on Human Factors in Computing Systems, 2011, vol. 66, pp. 2425–2428. doi: 10.1145/1979742.1979575.[11] D. Rothman, “A Tsunami of Learners Called Generation Z.,” 2016. http://docplayer.net/15163141-A-tsunami-of-learners-called-generation-z-by-darla-rothman- ph-d.html[12] L. Sun, “Enhancing Learning of Engineering Graphics Through Gamification,” presented at the 2020 ASEE Virtual Annual Conference Content Access, Jun. 2020. Accessed: Mar. 08, 2021. [Online]. Available: https://peer.asee.org/enhancing-learning-of-engineering- graphics-through-gamification[13] B. McCoy, “Digital Distractions in the Classroom: Student Classroom Use of Digital Devices for Non-Class Related Purposes,” 2013, Accessed: Mar. 06, 2021. [Online
). Spatial ability through engineering graphics education. International Journal Of Technology & Design Education, 23(3), 703-715. Page 24.982.78. Branoff, T. J. (2000). Spatial visualization measurement: A modification of the Purdue Spatial Visualization Test -Visualization of Rotations. Engineering Design Graphics Journal, 64(2), 14-22.9. Guay, R. (1977). Purdue Spatial Visualization Test: Visualization of Rotations. W. Lafayette, IN. Purdue Research Foundation.10. Bodner, M. G., & Guay, R. B. (1997). The Purdue Visualization of Rotations Test. The Chemical Educator, 2(4), 1-17.11. Howell, D. C. (2013). Statistical Methods
strategy (J2), which contained 5sketched features, 4 copy features, one hole and one edge feature. Two additional parts weremodeled to determine whether the feature types would affect the complexity index calculations.Part A, shown in Figure 6, was modeled using only extrusions for A1, and a combination of Page 24.1093.6revolve and extrude features for A2. Part B, shown in Figure 7, was modeled using onlyextrusions for B1 but included a blend feature for B2. Results of the complexity calculations forparts modeled with these alternative strategies are shown in Table 1. Note that parts K and J weremodeled using the same collection of features for the
Topics/Lessons FrequencyCOUNT RANKING Figure 4. Additional SOLIDWORKS Functionality/Tools Frequency and RankingsClassroom Activities and Outcomes Survey Table 4. Course Related Skill Gains Factor Mean Std. Deviation Design Skills a. Understanding of what engineers “do” in industry or as faculty 3.30 0.64 members b. Understanding of engineering as a field that often involves non- technical considerations (e.g., economic, political, ethical, 2.90 0.94 and/or social issues) c
test might perhaps help in understanding this relation better. For future research, it would be interesting to see the rate of improvement of spatial visualization skills those classified as having low spatial ability would have at the end of the semester after having been exposed to the formal training; while observing which learning style group makes the most gain. References1. Connolly, P. (2009). Spatial ability improvement and curriculum content. Engineering Design Graphics Journal, 73(1).2. Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. New York: Cambridge University Press.3. McGee, M. G. (1979). Human spatial abilities: Psychometric studies and
files of a correctly designed model that reflects what students are expected to submit.Within each rubric, instructors can specify which features to grade: volume, material, compositeshape score, center of mass, and check for fully defined sketches (see Appendix B for moredetail). However, similar to the rubrics used by the graduate student graders, GW rubrics do notattempt to measure design intent in any manner. Although GW was used to grade the bi-weeklylabs, it was not used to grade the weekly homework assignments because it was not certain GWcould replicate the same level of feedback performed by the TAs.Research ObjectiveIn general, computational tools for grading 3D CAD files do have many advantages however it isnot clear how they
gathering data to support innovation in thedelivery of instruction. Efforts will also be made to gather control group data.References 1 Bairaktarova, Diana, Matthew Reyes, Nooshin Nassr, and Dan Thomas Carlton, “Identifying Motivational Factors and Lived Experiences that Enhance Spatial Skills in Novices and Experts in STEM Disciplines,” American Society for Engineering Education, 2015. 2 Metz, Susan Staffin, Susan Donohue, and Cherith Moore. (2012) “Spatial Skills: A Focus on Gender and Engineering” In B. Bogue & E. Cady (Eds.), Apply Research to Practice (ARP) Resources. Retrieved January 31, 2017 from http://www.engr.psu.edu/AWE/ARPResources.aspx 3 Segil, Jacob L
) explains this seemingly contradictory result. Figure 3 shows a large number of studentspassed with a score in the 21-23 range in their first year, while a large number of students scoredin the 24-30 range in their final year. This result leads to a higher average test score in the finalyear, despite the greater number of students who failed the test in the final year. (a) First and final year test scores (b) Difference in entrance/exit scores Figure 1. Box plots of test scores, taken in the first year and final year of an engineering degree program. First Year 87.5% 12.5% Final Year
score amaximum of 30 on the test. Mean confidence levels to the following questions were compared Page 22.1314.3for students in each quartile. 1) Currently, how confident are you that engineering is the right career for you? 2) How confident are you that your current major in engineering is right for you?The five Likert-scale responses the students could choose were: a) Completely confident b) Very confident c) Moderately confident d) Slightly confident e) Not at all confident.The responses were rated on a scale of 1 to 5 with 5 corresponding to the “Completelyconfident” response, and 1 corresponded
: Examining practicing professionals. Engineering Design Graphics Journal, 68(2), 14-26.17. Hartman, N. W. (2009). Defining expertise in the use of constraint-based CAD tools by examining practicing professionals. Engineering Design Graphics Journal, 69(1), 6-15.18. Peng, X., McGary, P., Johnson, M., Yalvac, B., & Ozturk, E. (2012). Assessing novice CAD model creation and alteration. Computer-Aided Design & Applications, PACE, (2), 9-19.19. Rynne, A., Gaughran, W. F., & Seery, N. (2010). Defining the variables that contribute to developing 3D CAD modelling expertise. In E. Norman & N. Seery (Eds.), Graphicacy and Modelling. The International Conference on Design and Technology Educational Research and Curriculum
Science Education, 2009. 31(3): p. 459-480.3. Barr, R.E., Planning the EDG curriculum for the 21st century: A proposed team e ort. Engineering Design Graphics Journal, 1999. 63(2): p. 9.4. Sorby, S.A., & Baartmans, B. J. . A longitudinal study of a pre-graphics course designed to improve the 3-D spatial skills of low visualizers. in Proceedings of the 8th International Conference on Engineering Design Graphics and Descriptive Geometry. 1998. Austin, TX.5. Sorby, S.A., A New And Improved Course For Developing Spatial Visualization Skills. 2001, ASEE Conferences: Albuquerque, New Mexico.6. Sorby, S.A., The role of spatial training in improving spatial and calculus performance in engineering students. Learning and
. Journal of Experimental Psychology, 44, 288-291.Cohen, L., Manion, L., Morrison, K. 2007. Research Methods in Education, London, Routledge.Dreyfus, H. L., Dreyfus, S.E. 1986. Mind over machine: The power of human intuition and expertise in the era of the computer, New York, The Free Press.Hope, G. 2008. Thinking and Learning through Drawing, London, Sage Publications Limited.Kavakli, M., Suwa, M., Gero, J., Purcell, T. 1999. Sketching interpretation in novice and expert designers In: GERO, J. S., TVERSKY, B. (ed.) Visual Reasoning in Design. Sydney: Key Centre of Design Computing and Cognition, University of Sydney.Lane, D., Seery, N. 2010a. Freehand sketching as a catalyst for developing concept driven competencies
; Exposition, 2005.9. E. Towle, J. Mann, and B. Kinsey “Work In Progress – Development of Tools to Improve the SpatialAbility of Engineering Students”, 35th ASEE/IEEE Frontiers in Education Conference, October 19 – 22,2005, Indianapolis, IN10. J. L. Mohler, “Using interactive multimedia technologies to improve student understanding ofspatially-dependent engineering concepts “, The Proceedings of the International Graphicon 2001conference on Computer Geometry and Graphics, Nyzhny Novgorod, Russia, 2001.11. A. Rafi, K. Anuar, A. Samad, M. Hayati, M. Mahadzir, “Improving spatial ability using a Web-basedVirtual Environment (WbVE)”, Automation in Construction 14 (2005) 707– 71512. S. A. Sorby and B. J. Baartmans, “The Development and Assessment of a
minutes long and after this time their attention begins to dropdramatically. Breaking up the lecture can refresh their mind and help to keep them engaged3.PollEverywhere.com, an online real time service for classroom response, was adopted due to itssimple web interface and instant feedback analysis. Figure 1 (a) shows a snapshot of the concepttest question on a power point slide and Figure 1 (b) demonstrates the corresponding studentresponses on PollEverywhere.com. Page 24.728.3 20 18 16 14
help providevocational-technical education programs and services to youth and adults in middle school, highschool and college level " (Wileman, 14).Since the early 1980s there has been very little research to use when selecting specific types ofvisuals that will be most effective and efficient in facilitating student achievement of designatedlearning objectives. What is urgently needed is systematic research efforts focused on three basicareas designed to provide data on: (a) what specific individual difference variables in learnersactually make a difference in student achievement in the teaching learning process, (b) which ofthese individual difference variables interact significantly with different kinds of visualizationused to complement
three shown in Figure 2. This includes using the surface model of a product as a starting point to model a tool such as the calculator face mold shown in Figure 2(c). For composites design, modeling and analysis, a simple springboard is used. Though obviously trivial from a modeling perspective, the challenge students face is in redistributing material from a constant thickness design to meet a stiffness constraint with the objective of weight reduction. The Grid Design Method provided by the Composites Design App allows varying material thicknesses and ply sequences to be specified over a grid laid out on the surface (see Figure 3(a)) which is optimized into a ply lay-up (Figure 3(b)). The model is simple enough that it
. Figure-2(a) Participant solving a problem by sketching Figure-2(b) Participant folding up the cardboard model The data collected included the final isometric sketch, the transcripts generated from therecorded audio/video while sketching and manipulating cardboard, and the interview. These datawere analyzed to understand patterns in problem solving adopted by the participants. Moreover,the primary goal was to understand the differences in the visualization process employed by eachindividual. Results After testing eight participants in the study, three essential strategies were observed based ondifferent strategies employed by the
completed.Whiteboard Learning Modules:The whiteboarding learning modules focused on ideation, modeling strategies, problem-solvingstrategies. These techniques provided sketching practice prior to learning the CAD learningoutcomes. For instance, during basic drafting and design and engineering drawing principles,students were asked to freehand sketch an isometric, front, top, and side view of a simple shape,as presented in Figure 2 below. In addition, during this portion of the course, they used freehandsketching on whiteboards to learn sketching views through different axonometric projections, aswell as proportions and dimensioning for manufacturing.Figure 2: a) Sample question and b) student response to support engineering drafting and design.For the
prove to be more definitive as the spring scale components in theassembly project had several curved surfaces. It is likely more difficult for novices to properlydimension multiple curved features in comparison to dimensioning multiple linear features of anobject.References1 Fisher, B. R. (2013) Geometric Dimensioning and Tolerancing Visual Glossary with GD&T At-A-Glance Sheets.Sherwood, OR: Advanced Dimensional Management LLC.2Gay, D. & Gamebelin, J. (2013). Modeling and Dimensioning of Structures: An Introduction. Hoboken, NJ:Wiley-ISTE.3 Sriraman, V. & DeLeon, J. (1999). Teaching Geometric Dimensioning and Tolerancing in a Manufacturing
Page 12.1420.9of previous experience, improved their test performance and gained new information from thecollege-level engineering graphics course.Bibliography1. Guay, R. B., Purdue Spatial Visualization Test: Rotations, Purdue Research Foundation, West Lafayette, IN,1977.2. Vandenburg, S. G., and Kuse, A. R., “Mental Rotations, a Group Test of Three-Dimensional SpatialVisualization”, Perceptual and Motor Skills, 47, 1978.3. Yue, Jianping, “Spatial Visualization Skills at Various Educational Levels,” Proceedings of the 2002 AmericanSociety for Engineering Education Annual Conference & Exposition, 2002.4. Sorby, Sheryl A. and Young, Michael F., “Assessment of a Visualization-Based Placement Exam for aFreshman Graphics Course,” Proceedings
Graphics (Graphicon 2001). Nyzhny Novgorod, Russia 292–300.20. Connolly, P., & Maicher, K. (2005). The Developing and Testing of an Interactive Web-based Tutorial for Ortographic Drawing Instruction and Visualization Enhancement. Proceedings of the 2005 ASEE Annual conference & Exposition. Portland, Oregon.21. Barr, R. (1999). Planning the EDG Curriculum for the 21st Century: A Proposed Team Effort. Engineering Design Journal, 63(2), 4-12.22. Sorby, S., Wysocki, A., & Baartmans, B. (2003). Introduction to 3D Spatial Visualization: an active approach. Clifton Park, NY: Thomson Delmar Learning.23. Rafi, A., Samsudin, K., & Ismail, A. (2006). On improving spatial ability through computer-mediated Engineering Drawing
. Mohler, J.L. (2008). A review of spatial ability research. Engineering Design Graphics Journal, 72(2), Retrieved from http://www.edgj.org/index.php/EDGJ/article/view/4921. Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge; New York: Cambridge University Press.22. McGee, M. G. (1979). Human spatial abilities: Sources of sexual differences. New York: Praeger Publishers.23. McArthur, J.M., & Wellner, K.L. (1996). Reexamining spatial ability within a piagetian framework. Journal of Research in Science Teaching, 33(10), 1065-1082.24. Baenninger, M., & Newcombe, N. (1989). The role of experience in spatial test performance: A meta-analysis. Sex Roles, 20, 327-344.25. Deno, J.A. (1995
engineering students. Unpublished doctoral dissertation, Purdue University. 13 Branoff, T. (1998). The effects of adding coordinate axes to a mental rotations task in measuring spatial visualization ability in introductory undergraduate technical graphics courses. Engineering Design Graphics Journal, 62(2), 16-34. 14 Ary, D., & Jacobs, L.C. (1976). Introduction to statistics: Purposes and procedures. Orlando, FL: Holt, Rinehart, and Winston. 15 Best, J.W., & Kahn, J.V. (1993). Research in education. 7th Edition. Needham Heights, MA. Allyn and Bacon. 16 Harnisch, D.L., Polzin, J.R., Brunsting, J., Camasta, S., Pfister, H., Mueller, B., Frees, K., Gabric, K., Shope. R.J. (2002). Using visualization to make
be administered again at the conclusion of thecourse, to see if spatial skills are improved through other course activities, making the additionaltreatment modules unnecessary for this class. Finally, it is recommended that the study bereplicated with more balance in participant numbers in the treatment and control groups, andmore balance in pretest measured spatial ability between the control and treatment groups. Page 14.868.8 Bibliographic Information1. Sorby, S., Wysocki, A. F., & Baartmans, B. (2003). Introduction to 3D Visualization: An Active Approach. CD- ROM with workbook. Clifton
control of their own learning. Page 14.833.11 Figure 7. GC120 within the Moodle Learning Management System.Bibliography1. Marsh, G. E., McFadden, A. C., & Price, B. J. (2003). Blended instruction: Adapting conventional instruction for large classes. Online Journal of Distance Learning Administration, 6(4). Retrieved November 7, 2008 from, http://www.westga.edu/~distance/ojdla/winter64/marsh64.htm2. Graham, C. R. (2005). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk & C. R. Graham (Eds.), Handbook of blended learning: Global perspectives, local designs. (pp. 3-21). San
midterm. Similar trends are seen in the final examand final course score. While spatial visualization ability seems to have the largest influence ontest scores and the class performance as a whole, it appears that gender modifies the effect ofspatial visualization, such that male and female students with the same visualization ability maynot have the same experience on exams.a) b)Figure 2. Interaction plots showing influence of gender and spatial visualization ability on (a)midterm standardized score and (b) homework hours.Figure 2b shows that spatial visualization also appears to have some effect on the averagenumber of homework hours reported by students with different spatial visualization
workshop (possiblydue to an iceberg effect, which is discussed in the Findings section below). 5 4 3 2 1 0 Lesscomfortable Nochange MorecomfortableFigure 6. Phase I: Change in Rhino Comfort Level after Training in Modules 5 and 6; N = 9.Phase II testing was carried out for college-targeting high-school students who participated in aone-week-long summer design immersion workshop hosted by Rensselaer. These studentsranged in age from 15 to 18 and worked on open-ended design projects in groups of 3 to 4members. Group CAD output was analyzed according to a rubric designed by Krauss thatassessed command, strategic, and epistemic CAD knowledge as demonstrated by each team’sfinalized CAD models. (See Appendix B for the
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Design Graphics Journal, 62(2), 16-34. 5. Bodner, G. M. and Guay, R. B. (1997). The Purdue visualization of rotations test. The Chemical Educator. 2 (4), 38. 6. Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53, 801-813. 7. Duncan, T.G. & McKeachie, W.J. (2005). The making of the Motivated Strategies for Learning Questionnaire. Educational Psychologist. 40(2), 117-128. 8. Sheskin, D.J. (2007). Handbook of Parametric and Nonparametric Statistical Procedures. (4th ed.) Washington, DC: Chapman and Hall/CRC
visualization test: Rotations. West Lafayette, IN, Purdue Research Foundation, 1977.2. S. Sorby, A. Wysocki, and B. Baartmans, Introduction to 3D Spatial Visualization: An Active Approach, Clifton Park, New York: Thomson Delmar Learning, 2003. Workbook by Sorby and software by Wysocki.3. Personal communication with Professor Beverly Baartmans, retired, Department of Education, Michigan Technological University, Houghton, MI. October 22, 2007.4. CEEB, Special Aptitude Test in Spatial Relations, Developed by the College Entrance Examination Board, USA, 1939. Page 13.696.9