SBIR Phase II: A Student Centered Adaptive Learning Engine

SBIR第二阶段项目代表了自适应教育技术系统的革命性进步，它使用从以前的练习中收集的数据自动为学生生成提示和反馈。这项工作涉及SBIR教育技术和应用子主题EA 5-学习和评估，使适应性学习广泛提供，并提供工具来评估学生的表现，以便尽早进行干预，帮助学生取得成功。虽然众所周知，自适应基于计算机的训练（CBT）更有效，更有效，但在大多数领域中，传统上生产成本过高。通过利用大数据分析和教育数据挖掘（EDM）的最新研究，该项目将自动产生自适应能力，大大降低生产更有效培训的成本，并使此类培训广泛可用。该技术的核心客户将是培训系统的提供商。这包括出版组织、教育软件工具的开发者以及企业和政府培训的提供者。正在努力教育学生的机构，特别是在STEM领域（科学、技术、工程和数学）领域的专家将能够在现有的教学系统中使用这项技术，从而提高学生的参与度和表现。该项目的最终成果将是一套集成的软件工具，可以从现有的计算机/基于网络的培训软件中收集数据，并自动生成自适应能力，为学生创建个性化的学习环境。它通过使用新颖的EDM和机器学习技术来构建和组织学生和问题模型，随着时间的推移收集更多的数据。它还提供跟踪学生在特定概念或技能方面的进展（知识跟踪），以便在任何时间点进行轻松评估。该系统还动态地选择学生的下一个问题，以最大限度地提高学生的学习，并最大限度地减少掌握一组技能所需的时间（问题选择）。对于复杂的多步骤问题，该系统将提供特定于上下文的即时提示，以帮助学生学习。最终产品将包括数据适配器，允许现有软件的开发人员与该系统无缝连接。最后，该系统的一个主要区别是透明的数据管理过程和相关的可视化工具，这些工具可以揭示问题和学生模型的生成过程。这种人工输入和机器学习的结合将为研究人员、开发人员和教育工作者提供工具，以探索学生数据，并对学生的学习方式提出新的见解。

This SBIR Phase II project represents a revolutionary advance in adaptive educational technology systems by using data collected from previous exercises to automatically generate hints and feedback for students. This work addresses the SBIR Educational Technologies and Applications subtopic EA5 - Learning and Assessment by making adaptive learning widely available and by providing tools to assess student performance in order to make interventions as early as possible and help students succeed. While it is well known that adaptive Computer Based Training (CBT) is more efficient and more effective, it has traditionally been cost prohibitive to produce in most domains. By leveraging the latest research in Big Data analytics and Educational Data Mining (EDM), this project will produce adaptive capabilities automatically, dramatically reducing the costs of producing more effective training and making such training widely available. The core customers for this technology will be providers of training systems. This includes publishing organizations, developers of software tools for education, and providers of corporate and government training. Institutions that are struggling to educate students, particularly across STEM (Science, Technology, Engineering, and Mathematics) fields, will be able to use this technology in their existing teaching systems and thus improve student engagement and performance.The final outcome of this project will be an integrated set of software tools that collect data from existing computer/web based training software, and automatically generate adaptive capabilities to create a personalized learning environment for students. It does this by using novel EDM and machine learning techniques to build and organize student and problem models that improve over time as more data is collected. It also provides for the tracking of student progress on specific concepts or skills (knowledge tracing), allowing for easy assessment at any point in time. The system also dynamically selects the students' next problems to maximize student learning and minimize time needed to master a set of skills (problem selection). For complex multi-step problems, this system will provide context-specific, just-in-time hints to help students as they learn. The final product will include data adapters that allow developers of existing software to seamlessly connect with this system. Finally, a main differentiator of this system is the transparent process of data curation and the related visualization tools that expose the problem- and student-model generation process. This combination of human input and machine learning will provide researchers, developers, and educators tools to explore student data and allow for new insights into how students learn.