A synthesis of research on L2 vocabulary learning through audiovisual input and on-screen text
Bibliographic record
Abstract
Given the cognitive and pedagogical affordances of audiovisual materials and ongoing technological advancements, there is an increasing need to explore how they can be optimized for effective second language (L2) learning (Montero Perez, 2022). The multimodal nature of audiovisual input, particularly when enhanced with on-screen text, offers significant potential for developing multifaceted vocabulary knowledge. This narrative review synthesizes key theoretical frameworks, including Mayer’s Cognitive Theory of Multimedia Learning (Mayer, 2021), Krashen’s Input Hypothesis (Krashen, 1982), Vygotsky’s Zone of Proximal Development (ZPD) (Vygotsky, 1978), and Bjork’s Desirable Difficulties (Bjork, 1994), alongside recent empirical findings. We examine the pedagogical potential of various types of on-screen text (e.g., L1 captions, L2 captions, and bilingual subtitles), identify gaps in the existing literature, discuss methodological challenges and highlight promising directions for future research. By doing so, this review seeks to deepen our understanding of how audiovisual input enhanced with on-screen text can support the development of complex L2 vocabulary knowledge in an increasingly digital learning landscape.
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 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".