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

该SBIR第一阶段项目提议为移动的、基于游戏的教育开发一个个性化的自适应学习平台，这将有助于改善幼儿的入学准备结果。儿童对移动的设备的使用越来越普遍，但现有的学习应用程序市场福尔斯在提供基于证据的教育内容和引人入胜的体验方面存在不足。本项目试图通过构建一个移动的应用平台来解决这两个问题，该平台由专业策划的学习应用组成，并通过自适应个性化技术进行增强。现在比以往任何时候都更需要有效解决为我们最年幼的儿童上学做准备的挑战，特别是考虑到最近的调查结果强调了早期教育对长期社会经济成果的影响。解决这一挑战的一个有希望的解决方案是个性化，众所周知，个性化对教育具有强大的积极影响。该项目的影响将是在早期教育中应用个性化，从而提高儿童教育应用程序的质量，并通过使儿童的学习变得有趣和更有效来改善入学准备。鉴于儿童对适龄移动的内容的需求不断增长，该项目有可能作为商业企业产生巨大回报，重要的是，通过改善教育成果带来的经济收益。该项目的核心技术创新包括分析引擎，专门开发，以不显眼地衡量儿童与拟议平台的教育内容的互动，并动态调整游戏，游戏，以满足每个用户的个人技能，兴趣和教育需求。这种自适应学习技术将提供个性化的教育活动，对用户来说既不太难也不太容易，从而最大限度地提高用户参与度和教育成果。虽然个性化已经在其他市场（例如，电子商务，广告，媒体），它很少被应用于早期教育。拟议的项目将联合收割机在这些市场中使用的个性化技术（协同过滤，机器学习）与心理测量评估的统计方法（项目反应理论）和专家开发的课程框架相结合，以建立一个移动的平台，不仅推荐教育游戏，而且还调整每个游戏的难度。当儿童使用该平台时，将不引人注目地收集有关其兴趣和专业的数据，为平台提供基于证据的评估？的功效。通过观察一段时间的教育结果，该平台最终将用于验证个性化技术在早期教育中的优点，并帮助改善幼儿的入学准备结果。

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.