Evaluating Storytelling Videos Using YouTube Analytics
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
Prior research has shown that storytelling is an effective method for increasing comprehension of concepts. Students often find computational topics, such as data structures, to be difficult to grasp initially. To bridge this gap, we investigate whether the use of storytelling in pre-lecture videos increases students' retention and understanding. At a North American university, instructors randomly assigned students to two separate groups who watch different types of pre-lecture videos: one in a traditional format and the other where they teach a concept through storytelling. These videos were deployed as unlisted YouTube links embedded in students' quizzes. Using YouTube's Reporting API, we analyzed the audience retention data against elapsed time to compare audience retention between traditional and storytelling teaching methodologies. There were more storytelling videos with a higher average retention level, and the audience displayed less skipping behaviour than their traditional counterparts. In the future we will further analyze students' perceptions of storytelling videos to better understand higher audience retention and the effectiveness of learning through storytelling lectures.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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