STTR Phase I: A Student Centered Adaptive Learning Engine

该STTR第一阶段项目提出开发和验证以学生为中心的自适应学习引擎，该引擎专注于使用从新的和现有的教育技术项目收集的数据，结合先进的技术来自动生成自适应能力，从而提高学习效果，从而创建准备就绪的智能辅导系统。 为学生提供自适应教学已被证明是提高学生成绩的有效方法，但由于创建自适应内容的成本很高，很少有教育软件利用自适应教学。这种数据驱动的引擎将通过创建从收集的学生数据中获得智能辅导能力的新方法来显着降低自适应学习的成本。与纯粹的机器学习解决方案不同，该引擎将允许人工输入，通过不断改进来最大限度地提高性能。通过使用以前从现有导师收集的大型数据集，可以测试和验证这些目标。人类输入与机器学习的结合有可能在理解学生建模方面取得重要进展。最后，该引擎将包括新的可视化，为研究人员、开发人员和教育工作者提供工具，以允许对学生如何学习的新见解的方式探索学生数据。自适应学习引擎的更广泛/商业影响包括连接到教育软件的能力，从而为软件公司提供服务，改进和扩展他们的新的和/或现有的软件，以适应个别学生和最大限度地学习。由于创建自适应指令的高成本，将自适应指令添加到现有软件传统上是困难的。该引擎通过提供与现有软件和新软件的连接，降低了提供自适应指令功能的成本。现有的软件可以在不完全重新开发的情况下增加功能，为现有的教育软件公司创造全新的市场，同时使智能导师成为主流。该引擎将为教育工作者提供新的工具，以了解学生如何使用软件系统学习。K-12、高等艾德和企业/政府教育市场的主要软件提供商将在保持现有培训和辅导工具的同时，提高学生的学习能力。公司和组织正在寻找有效的在线教学和培训解决方案，这些解决方案可以灵活地满足用户不同的学习需求和偏好，以最大限度地提高学习效率。该引擎将现有的专业知识和研究与创新愿景相结合，以扩大智能辅导系统的能力，从而使用以人为本的数据驱动方法进入各种市场。

This STTR Phase I project proposes to develop and validate a student centered adaptive learning engine that is focused on improving learning outcomes using data collected from new and existing educational technology projects combined with advanced technology to automatically generate adaptive capabilities, thus creating ready-to-go intelligent tutoring systems. Providing adaptive instruction to students has been shown to be an effective way to improve student performance, yet very little educational software takes advantage of adaptive instruction due to high cost of creating adaptive content. This data-driven engine will significantly reduce the cost of adaptive learning by creating new methods of deriving intelligent tutoring capabilities from collected student data. Unlike pure machine learning solutions, this engine will allow for human input to maximize improvements through refinement over time. By using large datasets previously collected from existing tutors, these objectives can be tested and validated. The combination of human input with machine learning has the potential to make important gains in understanding student modeling. Finally, the engine will include new visualizations providing researchers, developers, and educators the tools to explore student data in ways that will allow for new insights into how students learn.The broader/commercial impact of an adaptive learning engine includes the ability to connect to educational software providing a service to software companies thereby, improving and extending their new and/or existing software to adapt to individual students and maximize learning. Adding adaptive instruction to existing software has traditionally been difficult due to the high costs of creating adaptive instruction. This engine reduces the cost of offering adaptive instruction capabilities by providing connections to existing and new software. Existing software can add capabilities without complete redevelopment, creating whole new markets for existing educational software companies, while bringing intelligent tutors mainstream. The Engine will provide educators new tools to understand how students learn with software systems. Key software providers in the K-12, Higher Ed, and Corporate/Government educational markets will enhance the learning of their students while maintaining existing training and tutoring tools. Companies and organizations are looking for effective online teaching and training solutions that are flexible to meet varying learning needs and preferences of users to maximize learning efficiency. This engine will connect existing expertise and research with the innovative vision to expand the capabilities of intelligent tutoring systems to reach to a variety of markets using a human-centered, data-driven approach.