How Do Consumers Evaluate Explainer Videos? An Empirical Study on the Effectiveness and Efficiency of Different Explainer Video Formats
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
There is a significant rise in the use of videos. More and more people use videos not only as a source of information but also as learning tool. This article explores the future potential of explainer videos, a format that conveys complex facts to a target group within a very short time. The findings are based on an empirical study representative for the German and U.S. population (18+ years). In the first step, the status quo of the use of e-learning in general and explainer videos in particular is presented. Subsequently, the effectiveness and efficiency of five different explainer video formats are analyzed using an experimental test design for one topic (US presidential election). On one hand, all formats reach a favorable evaluation based on the perception of the respondents, with only a few differences between test groups. On the other hand, significant differences occur in terms of relative improvements in knowledge level as well as input/output-ratios. Thirdly, expectations of potential users regarding the design of explainer videos are determined.
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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 | 0.002 | 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 | 0.000 | 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 it