Open Access Academic Lectures as Sources for Incidental Vocabulary Learning: Examining the Role of Input Mode, Frequency, Type of Vocabulary, and Elaboration
Bibliographic record
Abstract
Abstract Open access academic lectures are potential sources for incidental vocabulary learning. These lectures are available in various formats (transcripts, audios, videos, and video with captions), but no studies have compared the learning of vocabulary in these lectures through different input modes. This study adopted a pretest–posttest design to compare learning at the meaning recall level of 50 words in the same academic lecture through five input modes: reading, listening, reading while listening, viewing, and viewing with captions. One hundred sixty-five English for Academic Purposes learners in China were assigned to five experimental groups and a control group. The experimental groups received the treatment with the assigned input mode while the control group received no treatment. Results show that although learning occurred through all input modes, only viewing significantly contributed to the learning gains. Frequency of occurrence and type of vocabulary significantly predicted the learning gains, but the type of verbal elaboration and nonverbal elaboration did not. This study provides further insights into the value of academic lectures for incidental vocabulary learning and supports the multimedia learning theory and its principles.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| 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".