Evaluating Storytelling Videos Using YouTube Analytics
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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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.003 | 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".