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EAGER: Technology to Review Online Videos for Education (TROVE): 2139219

Principal Investigator: Claire Christensen
CoPrincipal Investigator(s): Anirban Roy
Organization: SRI International
NSF Award Information: EAGER: Technology to Review Online Videos for Education (TROVE)
Online videos are becoming increasingly popular with young children. This presents a challenge for parents and educators who want them to watch educational videos but may lack the ability or time to distinguish educational from non-educational content within the rapidly growing universe of online video. To our knowledge, there are currently no machine learning methods for classifying educational video content; current methods rely on humans to identify video content. But human content reviews cannot keep pace when, on average, approximately 500 hours of content are uploaded to YouTube every minute. The goal of this project is to develop Technology to Review Online Videos for Education (TROVE), a machine learning-based tool to identify early childhood mathematics content in a high volume of videos. This capability will enable new approaches to increase young children’s exposure to developmentally appropriate mathematics content in videos, which has been shown to improve mathematics learning outcomes. TROVE will lay the groundwork to identify a range of subjects in videos, including literacy, science, and social-emotional content. Further, the technological advances developed under this project will have applications in other fields, including adaptive learning, social media analytics, propaganda detection, and video summarization.

Our multidisciplinary team of education and machine learning researchers will develop a content classification engine to identify mathematics content in online videos. We will define developmentally appropriate mathematics content at the toddler, preschool, and kindergarten levels based on the Head Start Early Learning Outcomes Framework and Common Core State Standards. To train the content classification engine, researchers who have demonstrated reliability in identifying the target mathematics content will annotate the mathematics content in 100 hours of online videos. The project’s central research question asks, how accurately can the content classification engine identify early childhood mathematics content in videos, as compared to humans? To answer this, we will compare the mathematics content identified by TROVE to that identified by researchers in a set of videos that were not used to train the classification engine. We will share our findings with education technology researchers and developers, educators, and policymakers via a peer-reviewed journal article and blog post. TROVE has transformative potential to support young children’s learning through exposure to high-quality, developmentally appropriate educational videos. Further, this technology may enable large-scale research on the impacts of children’s exposure to educational and non-educational video content.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

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