Enhancing 3-D Spatial Skills Of Engineering Students Using Augmented .
Paper ID #28639Enhancing 3D spatial skills of engineering students using AugmentedRealityMr. Ali Sheharyar, Texas A&M University at QatarAli Sheharyar is the scientific visualization analyst at Texas A&M University at Qatar. Ali received hisB.S. degree in Computer Science from National University of Computer and Emerging Sciences in Pakistan in 2004 and his M.S. degree in Computing from Qatar University in 2015. Ali’s professional interestsinvolve scientific data visualization, 3d computer graphics, and applications of emerging technologies inteaching and research. Ali is currently working on virtual and augmented reality technologies to supportthe research and teaching applications.Prof. Arun R Srinivasa, Texas A&M UniversityDr Arun Srinivasa is the Holdredge/Paul Professor and associate department head of Mechanical Engineering at Texas A&M University and has been with TAMU since 1997. Prior to that he was a facultyat University of Pittsburgh. He received his undergraduate in mechanical Engineering from the IndianInstitute of Technology, Madras, India in 1986 and subsequently his PhD from University of California, Berkeley. His research interests include continuum mechanics and thermodynamics, simulations ofmaterials processing, and smart materials modeling and design. His teaching interests include the useof technology for education, especially in the area of engineering mechanics and in effective teachingmethodologies and their impact on student progress in mechanical engineering.Dr. Eyad MasadDr. Masad is a Professor in Mechanical Engineering at Texas A&M at Qatar. His main areas of expertiseare materials engineering, mechanics and teaching pedagogy.c American Society for Engineering Education, 2020
Enhancing 3D spatial skills of engineering students usingAugmented RealityAli Sheharyar, Eyad Masad, Arun Srinivasaali.sheharyar@qatar.tamu.edu, eyad.masad@qatar.tamu.edu, asrinivasa@tamu.eduTexas A&M University at Qatar, Doha, QatarAbstract3D spatial visualization and reasoning skills are of great importance for success in a number oftechnical fields. For engineering, the ability to examine, manipulate, rotate or twistthree-dimensional models in space is of particular interest. Students differ in their development ofspatial skills, and this difference affects their performance on some spatial tasks. Instructorsinvolved in teaching the statics and mechanics courses at Texas A&M University at Qatar noticedthat some students have challenges in comprehending three-dimensional (3D) technical figures asthey presented in engineering textbooks. Consequently, this challenge may lead to limitingin-class instruction and discussion of 3D problems, especially those with vectors representingdifferent physical forces.To help students in understanding these 3D representations and to enhance their spatial skills, weemployed the Augmented Reality (AR) technology that enables overlaying 3D figures onto thecorresponding 2D figures through a smartphone or a tablet. The students can freely explore the3D figure from various angles by either moving the book or the device itself. We converted somefigures in the textbook used in teaching the Statics subject, which is part of the MEEN 225Engineering Mechanics course at Texas A&M University. We shared the AR tool in theclassroom with students in class and asked them to solve a problem in both 2D (using only book)and 3D (using AR tool). In this paper, we demonstrate the AR tool and share our experience andthe assessment results.IntroductionSpatial ability or visuo-spatial ability is the capacity to comprehend, reason and remember thespatial relations among objects or space 1 . It is of value for the success in engineering and othertechnical fields. In engineering, for instance, engineers utilize spatial skills when designing partsof a machine; they must understand functions and interaction of the parts from multipleperspectives while integrating the parts among a variety of other components in an assembly 2 . It
is very common to find higher spatial ability in people working on engineering and architecturerelated activities 3 .Engineering students differ in their development of spatial skills, and this difference may affecttheir performance of some tasks. The development of spatial skills is critical for understandingand analyzing 3D technical drawings in mechanics problems. Students with limited spatial skillsusually struggle to comprehend these figures. For instance, Figure 1 is part of a problem thatrequires students to determine the largest magnitude of 3D force vector F that can be applied tothe bracket so that the bracket remains under equilibrium. In this figure, the way the angles alongthe three axes around vector F are annotated might be difficult for students to comprehend. As aresult of this limitation in spatial analysis of 3D figures, faculty members may limit the use ofthree-dimensional problems in the class.Figure 1: A sample problem from Statics text book 4While spatial ability is a skill that cannot be taught, it can be improved through training andpractice 3 . There are some studies in the engineering field that demonstrate the improvement inspatial skills by using multimedia training, video games, and 3D graphics software 5,6,7 . Hsi et al. 8used spatial strategy instruction in their teachings and found that their students made significantprogress in developing the spatial reasoning. Ha and Fang 9 identified two broad categories oftraining: tangible methods and virtual methods. Tangible training methods use physical objects,such as blocks, to scaffold the development of mental rotation 10 . In contrast, virtual methodsemploy computer-based techniques such as 3D animations and simulations, virtual reality andaugmented reality tools 11 .Project DescriptionIn this project, we employed a tangible training model using the augmented reality technology tosupport students in enhancing their spatial skills. Augmented reality is a technology that allowscomputer-generate imagery (text, images, 3D models etc.) to be overlaid onto a live video stream
of a real-world environment 12 . In AR, the real environment is extended with information andimagery coming in real-time from the mobile phone or tablet. The AR application monitors thecamera stream constantly to detect and track the marker (or target) image. As soon as it detectsthe marker image in real-world, it overlays the virtual imagery on the video stream. The virtualimagery stays visible and anchored to the position and matches the orientation of the markerimage until the marker disappears from the camera field of view.We created 3D models of nine problems in a textbook used in teaching the Statics subject, whichis part of the MEEN 225 Engineering Mechanics course at Texas A&M University. Weimplemented our solution using Augment 13 , which is an online platform enabling the creationand the management of the AR visualizations. It allows placing 3D models in space on themarker images. It also allows scaling the models in real time. We used existing 2D figures fromthe textbook as marker images. This allows students to launch the Augment application on theirmobile devices and point the camera at the textbook figure and experience the corresponding 3Drepresentation in real time.One of the issues we faced in this project was the development of 3D figures. The creation of 3Dfigures requires advanced skills in modeling tools. We involved a student worker from themechanical engineering senior class to develop 3D models using SolidWorks R 14 and he usedAutodesk 3D Studio Max R 15 to incorporate annotations. 3DS Max R is a full-featured 3Dmodeling and animation software and making the 3D models from scratch in it would have been adaunting task. Therefore, we decided to model the bulk of work in SolidWorks R and import theresults into the 3DS Max R only to include the annotations. We applied the colors to the differentsub-parts to match it with the book.AssessmentWe converted nine (9) figures from the Statics textbook (Engineering Mechanics Statics by R. C.Hibbeler 4 ) to 3D. The class of 12 students was divided into two groups. One group was asked tosolve problem in Figure 1 in 2D using only book and the problem in Figure 2 in 3D using the ARapp. The order of solving the problems was switched for the other group.We conducted the self-efficacy test before and after this exercise to determine if AR improves thestudents’ ability to solve the problems. Students were asked to indicated their confidence inconducting various tasks in a scale from 1 (strongly disagree) to 5 (strongly agree). The 14 tasksthat students were asked to respond to were:1. Finding the x, y and z components of forces given a picture of a 3-dimensional force systemwhere the direction of forces are indicated by Cartesian coordinates of a point.2. Finding the x and y components of forces given a picture of a 2-dimensional force systemand the magnitudes of the forces and angles that the forces make with the coordinate axes?3. Finding the x and y components of forces given a picture of a 2-dimensional force systemwhere the direction of forces are indicated by Cartesian coordinates of a point.4. Finding the x, y and z components of forces given a picture of a 3-dimensional force system
(a) Student scanning the problem4-63 from the textbook(b) 3D model overlaid on the 2D figure using AR.Figure 2: Demonstration of the AR tooland the magnitudes of the forces and the angles that the forces make with the coordinateaxes.5. Finding the unit vector along a line given a picture of the line in 3-dimensions with theangles that the line makes with the x, y and z axes.6. Finding the unit vector along a line given a picture of the line in 2-dimensions with theangle(s) that the line makes with the x and/or y axis.7. Finding the unit vector along a line given a picture of the line in 2- dimensions withthe,Cartesian coordinates of a point on the line.8. Finding the unit vector along a line given a picture of the line in 3-dimensions with theCartesian coordinates of a point on the line.9. Finding an expression for the force vector in cable in 2-dimensions, given a picture of acable attached to a body and the angle that the cable makes with the x and y-axis.10. Finding the force vector in cable in 2-dimensions, given a picture of a cable attached to abody and the coordinates of 2 points that the cable passes through.11. Finding the force vector in cable in 3-Dimensions, given a picture of a cable attached to abody and the coordinates of 2 points that the cable passes through.12. Finding the force vector in cable in 3-Dimensions, given a picture of a cable attached to abody and the angle that the cable makes with the x, y and z-axis.13. Visualizing the forces in top view, side view, and end view if the forces are shown as a 3-dpicture from the book.14. Creating a rough drawing or sketch of the forces in top view, side view, and end view if theforces are shown as a 3-d picture from the book.After the AR exercise, we also asked the students for feedback about engagement and usability of
the AR application and experience, see below for the list of questions. Using five-point Likertscale (1 strongly disagree; 2 disagree; 3 neutral; 4 agree; 5 strongly agree), students expressedtheir feedback.1. I felt interested in the activity.2. I found the activity confusing.3. I felt frustrated while participating in this activity.4. I lost my self in the experience.5. I was absorbed in the activity.6. Participating in the activity was mentally exhausting.7. This experience was worthwhile.8. This experience helped me gain a better understanding of force in 3 dimensions.9. I was more engaged than usual while using the AR app.10. Using the AR app during assignment was helpful.11. I would like to use the AR app again during assignments.12. The AR app gave me confidence about completing my task(s).13. The AR app improved my ability to visualize 3 dimensional problems better.14. I didn’t like the AR app.15. The AR app was difficult to use.Analysis of ResultsTable 1 reports the means and variances of the student responses of the self-efficacy survey beforeand after experiencing the AR application. To determine if there is a statistically significantdifference between the two means (before and after the experiment), we used the paired t-test 16 .The t-test is a common method to test the null hypothesis that there is no difference between themean of a sample and the population mean, and no difference between the means of twosamples 16 .The mean, variance, and t-test results are shown in Table 1. A p-value higher than 0.05 indicatesno significant difference between the means of the two samples. As can be seen, all the resultsshow that students did not indicate change in their confidence in conducting various tasks. Therecould be multiple reasons of this outcome. First, students might had difficulty in adopting the ARtechnology. It was possible that most of them might had experienced it for the very first time inthe classroom. It takes time to adjust to any new way of working that shifts teaching from a paperto a dramatic method. Second, the problems used during the assessment were somewhat complex.Third, the sample size (12 students) was very small to perform this assessment.
Question1234567891011121314Before ARMean 0.813.080.633.170.70After ARMean 1Table 1: Responses of Students to the Self-Efficacy TasksWe analyzed the engagement and usability feedback from students using machine learning. Wedivided the questions into three categories: 1) Activity engagement (Questions 1-7), 2) Activityoutcome (Questions 8-13), and 3) AR app usability (Questions 14-15). We performed the clusteranalysis on the activity engagement and outcome categories using the hierarchical clusteringalgorithm. The objective was to group the students with similar feedback.Figure 3 shows the heat map of the students’ feedback on the activity engagement questions –each row represents responses (from 1 to 5) to all questions (columns) by one student. On top ofthe figure, we show the hierarchical clustering dendrogram with respect to the questions. We canobserve that there are two clear clusters (marked as clusters 1 and 2 in the Figure 3). Cluster 1contains the questions 1, 5, and 7. Question 1 asked students if they found the activity to be“interesting” and question 5 asked if they were “absorbed” during the activity. Question 7 asked ifthe overall experience was “worthwhile”. Except for one student (student 2), all other studentsgave positive response.The response to the questions in the second cluster (Questions 2, 3, 4 and 6) was somewhatmixed. This cluster contained the questions that asked if the activity was exhausting or confusing,or if they were lost or frustrated during the activity. Majority of students agreed the activity to beexhausting and confusing. This outcome reinforces our observation mentioned in the self-efficacyresults earlier. Students found it somewhat difficult to adopt to a new way of learning. For the ARapp usability, the response to both questions 15 and 16 was positive (average 3.4 out of 5).
Figure 3: Heat map of feedback for students’ engagement in the activityWhen analyzing the results of the activity outcome survey (Questions 8-13), almost half (9 out of17) of students gave positive response to all questions. This is clear from the clustering results inFigure 4 in which we see three clusters. Cluster 1 shows the group of students who mostly agreedto all questions. Among the rest, 5 students disagreed and remaining 3 gave mostly neutralresponse.
Figure 4: Heat map of feedback for the activity outcomeConclusionsSpatial visualization and reasoning skills are of great significance for the success in engineeringand other technical fields. Engineering students differ in their development of spatial skills, andthis difference may affect their performance on certain tasks. To help students in enhancing theirspatial skills, we employed augmented reality technology in the classroom to present the 3Dmodel of the complex 2D figures from the textbook. We performed a small study in one of thecourses offered at Texas A&M University at Qatar. We conducted self-efficacy surveys before andafter giving students the AR application and also gathered their feedback on the engagement andusability aspects.The majority of students gave positive response in terms of being interested in AR andrecognizing its value. AR helped students to be more engaged in solving the problems. However,several students indicated that the activity was “exhausting” and they were “lost” during theactivity. The authors believe that this can be improved by streamlining the practice and trainingstudents in the use of the tool.The responses of students to the self-efficacy questions showed that there was no statisticaldifference in assisting students in understanding the problems. We plan to continue to use AR inthe same course. However, we will streamline the instructions on the tool before we introduce the3D problems in class. We also plan to use AR in more complex 3D problems in which its valuecan have more impact.
References[1] Jeffrey Buckley, Niall Seery, and Donal Canty. Investigating the use of spatial reasoning strategies in geometricproblem solving. International Journal of Technology and Design Education, 29(2):341–362, 2019.[2] T Tseng and M Yang. The role of spatial-visual skills in a project-based engineering design. Course, 2011.[3] Jorge Martı́n-Gutiérrez, Rosa E Navarro, and Montserrat Acosta González. Mixed reality for development ofspatial skills of first-year engineering students. In 2011 Frontiers in Education Conference (FIE), pages T2D–1.IEEE, 2011.[4] RC Hibbeler. Statics, 2017.[5] Sheryl A Sorby. Developing 3-d spatial visualization skills. Engineering Design Graphics Journal, 63(2), 2009.[6] Jia-Hau You, Tsung-Yen Chuang, and Wei-Fan Chen. Enhancing students’ spatial ability by implementing adigital game. In Proceedings of the 16th international conference on computers in education, Taipei, Taiwan.Citeseer, 2008.[7] James L Mohler. Using interactive multimedia technologies to improve student understanding ofspatially-dependent engineering concepts. In Proceedings of the GraphiCon, 2001.[8] Sherry Hsi, Marcia C Linn, and John E Bell. The role of spatial reasoning in engineering and the design ofspatial instruction. Journal of engineering education, 86(2):151–158, 1997.[9] Oai Ha and Ning Fang. Spatial ability in learning engineering mechanics: Critical review. Journal ofProfessional Issues in Engineering Education and Practice, 142(2):04015014, 2015.[10] Yi-Chen Chen, Hung-Lin Chi, Wei-Han Hung, and Shih-Chung Kang. Use of tangible and augmented realitymodels in engineering graphics courses. Journal of Professional Issues in Engineering Education & Practice,137(4):267–276, 2011.[11] John E. Bell, Cui Cheng, Hannah Klautke, William Cain, Daniel Joseph Freer, and Timothy J. Hinds. A studyof augmented reality for the development of spatial reasoning ability. In 2018 ASEE Annual Conference &Exposition, Salt Lake City, Utah, June 2018. https://peer.asee.org/29726.[12] Kangdon Lee. Augmented reality in education and training. TechTrends, 56(2):13–21, 2012.[13] 3d and augmented reality product visualization platform — augment. https://www.augment.com/.(Accessed on 01/20/2020).[14] 3d cad design software — solidworks. https://www.solidworks.com/. (Accessed on 02/04/2020).[15] 3ds max — 3d modeling, animation & rendering software — /overview. (Accessed on 02/04/2020).[16] Paul Watters and Sarah Boslaugh. Statistics in a nutshell. O’Reilly Media, Incorporated, 2008.
Enhancing 3D spatial skills of engineering students using Augmented Reality Ali Sheharyar, Eyad Masad, Arun Srinivasa ali.sheharyar@qatar.tamu.edu, eyad.masad@qatar.tamu.edu, asrinivasa@tamu.edu Texas A&M University at Qatar, Doha, Qatar Abstract 3D spatial visualization and reasoning skills are of great importance for success in a number of
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