A Study on English Collocation Errors of Thai EFL Students
Bibliographic record
Abstract
Collocation is an often-neglected language form that foreign language teachers and students should focus on to achieve competence in a target language. In this research, the researchers studied purposely to facilitate both second language teachers and students to produce correct and appropriate collocations. The purposes of this research were 1) to study the collocation error levels of Bachelor of Arts English and Business English students studying at Northern Rajabhat University, 2) to study the relationship between first language (L1) and second language (L2) transfer collocation errors in students’ writing, and 3) to compare the collocation errors between high-proficiency students, medium- proficiency students and low-proficiency students. The research sample included 285 Thai EFL students enrolled in second-year English and Business English programs at Northern Rajabhat University during the first semester of the academic year 2021. The research instrument was a collocation proficiency test with 54 questions, which was divided into 2 sections: 1) 36 questions with 4 multiple-choice tests, and 2) 18 Thai to English translation questions. The items were chosen from the Oxford 3000™, and were common vocabulary appearing in various contexts. Research data were analyzed using mean, standard deviation, t-test (Dependent), and Friedman test. The research found that 1) the level of Grammatical Collocation errors of high-proficiency EFL students was at a moderate level, medium-proficiency and low-proficiency EFL students were at a high level, and for Lexical Collocation errors, all three groups of students were at a high level; 2) the EFL students’collocation errors were caused by the language transfer from their first language (L1) to their second language (L2), synonyms, and lack of collocation competency; and 3) the most Lexical Collocation errors found in all student groups were Adverb + Adjective. For Grammatical Collocation, all groups could use Verb + Preposition better than Noun + Preposition, and the high proficiency and medium proficiency students could perform Adjective + Preposition the least, whilst the low proficiency students were better at Adjective + Preposition.  
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 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".