SBIR Phase II: An embedded and in-context professional learning platform for math problem-solving instruction

这个项目建议开发一种创新的方法来提高和维持数学教育者的问题解决教学技能。尽管《共同核心国家标准》对数学教师寄予厚望，但大多数教师都没有做好充分准备，教会学生如何成为批判性思考者。这种问题在很大程度上是由于教师在提供足够的问题解决指导和支持方面的教学技能有限，而教师本身解决问题的技能有限。该项目以其集成的模块和强大的分析引擎来补救这一问题，该引擎为专家和新手教师提供了学习途径。他们的研究发表在全国数学原理教师行动委员会上。它将帮助教师培养计划和评估课程的信心和技能，以及理解学生的误解和及时干预。自信地解决问题的教师会激励学生以同样的方式处理困难的数学任务。这对将有多少学生继续攻读科学、技术、工程和数学课程具有重大影响。此外，该项目为教育工作者培养21世纪的技能奠定了基础，包括批判性思维、沟通和协作--这是他们指导的年轻人的基本工作技能。这一努力改进和扩大了他们的产品，这是一个网络和移动应用程序，与我们目前的面向学生的平台无缝结合，为教师提供及时的支持，以提高学生解决问题的技能和数学交流。该项目将利用虚拟同行、丰富的知识、互动工具和可操作的数据，持续提供专业发展。分析引擎利用自适应学习模型来构建健壮的模块。数据收集器层将包含用户获取建议、接收用户反馈和提供其他分析报告的接口。分析核心层将使用一组机器学习算法来实现。服务层将根据用户配置文件、用户反馈、预先存储的最佳实践和其他使用案例计算建议。持久层将计算得到的数据存储到推荐引擎自己的数据库中。该公司计划在项目过程中进行几次形成性评估，并在每年年底与一个控制和实验小组一起进行两项试点研究。结果将使他们能够确定持续的、及时的支持在课堂内外提高教师技能和信心的有效性。

This project proposes to develop an innovative approach to improve and sustain math educators' problem solving teaching skills. Despite the expectations placed on math teachers by the Common Core State Standards, most are insufficiently prepared to teach students how to become critical thinkers. Much of this problem is due to limited pedagogical skills of teachers in providing adequate problem solving instruction and supports on top of teachers' own limited problem solving skills. This project remedies this with its integrated modules and powerful analytics engine that suggests learning pathways for both expert and novice teachers. They anchor their research in National Council of Teacher's of Mathematics Principles to Action. It will help teachers develop confidence and skills in planning and evaluating their lessons, as well as understanding student misconceptions and intervening in a timely manner. Teachers who approach problem solving with confidence inspire students to approach difficult math tasks the same way. This has great implications for how many students will continue to enroll in Science, Technology, Engineering and Math programs. In addition, the project sets the stage for educators to develop 21st Century skills including critical thinking, communication and collaboration - essential job skills for the young minds they mentor.This effort refines and scales up their product, which is a web and mobile application that works seamlessly in conjunction with our current student-facing platform, to provide teachers with timely supports for improving students' problem-solving skills and math communication. This project will deliver professional development continuously and in-context using virtual peers, rich rubrics, interactive tools and actionable data. The analytics engine leverages adaptive learning models in order to build robust modules. The Data Collector Layer will contain interfaces for users to get recommendations, receive user feedback and provide other analysis reports. The Analytics Core Layer will be implemented using a collection of machine learning algorithms. The Service Layer will calculate recommendations based on user profile, user feedback, pre-stored best practices and other use cases. The Persistence Layer will store and get calculated data to recommendation engine's own database. The company plans to conduct several formative evaluations during the course of the project, as well as two pilot studies at the end of each year with a control and experiment group. The results will enable them to determine the effectiveness of ongoing, just-in-time supports for improving teachers' skills and confidence inside and outside the classroom.