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

SBIR 第二阶段：用于解决数学问题的嵌入式专业学习平台

• 批准号: 1660216
• 负责人: Sheela Sethuraman
• 金额: $ 74.73万
• 依托单位: CueThink
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1660216&HistoricalAwards=false
• 关键词: SBIR; Phase; II; embedded; context

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.

本计画旨在发展一种创新的教学方法，以提升和维持数学教育者的问题解决教学技巧。尽管共同核心国家标准对数学教师寄予厚望，但大多数人都没有做好充分的准备来教学生如何成为批判性思维者。这一问题主要是由于教师的教学技能有限，无法在教师自身有限的解决问题技能之上提供充分的解决问题的指导和支持。该项目通过其集成模块和强大的分析引擎来弥补这一点，为专家和新手教师提供学习途径。他们的研究锚在全国理事会教师的数学原则的行动。它将帮助教师培养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进行干预。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。这对有多少学生将继续参加科学，技术，工程和数学课程有很大的影响。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--这些技能是他们所指导的年轻人的基本工作技能。这一努力完善和扩大了他们的产品，这是一个网络和移动的应用程序，与我们目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。该项目将使用虚拟同行，丰富的规则，交互式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强大的模块。数据收集器层将包含供用户获取建议、接收用户反馈和提供其他分析报告的界面。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、预存储的最佳实践和其他用例计算建议。持久层将存储和获取计算数据到推荐引擎自己的数据库。该公司计划在项目期间进行几次形成性评估，并在每年年底与控制组和实验组进行两次试点研究。结果将使他们能够确定持续的，及时的支持，以提高教师的技能和信心在课堂内外的有效性。