Bibliographic record
Abstract
This thesis considered whether multimedia learning materials should simultaneously combine continuous text captions, audio, and video. Multiple Resource Theory suggests that interference among media competing for resources may diminish attention and learning. In contrast, redundancy advocates claim that when media present redundant content, learning is not limited by interference and may even be enhanced. These contrasting perspectives were tested by measuring recognition, comprehension, and subjective experience in three groups. One group saw an audio-video (AV) program with redundant text captioning ("in-synch"). A second group saw the same content, but the captions were delayed by 10 seconds. The control group saw only the AV. Results were marginal, but indicated some trends. The "in-synch" group's recognition of audio content was enhanced relative to the control group. They found it easier to pay attention, were more biased to say they recognized audio content-prompts, and tended to be more sensitive (accurate) than the control. In fact, significantly more subjects had a higher sensitivity to audio than video. The group which received "out-of-synch" captions also were more biased, but not more sensitive than the control, and found the captions difficult to follow.
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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".