Designing Knowledge Dissemination in a Digital Era – Analysing TED Talk’s Multimodal Orchestration
Bibliographic record
Abstract
Online learning has gained increasing attention during the COVID-19 pandemic. Teachers face social exigencies to design ways of knowledge dissemination in online instruction. We posit that understanding how knowledge can be represented in successful online academic genres can inform teachers on how they can design students’ online learning experiences. This study examined how scientific knowledge is disseminated in one of the most widespread academic genres, TED Talks, which share discoursal similarities with other academic genres such as online lectures. This study adopted a systemic functional multimodal discourse analysis approach to explore how a presenter used speech, images, and gestures to disseminate knowledge. The analysis shows that a presenter orchestrates speech, images, and gestures strategically to clarify the scientific ideas and engage the audience. Based on understanding how the three semiotic modes are used to disseminate scientific knowledge in accessible and engaging ways, this paper discusses how insights on multimodal orchestration can function as a heuristic tool to inform design in online learning.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".