A machine learning model to detect early math content in YouTube videos

Purpose Supporting the development of math skills in early childhood has wide-ranging benefits. Exposure to high-quality screen media is a convenient and effective way to promote early math skills. Yet as children’s video consumption has shifted away from television and toward online streaming platforms, it is more difficult to understand the extent to which they engage with high-quality early math media. We developed the first machine learning tool to automatically detect prekindergarten and kindergarten math content in online videos for young children. Design/methodology/approach We describe our approach to developing a multimodal machine learning model. The process included codebook creation, video annotation, model development, and model training and testing. To test the model, we compared its performance for classifying video content to that of human annotators, in both a curated and a naturalistic dataset. Findings Compared with human annotators, the model detects early math content with 92% precision in a curated dataset and with 63% precision when adapting to a new dataset of videos watched by children. We describe the model’s precision for detecting math content categories and indicators of pedagogical quality. Further, 79% of videos in the curated dataset that included at least one early math content category also included at least one pedagogical quality indicator. Originality/value This is the first machine learning model to detect standards-aligned early math content in videos. Because this method is more efficient than human content classification (the current standard practice), it can better keep pace with the rate of online video production. In the future, developers could use this capability to create a content recommender tool for parents and educators. Developers could also create tools for researchers to better understand the effects of online video exposure on children’s early math learning. These findings offer a scalable new method for understanding and improving young children’s exposure to high-quality educational media.