The Effect of Input Enhancement of Collocations in Reading on Collocation Learning and Retention of EFL Learners
Bibliographic record
Abstract
Collocation is one of the most problematic areas in second language learning and it seems that if one wants to improve his or her communication in another language should improve his or her collocation competence. This study attempts to determine the effect of applying three different kinds of collocation on collocation learning and retention of Iranian EFL university students. In this study collocations were presented in highlighted (bold), non highlighted and L1 glossed forms and these three groups of collocations were distributed among three 20 member groups of Iranian TEFL university students in Jahad daneshgahi university in Isfahan. Participants were upper intermediate sophomores and juniors. Participants read three passages under three different conditions (bold collocations, L1 glossed collocations, and non highlighted (text only) collocations). Afterwards, participants answered two collocation tests, one administered immediately after reading the texts and another two weeks later. One way repeated measures of ANOVA and follow up Scheffe post hoc tests (p<.05) showed that the students in L1 glossed group outperformed the students in the other two groups and participants in highlighted group out performed non highlighted ( text only) group.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".