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

该项目建议开发一种创新方法来提高和维持数学教育者解决问题的教学技能。尽管共同核心州标准对数学教师寄予了期望，但大多数教师在教导学生如何成为批判性思考者方面准备不足。这个问题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，而教师自身解决问题的技能有限。该项目通过其集成模块和强大的分析引擎来解决这个问题，为专家和新手教师提供学习途径。他们的研究以全国数学教师行动原则委员会为基础。它将帮助教师增强规划和评估课程的信心和技能，以及了解学生的误解并及时干预。充满信心地解决问题的教师会激励学生以同样的方式完成困难的数学任务。这对于有多少学生将继续就读科学、技术、工程和数学课程具有重大影响。此外，该项目还为教育工作者培养 21 世纪的技能奠定了基础，包括批判性思维、沟通和协作，这是他们所指导的年轻人的基本工作技能。这项工作完善并扩大了他们的产品，该产品是一个网络和移动应用程序，可以与我们当前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能力和数学沟通能力。该项目将使用虚拟同行、丰富的标准、交互式工具和可操作的数据，在上下文中持续提供专业发展。分析引擎利用自适应学习模型来构建强大的模块。数据收集层将包含供用户获取建议、接收用户反馈并提供其他分析报告的接口。分析核心层将使用一系列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用例来计算推荐。持久层将计算数据存储并获取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多项形成性评估，并在每年年底与对照组和实验组进行两项试点研究。结果将使他们能够确定持续、及时的支持对于提高教师课堂内外的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.