SBIR Phase I: Developing a personality and usage-based user model for an advanced personalized learning system for pre-collegiate and remedial mathematics

SBIR 第一阶段：为大学预科和数学补习的高级个性化学习系统开发基于个性和使用的用户模型

• 批准号: 1345718
• 负责人: Smita Bakshi
• 金额: $ 15万
• 依托单位: Zyante Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1345718&HistoricalAwards=false
• 关键词: SBIR; Phase; Developing; personality; usage

This SBIR Phase 1 project proposes to demonstrate the feasibility of an advanced personalized learning system for pre-collegiate and remedial Algebra, with future extension to other scientific and mathematical disciplines. The project aims to model learning as a complex dynamics system, and to develop a user-model that incorporates student profile data with a detailed and large set of markers on usage and performance collected from an analytics engine. These markers can be derived from a student's learning and social activities, such as her interaction with various types of learning resources, participation on question and answer forums and so on. This model then guides a recommender system to select appropriate resources from a learning catalog based on the student's unique learning path. Whereas, the project uses open source recommender algorithms, the intellectual merit lies in developing a user model, identifying the specific markers that impact learning, automatically reconfiguring the learning material, and in demonstrating its viability for algebra learners. This model will be built leveraging some of the latest infrastructures in education technology including an authoring and delivery framework, a learning catalog of 10 million curated learning resources, and the latest advances in data-analytics and information filtering systems. The broader/commercial impact lies in using the proposed technology to replace textbooks and other less-advanced learning systems, not only in mathematics, but also in other STEM disciplines, making it easier, faster and more affordable for students to learn. To an extent, it will level the playing field by providing equal access to a personalized 1-on-1 type of experience, even for those students who don't have the opportunity to get high quality instruction and mentorship today. It will significantly impact students who today struggle with mathematics and STEM subjects, by providing alternate and more relevant ways for them to visualize and learn. Additionally, Zyante's technology and expanded services will enable colleges/universities to offer high-quality online courses and successfully handle larger enrollments. All this will ultimately improve national outcomes and provide a better-trained workforce in disciplines needed to drive the economic success of the US. For these reasons, it is important that the proposed technology be readily commercialized.

这个SBIR第一阶段项目建议展示一个先进的个性化学习系统的可行性，用于预科和补习代数，并在未来扩展到其他科学和数学学科。该项目旨在将学习建模为一个复杂的动态系统，并开发一个用户模型，该模型将学生档案数据与从分析引擎收集的一组详细和大量的使用和性能标记结合在一起。这些标记可以来自学生的学习和社会活动，如她与各种类型的学习资源的互动，参与问答论坛等。然后，该模型指导推荐系统根据学生独特的学习路径从学习目录中选择合适的资源。鉴于该项目使用开源推荐算法，其智力优势在于开发用户模型，识别影响学习的特定标记，自动重新配置学习材料，并展示其对代数学习者的可行性。这一模式将利用教育技术中的一些最新基础设施，包括编写和交付框架、1000万精选学习资源的学习目录以及数据分析和信息过滤系统的最新进展。更广泛的商业影响在于使用拟议的技术来取代教科书和其他不太先进的学习系统，不仅在数学领域，而且在其他STEM学科中也是如此，使学生更容易、更快、更负担得起学习。在某种程度上，它将通过提供平等的机会获得个性化的一对一类型的体验，即使是那些今天没有机会获得高质量指导和指导的学生，从而创造公平的竞争环境。它将为今天在数学和STEM科目上苦苦挣扎的学生提供另一种更相关的可视化和学习方法，从而对他们产生重大影响。此外，Zyante的技术和扩展的服务将使学院/大学能够提供高质量的在线课程，并成功地处理更多的招生。所有这一切最终将改善国家成果，并在推动美国经济成功所需的学科领域提供训练有素的劳动力。出于这些原因，重要的是，建议的技术应易于商业化。