SBIR Phase I: Learning to Think Mathematically and Problem-Solve Right From the Start

SBIR 第一阶段：从一开始就学习数学思考和解决问题

• 批准号: 1547913
• 负责人: Elizabeth Schwartz
• 金额: $ 14.99万
• 依托单位: Production Resource Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1547913&HistoricalAwards=false
• 关键词: SBIR; Phase; Learning; Think; Mathematically

ABSTRACT This SBIR Phase I project will produce the prototype for a supplementary K-2 math program, tablet delivered, that builds mathematical reasoning and problem-solving skills. For students to succeed in gate keeper courses that are prerequisite to high-paying jobs, reasoning and problem-solving skills are essential, yet the majority of schools focus strictly on computation. To address this nationwide problem, the pedagogy and strategies from the federally-funded Comprehensive School Mathematics Program (CSMP) will be appropriated. Studies (1970-1990) show that CSMP students had greater ability to solve novel problems as measured by the MANS test. Although results were significant from the gifted to the learning challenged, CSMP?s hands-on, personalized approach was too costly at the time to implement on a larger scale. This project will address harnessing today?s technology to leverage the strongest aspects of CSMP. Students will work through repeated exposures to problem-solving scenarios presented in game-like settings. Rather than traditional manipulatives, colorful toy building bricks are used to pose problems, explore concepts, develop skills, and define new ideas. As new generations are able to think mathematically, considerably more Americans will be able to thrive in the technologically sophisticated 21st century marketplace, fostering jobs and generating tax revenues.Employing powerful technologies devised exclusively for the tablet (touch, drag-and-drop, and accelerometers) makes possible the creation of detective-like adventures that encourage students to form hypotheses, consider alternatives, and test conclusions. Resplendent with pictorial and concrete representations, children are able to interpret and internalize mathematical concepts many think are beyond their comprehension. Replicas of popular childhood building bricks make the experience seem play-like rather than a series of tasks to be completed. By moving these familiar objects across the tablet screen, children see the cause and effect of their actions. Adaptive learning mechanics deliver auto-graded experiences based on performance -- saving considerable preparation and supervisory time. Automatically generated reports provide data for decision-making. Phase I research will focus on the viability of the technology, student engagement and teacher use. During Phase II, an extensive evaluation will determine if students internalize Common Core concepts and use their growing understanding of mathematics to solve problems. Traditional qualitative analysis techniques will be used to answer research questions and provide a foundation for continued studies. In schools where the supplementary program has been implemented as designed, the MANS test can be administered to ascertain if students have similar or better outcomes than the original CSMP cohort.

这个SBIR第一阶段的项目将产生一个补充K-2数学程序的原型，平板电脑交付，建立数学推理和解决问题的能力。对于学生来说，要想成功地完成作为高薪工作先决条件的守门人课程，推理和解决问题的能力是必不可少的，但大多数学校都严格关注计算。为了解决这个全国性的问题，将从联邦资助的综合学校数学方案（CSMP）的教学法和战略拨款。研究（1970-1990）表明，CSMP学生有更大的能力来解决新的问题，通过MANS测试测量。虽然结果是显着的天才学习挑战，CSMP？在当时，这种亲自动手的个性化方法成本太高，无法大规模实施。这个项目今天将解决治理？的技术，以利用CSMP的最强方面。学生将通过反复暴露在游戏般的设置中提出的解决问题的方案。与传统的操作方式不同，彩色玩具积木被用来提出问题，探索概念，发展技能和定义新想法。随着新一代人能够进行数学思考，更多的美国人将能够在技术先进的21世纪世纪市场中茁壮成长，创造就业机会和税收收入。使用专为平板电脑设计的强大技术（触摸、拖放和加速计），可以创造侦探般的冒险，鼓励学生形成假设，考虑替代方案，并检验结论。孩子们能够用图画和具体的表现来解释和内化许多人认为超出他们理解范围的数学概念。儿童时期流行的积木的复制品使这种体验看起来像是玩耍，而不是一系列需要完成的任务。通过在平板电脑屏幕上移动这些熟悉的物体，孩子们可以看到他们行为的因果关系。自适应学习机制提供基于表现的自动分级体验-节省大量准备和监督时间。自动生成的报告为决策提供数据。第一阶段的研究将集中在技术的可行性，学生参与和教师使用。在第二阶段，广泛的评估将确定学生是否内化共同核心概念，并利用他们对数学的不断增长的理解来解决问题。传统的定性分析技术将用于回答研究问题，并为继续研究提供基础。在补充计划已按设计实施的学校，可以进行MANS测试，以确定学生是否有类似或更好的结果比原来的CSMP队列。