problematic words for students were the vowel based types 24 (17.52) and borrowing and blending were not problematic at all. At the individual level of lexical semantic errors, the most problematic words for students were near synonyms 8 (5.84) and the least problematic words were general terms for specific ones and overtly specific terms 1 (0.73).Pedagogical implications for teaching vocabulary to EFL learners and recommendations for areas for further research were suggested.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 it