On the Impacts of Four Collocation Instructional Methods: Web-Based Concordancing vs. Traditional Method, Explicit vs. Implicit Instruction
Bibliographic record
Abstract
Due to the fact that collocations have been considered as one of the main concerns of both EFL learners and teachers for many years, the present study has dealt with this issue in a three-dimensional way. First, it compared the efficiency of teaching collocations both through web-based concordancing practices and through traditional methods. Second, it investigated and compared the impact of implicit and explicit collocation teaching on the students` learning. Third, it examined the effect of L1 (Farsi) on collocation learning; in other words, the effect of congruent (those collocations which have equivalent in Farsi) and non-congruent collocations. Fifty-four EFL students participated in this study. At the beginning, the researchers gave the participants a Michigan test to select those with the same level of proficiency. There were two treatments: A and B, the former investigated the effect of concordancing and traditional approaches, and the latter examined the implicit and explicit collocation teaching. In both treatments, learners were randomly divided into two experimental and control groups. There were both a pre-test and a post-test to determine the effect of treatments. Subsequently, after obtaining the data, some statistical analyses (t-Tests) were performed. The results indicated that concordancing approach was highly efficient in teaching and learning collocations, and participants’ scores learning collocations through this method were higher than learners’ scores in traditional method (especially in learning non-congruent collocations that the difference was significant); in addition, learners’ performance in the group receiving explicit instruction of collocations was meaningfully better than those receiving implicit instruction through mere exposure. Key words: Collocation Learning; Web-based Concordancing; Traditional Method; Explicit Instruction; Implicit Instruction; L1
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".