INCIDENTAL LEARNING OF SINGLE WORDS AND COLLOCATIONS THROUGH VIEWING AN ACADEMIC LECTURE
Bibliographic record
Abstract
Abstract Academic lectures are potential sources of vocabulary learning for second language learners studying at universities where English is the medium of instruction, as well as those in English for Academic Purposes (EAP) programs. Topic-related vocabulary is likely to occur frequently in academic texts, and academic speech consists of a reasonable proportion of frequently occurring sequences of words. Yet no intervention studies have explored the potential for learning single words and collocations through viewing a video of an unmodified academic lecture. To address this gap, this study collected data from 55 EAP learners in China, following a pretest-posttest design. The experimental group (n = 28) watched a video of an academic lecture in which 50 target single words and 19 target collocations were presented while the control group (n = 27) received no treatment. Results show that viewing the lecture led to significant learning gains of single words at the meaning recall level and collocations at the form recognition level. Frequency of occurrence in the lecture appeared to significantly contribute to the learning of single words but not the learning of collocations. Prior knowledge of general vocabulary appeared to make no significant contribution to the learning of single words and collocations.
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 | 0.002 |
| 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 | 0.000 |
| 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".