Crash Course in the Classroom: Exploring How and Why Social Studies Teachers Use YouTube Videos
Bibliographic record
Abstract
This article explores how the Crash Course video series are being used as a content-focused resource in the social studies classroom. It argues that the Crash Course series, alongside its YouTube competitors, has significantly stepped in to fill a vacuum left by criticisms and the unpopularity of lectures, textbooks, and feature films. With over 15 million subscribers and accumulated views over 1.9 billion, Crash Course has become an important and ubiquitous force in history and social studies classrooms and represents a new genre of educational media found on YouTube. However, the dramatic rise in the popularity of educational videos online has not coincided with a growth in educational research, particularly in social studies and history education. This article explores the findings of a mixed-methods study that examines how and why history and social studies teachers are using Crash Course videos in their teaching and planning. In particular, it analyses descriptive statistics derived from the results of a teacher survey ( n = 181) and semi-structured interviews with seven social studies teachers who have used Crash Course in their classrooms. The authors found that teachers in the study are regularly using the Crash Course video series to deliver content which is clearly meeting a need many social studies teachers have. The videos–short, easily accessible, and fun–fit nicely with demands to keep students engaged, reduce reliance on textbooks, and explore new content that teachers have little time to learn themselves. This study also found that rarely, if ever, are teachers inviting students to evaluate or assess the content, trustworthiness, or perspective of Crash Course videos. This article discusses what is gained and what are the risks of embracing Crash Course in the social studies and history classroom.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".