Research Initiation: Computational Modeling in the Professional Formation of Materials Engineers (PFE: RIEF)

工程师需要能够使用强大的计算工具，以继续解决日益复杂的问题。对于材料科学与工程（MSE）领域尤其如此，在该领域，仿真工具对于开发和测试尖端新材料（如新金属合金，智能材料和复合材料）至关重要。培养未来工程师的大学已经做出回应，将这些信息纳入他们的课程。然而，一部分本科工程专业的学生在学习使用这些工具时遇到了特别的困难，并且在学习期间没有培养出必要的能力。一个可能的原因是，许多工程专业的学生，特别是材料科学和工程专业的学生，不认为他们有能力学习这些技能，或者不相信他们需要在职业生涯中使用这些技能。本研究将对坎贝尔大学和俄亥俄州州立大学两所大学的大量学生进行调查和访谈，以确定学生的学习动机是否存在显著差异，以及这种动机差异是否与学生的学习有关。通过更好地理解这种关系，工程教育工作者可以帮助开发计划或课程，这将提高学生在这个重要领域的学习。计算思维和技能是至关重要的，无论是工程专业的学生和实践工程师在21世纪世纪的成功。该研究将探讨学生学习计算技能的关键动机因素（如功效，期望值和效用值）在工程学科中的差异程度。来自一所大型公立大学（俄亥俄州州立大学）和一所小型私立大学（坎贝尔大学）的学生将在工程学第一年接受调查和采访，以确定他们的动机信念。不同类型的动机之间的联系，在工程领域内选择的专业，和学生的背景将进行调查。这些因素相互关联的程度以及学生在基本计算建模作业中的表现将决定假设是否得到支持，即工程研究的某些子学科（如材料科学和工程）的学生学习计算工具的积极性较低。最终，这些发现可以导致适当的和高影响力的干预措施，以改善计算材料科学教育。我们可能会发现，这是更容易解决的方式，学生认为学习计算技能的重要性，以及他们是否能够成功，如果是这样，学生的学习可能会产生显着的改善。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

Engineers need to be able to use powerful computational tools in order to continue to address increasingly complex problems. This is particularly true for the field of Materials Science and Engineering (MSE), in which simulation tools are vital to the effort to develop and test cutting-edge new materials like new metal alloys, smart materials, and composites. Universities who train future engineers have responded by including this information in their curriculum. However, a portion of undergraduate engineering students have had particular difficulty learning to use these tools and do not develop the competence necessary during their studies. One possible reason for this is that many engineering students, particularly materials science and engineering students, do not think they are capable of learning these skills or do not believe they will need to use them in their career. This project will survey and interview a large number of students at two different universities, Campbell University and Ohio State University, to determine whether or not there are significant differences in students’ motivation based on their program of study, and whether or not this difference in motivation is tied to student learning. By better understanding this relationship, engineering educators can help develop programs or curriculum that will improve students’ learning in this vital area.Computational thinking and skills are critical for the success of both engineering students and practicing engineers in the 21st century. The study will explore the extent to which key motivation factors for students learning computational skills – such as efficacy, expectancy value, and utility value – differ across engineering disciplines. Students from a large public university (Ohio State University) and a small private university (Campbell University) will be surveyed and interviewed during their first year of studies in engineering to determine their motivational beliefs. Links between different types of motivation, chosen specialty within the field of engineering, and student background will be investigated. The degree to which these factors correlate with each other and with students’ performance in basic computational modeling assignments will determine whether the hypothesis – that students in some sub-disciplines of engineering studies such as materials science and engineering will be less motivated to learn computational tools – is supported. Ultimately, these findings can lead to appropriate and high-impact interventions to improve computational materials science education. We may find that it is easier to address the way students think about the importance of learning computational skills and whether or not they are able to succeed, and if so dramatic improvements in student learning could result.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.