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CAREER: Intelligent Representations: How to Blend Physical and Virtual Representations by Adapting to the Individual Student's Needs in Real Time

职业：智能表示：如何通过实时适应个别学生的需求来融合物理和虚拟表示

基本信息

批准号：
1651781
负责人：
Martina Rau
金额：
$ 59.84万
依托单位：
University of Wisconsin-Madison
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：
2017-07-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651781&HistoricalAwards=false
关键词：
CAREER Intelligent Representations How Blend

项目摘要

Adaptive educational technologies can much improve students' learning in science, technology, engineering, and mathematics (STEM) domains. A particular strength of these technologies is that they can provide interactive models that visualize complex concepts. Educational technologies typically use virtual models that students manipulate via mouse or keyboard. Yet, physical models that students manipulate with their hands can be more intuitive because they relate abstract concepts to students? bodily experiences in the real world. This CAREER project examines how best to integrate physical models into adaptive educational technologies. The project will focus on a domain where models play a crucial role in instruction: undergraduate chemistry. A series of experiments at 2-year and 4-year colleges will test whether physical or virtual models are most effective for particular concepts, in which order to present them, and how to help students make connections among them. Further, the team will develop a technology that can assess how students manipulate physical models. Results will be consolidated in a comprehensive theory of how physical and virtual models affect student learning. The results will help instructors select the best model for their students. The team will build on the results to develop an educational technology that adaptively selects physical and virtual models that is most helpful to the individual student given his/her learning progress. This research will yield a new type of educational technologies that blend physical and virtual models and that can adapt to individual students' bodily interactions. Such technologies can make STEM concepts more accessible to students with diverse backgrounds. Further, by involving students and instructors from 2-year colleges who often lack access to technology innovations, the project will broaden participation and enhance socioeconomic equality in STEM.Students' difficulties in thinking in terms of visual representations jeopardize their learning in science, technology, engineering, and math (STEM) domains such as chemistry. Physical (tangible) and virtual representation modes have complementary benefits for students' learning. Yet, there is no comprehensive theory of how representation modes complement one another when students learn abstract content. The goal of this CAREER project is to develop such theory. Because effective combinations of physical and virtual representations are concept-, action-, and student-specific, they are likely too complex for instructors to achieve without support. Educational technologies can offer such support, but they cannot interface with physical representations. Hence, another goal of this CAREER project is to translate theory about representation modes into adaptive educational technologies that intelligently blend physical and virtual representations. A series of experiments in 2- and 4-year college chemistry will investigate how physical and virtual representations complement one another, how best to sequence them, and how best to help students make connections among them. User studies with instructors and students will investigate how educational technology design can address educational needs and systemic constraints in real learning contexts and how to support instructors in combining representation modes effectively. Further project activities will expand the intelligent blending framework to STEM domains in K-12 contexts. The project will contribute new theory about how physical and virtual representations complement one another. It will yield practical recommendations for the design of educational technologies that adapt to individual students' body-based interactions. A concrete deliverable will be an adaptive educational technology for 2- and 4-year college chemistry, available for free and disseminated to diverse populations. By including 2-year-colleges and by making this research applicable to STEM education in K-16 contexts, this project may broaden participation and enhance socioeconomic equality in many STEM domains.

自适应教育技术可以大大改善学生在科学、技术、工程和数学（STEM）领域的学习。这些技术的一个特殊优势是，它们可以提供可视化复杂概念的交互式模型。教育技术通常使用学生通过鼠标或键盘操纵的虚拟模型。然而，学生用手操作的物理模型可以更直观，因为它们将抽象概念与学生联系起来？在真实的世界中的身体体验。这个CAREER项目研究如何最好地将物理模型集成到自适应教育技术中。该项目将集中在模型在教学中发挥关键作用的领域：本科化学。在两年制和四年制大学进行的一系列实验将测试物理或虚拟模型是否对特定概念最有效，以何种顺序呈现它们，以及如何帮助学生在它们之间建立联系。此外，该团队将开发一种技术，可以评估学生如何操纵物理模型。结果将在物理和虚拟模型如何影响学生学习的综合理论中得到巩固。结果将帮助教师为学生选择最佳模型。该团队将在研究结果的基础上开发一种教育技术，该技术可以自适应地选择物理和虚拟模型，这些模型对学生的学习进度最有帮助。这项研究将产生一种新型的教育技术，融合了物理和虚拟模型，可以适应个别学生的身体互动。这些技术可以使不同背景的学生更容易获得STEM概念。此外，该项目还将通过让通常无法接触到技术创新的两年制大学的学生和教师参与进来，扩大STEM领域的参与度，提高STEM领域的社会经济平等性。学生们在视觉表征方面的思维困难会危及他们在化学等科学、技术、工程和数学（STEM）领域的学习。物理（有形）和虚拟表征模式对学生的学习有互补的好处。然而，当学生学习抽象内容时，没有全面的理论来解释表征模式如何相互补充。这个职业项目的目标是发展这样的理论。由于物理和虚拟表示的有效组合是概念，动作和学生特定的，他们可能太复杂，教师实现没有支持。教育技术可以提供这样的支持，但它们不能与物理表示接口。因此，这个CAREER项目的另一个目标是将有关表征模式的理论转化为自适应教育技术，智能地融合物理和虚拟表征。在2年和4年的大学化学一系列实验将探讨物理和虚拟表示如何相互补充，如何最好地排序，以及如何最好地帮助学生建立它们之间的联系。教师和学生的用户研究将探讨教育技术设计如何解决教育需求和系统的限制，在真实的学习环境，以及如何支持教师有效地结合代表模式。进一步的项目活动将把智能混合框架扩展到K-12背景下的STEM领域。该项目将为物理和虚拟表示如何相互补充提供新的理论。它将产生实际的建议，教育技术的设计，适应个别学生的身体为基础的互动。一个具体的可交付成果将是2年和4年制大学化学的适应性教育技术，免费提供并向不同人群传播。通过包括2年制大学，并使这项研究适用于K-16背景下的STEM教育，该项目可以扩大参与，提高许多STEM领域的社会经济平等。