A Study on the Impact of a Statics Sketch-Based Tutoring System Through a Truss Design Problem

Josh Hurt, Matthew Runyon, T. Hammond, J. Linsey
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引用次数: 1

Abstract

Providing opportunities for students to exercise their creative skills in large, entry engineering classes challenges most faculty. This paper presents a study of a large statics class provided with a homework problem that asks them to design a truss structure. Automatic grading was done by Mechanix, an AI tutor-based software package that can automatically recognize a free-body diagram or a planar, 2d, statically determinate truss structure. The paper presents a study done in two different semesters, comparing the students using Mechanix to a control (problem on paper). To ease grading, the control group's trusses were analyzed by Mechanix after submission. No mean homework grade differences were observed, but students in the Mechanix group produced trusses that could withstand higher loads. This is despite the fact the only guidance or feedback Mechanix provides was if the students' calculated max load was correct, and if it was not, which member failed. This study occurred in Fall 2019 and Spring 2020. Students also submitted more attempts in Mechanix than the control. It may be students in the control group only submitted correct answers despite being asked to submit all attempts. Future work will provide more incentive for students to submit all attempts on paper. Mechanix automatically records all attempts. During high stress (Covid-19), more students in the Mechanix group submitted the assignment indicating that students may find this system less mentally taxing to use, less stressful, or something else led to this difference. It will be explored with focus groups in the future. AI tools have the potential to provide automatic grading for open-ended, creativity required, design problems, and to engage students more, allowing universities to develop more innovative engineers while also deepening their knowledge.
通过桁架设计问题研究基于静力学草图的教学系统的影响
在大型入门级工程课程中为学生提供锻炼创造性技能的机会,对大多数教师来说都是一项挑战。本文以某大型静力学班级为例,对布置桁架结构的作业进行了研究。自动分级由Mechanix完成,这是一个基于AI教程的软件包,可以自动识别自由体图或平面二维静定桁架结构。本文介绍了在两个不同的学期中进行的一项研究,将使用Mechanix的学生与对照(纸上的问题)进行比较。为了便于分级,对照组提交后用Mechanix对桁架进行分析。没有观察到平均作业成绩的差异,但Mechanix组的学生制作的桁架可以承受更高的载荷。尽管Mechanix提供的唯一指导或反馈是学生计算的最大负载是否正确,如果不正确,哪个成员失败了。这项研究发生在2019年秋季和2020年春季。与对照组相比,学生们在Mechanix上提交了更多的尝试。可能是控制组的学生尽管被要求提交所有尝试,但只提交了正确的答案。未来的工作将提供更多的激励学生提交所有的书面尝试。Mechanix自动记录所有尝试。在高压力(Covid-19)期间,Mechanix组中更多的学生提交了作业,这表明学生可能会发现这个系统使用起来不那么费力,压力更小,或者是其他原因导致了这种差异。未来将与焦点小组探讨这一问题。人工智能工具有潜力为开放式、需要创造力的设计问题提供自动评分,并更多地吸引学生,使大学能够培养更多创新的工程师,同时加深他们的知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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