Incidental Learning of L2 Collocations in an Academic Lecture: A Multimedia Theory Perspective
Bibliographic record
Abstract
This study aimed to examine how L1 Arabic learners incidentally acquire L2 English collocations through various input modes in academic lectures. A quasi-experimental design was employed, involving 87 Arabic learners studying L2 English at a Saudi university. The participants were randomly divided into six groups (5 intervention groups and one control group). An objective type multiple-choice question test was conducted in three phases: a pre-test, immediate post-test, and delayed post-test, to assess the participants' learning. Each experimental group received a specific input mode during the lecture, encountering a total of 17 English collocations. The input modes included listening, reading, reading while listening, viewing, and viewing with captions. The data were analyzed using SPSS version 25.0, employing ANOVA tests to compare mean scores across different test types (pre-test, immediate post-test, and delayed post-test) and the five input modes. The results revealed significant improvements in form-recognition learning from reading, viewing, and viewing with captions. These findings contribute further evidence supporting the effectiveness of academic lectures and multimedia theory in facilitating the incidental acquisition of L2 collocations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".