SBIR Phase I: Platform to Coordinate Personalized Learning Between Third Party Mobile Educational Apps to Improve School Readiness

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This SBIR Phase I project proposes to develop a personalized adaptive learning platform for mobile, game-based education that will help improve school-readiness outcomes for young children. The use of mobile devices by children is widespread and increasing, but the existing market for learning apps falls short in providing both evidence-based educational content and engaging experiences. This project attempts to address both of these problems by building a mobile app platform consisting of expertly curated learning apps that are enhanced by adaptive personalization technology. The need for an effective solution to the challenge of preparing our youngest children for school has never been greater, particularly in light of recent findings that emphasize the impact of early education on long-term socioeconomic outcomes. One promising solution to this challenge is personalization, which is known to have strong, positive effects in education. The impact of this project will be to apply personalization in early education, thereby elevating the quality of children's educational apps and improving school-readiness by making learning fun and more effective for children. Given the growing demand for age-appropriate mobile content for children, this project has the potential to generate significant returns as a commercial enterprise and, importantly, through the economic gains resulting from improvements in educational outcomes.This project's core technological innovation consists of an analytic engine, developed specifically to inconspicuously measure children's interaction with the proposed platform's educational content and dynamically adjust game-play to suit each user's individual skills, interests and educational needs. This adaptive learning technology will deliver personalized educational activities that are neither too difficult nor too easy for users, maximizing user engagement and educational outcomes. While personalization has been applied with widespread success in other markets (e.g., e-commerce, advertising, media), it has rarely been applied in early education. The proposed project will combine the personalization techniques used in these markets (collaborative filtering, machine learning) with statistical methods of psychometric assessment (item response theory) and an expert-developed curricular framework to build a mobile platform that not only recommends educational games, but also adjusts the difficulty of play within each game. As children use the platform, data will be unobtrusively gathered on their interests and proficiencies, providing evidence-based assessment of the platform?s efficacy. By observing educational results over time, the platform will ultimately serve to validate the merits of personalization technology in early education and help improve school-readiness outcomes for young children.