Enhancing Materials Science and Engineering Curricula through Computation
Enhancing Materials Science and Engineering Curricula through Computation
批准号:
0633180
负责人:
John Kieffer
金额:
$15.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-01-01 至 2010-12-31
中文摘要
材料科学家和工程师所需要的技能越来越多样化,这对教育者来说是一个巨大的挑战,因为他们必须在本科学习的过程中传授广泛的知识基础。通过在材料科学与工程(MSE)本科课程中增加计算内容和信息技术的使用,该项目团队正在设计一个更有效的教学过程,并改善材料工程师和科学家的准备工作。该方法包括:(i)使用计算来增强认知过程;(ii)加强MSE学生的数学和计算能力;(三)利用计算机教学加速信息传递。在这个概念验证项目中,目标是材料热力学的核心MSE课程。一个由攻读计算材料研究的博士生、课程讲师和来自该机构学习与教学研究中心的经验丰富的评估人员组成的团队正在开发和实施计算学习模块,旨在通过(i)可视化地呈现MSE中的基本概念来增强学生的学习;(ii)积极让学生参与以电脑为基础的实验;(三)将学生的注意力集中在科学计算中的算法思维和概念上。这些新技术的影响和有效性是通过定性和定量评估来衡量的,包括来自测验、调查和访谈的反馈。
英文摘要
Engineering - Materials Science (57)The increasingly diverse skill set required of materials scientists and engineers presents a formidable challenge for educators as they must impart a broad knowledge base within the span of an undergraduate experience. By increasing the computational content and the use of information technology throughout the Materials Science and Engineering (MSE) undergraduate curriculum, this project team is devising a more effective instructional process and improving the preparedness of materials engineers and scientists. The approach involves: (i) using computation to enhance the cognitive process; (ii) strengthening the mathematical and computational proficiency of MSE students; and (iii) using computer-based instruction to accelerate information transfer. In this proof-of-concept project, the core MSE course of thermodynamics of materials is targeted. A team consisting of a Ph.D. student pursuing computational materials research, the course instructor, and an experienced evaluator from the institution's Center for Research in Learning and Teaching are developing and implementing computational learning modules designed to enhance student learning by (i) visually presenting fundamental concepts in MSE; (ii) actively engaging students in computer-based experimentation; and (iii) focusing student attention on algorithmic thinking and concepts in scientific computation. The impact and effectiveness of these new techniques are measured through qualitative and quantitative evaluation, including feedback from quizzes, surveys and interviews.
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