Open Access Academic Lectures as Sources for Incidental Vocabulary Learning: Examining the Role of Input Mode, Frequency, Type of Vocabulary, and Elaboration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 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 it