The effect of research video abstract presentation style on viewer comprehension and engagement
Bibliographic record
Abstract
This study investigated the effect of video abstract (VA) presentation style (slideshow versus animation) on viewer comprehension and user engagement. Video abstracts, short video presentations of journal articles, were selected and used in a randomized between-subjects experiment (N = 290) with Amazon Mechanical Turk (MTurk) participants. The Cognitive Theory of Multimedia Learning (CTML) informed the selection of VAs. Barrett’s Taxonomy of Cognitive and Affective Dimensions of Reading Comprehension (Barrett’s Taxonomy) was used to develop comprehension measures focused on recall and summarization, and user engagement was measured using a questionnaire. The study found that: 1) comprehension outcomes did not vary between slideshow and animation style VAs, 2) animation VAs were perceived to be more engaging than slideshow VAs, and 3) user engagement was weakly negatively correlated with comprehension scores. In other words, animation VAs attracted viewers with their content, but did not lead to increased comprehension. In fact, viewers in the study who reported higher levels of engagement had slightly lower comprehension outcomes.
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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".