time lag in bringingthese technologies to the classroom. The high costs of laboratory equipment and the short lifespan of the equipment hold back many engineering and technology programs from offeringcourses in these areas. To overcome the cost and constant need for upgrades, the authordesigned a course titled “Computerized Input-Output.” The course introduces students to theexciting world of interfacing sensors and devices to a computer and programming them toexhibit intelligence, while learning about sensors, measurements, programming, Graphical UserInterfaces (GUI), and other important skills that are desirable in the modern industry. A DataAcquisition (DAQ) box is used to interface the world to a computer via sensors and actuators. Alow
AC 2008-1930: INCORPORATING TABLET PORTABLE COMPUTERS INTO THECLASSROOMSofia Vidalis, Pennsylvania State University-HarrisburgJoseph Cecere, Pennsylvania State University-Harrisburg Page 13.735.1© American Society for Engineering Education, 2008 Incorporating Tablet Portable Computers into the ClassroomAbstractUniversities are constantly updating to keep up with changes in the student’s future profession.That is why Penn State Harrisburg’s engineering technology classrooms and laboratoriesemphasis is placed on integrating modern technology with practical experimentation. Thecomplexity of accomplishing various learning environments has become enormous. Therefore
material covered.This can be accomplished by reducing the amount of writing by the professor and copying by the students.Specifically, it is recognized that copying complex diagrams, computer programs, and lengthy equations andtext diverts both the professor and the student from the task of teaching and learning. Therefore, technologyshould be employed to eliminate this copying, while retaining the type of note taking that is vital to learning andthe retention of knowledge. Secondly, what is expected in the classroom should be changed by modi@ing the classroomenvironment. If every classroom has only a blackboard, it is not surprising that most instructors will use theblackboard. On the other hand, if every classroom is equipped with an
Efficient Use of Computational Tools in Machine Design Kyu-Jung Kim, Ph.D. & Amir Rezaei, Ph.D. College of Engineering California State Polytechnic University, Pomona, CAAbstract Machine design is a required course at junior year to learn essential skills for seniordesign projects. There is a great need for comprehensive and integrated software due to itscomplicate nature of the course materials. Such tools are expected to empower students to solvemore challenging open-ended and/or integrated design problems, and to conduct design projectsfor a more rewarding experience in machine design. The Mechanical Design Toolbox hasevolved over
, University of California, Davis Harry H. Cheng is a Professor in the Department of Mechanical and Aerospace Engineering, Graduate Group in Computer Science, and Graduate Group in Education at the University of California, Davis, where he is also the Director of the UC Davis Center for Integrated Computing and STEM Education (http://c-stem.ucdavis.edu) and Director of the Integration Engineering Laboratory. His current research includes developing computing and robotics technologies and integrate them into STEM education in both formal and informal settings for integrated learning. From 1989 to 1992, he was a Senior Engineer for robotic automation systems with the Research and Development Division, United Parcel Service
group of fourlearn (or re-learn) their groupmates’ names and hometown as well as answer a daily ice-breakerquestion (such as what is your favorite sandwich shop in the area? Do you have any pets and ifso what are they (or if not – did you ever want any)? [19] So by the end of the week, the studentswould hopefully know something about 3 other students in their class. By the end of 9 weeks,the students would have met every other student in the course.Exposing Students to Successful Role Models From Diverse BackgroundsWe took two approaches to expose students to successful role models from diverse backgroundswithin the field of computing. The first approach was to highlight one computing contributorfrom an under-represented background each week by
professional development training to high school teachers, with the goal to improvehigh school education related to computer/computing. A total of fourteen high school teachersattended the workshop. Through multiple theoretical, hands-on, and discussion sessions duringthe two-day workshop, the participating high school teachers learned about state-of-the-artcomputing knowledge and technology, obtained hands-on training on pedagogical tools, and hadextensive interactions with university educators to discuss how to inspire high school students(particularly those from minority groups) to choose majors related to computer/computing.Assessment was conducted primarily via a series of surveys before and after the workshop,which included both formative
Paper ID #19731Promoting Computational Thinking in children Using AppsMs. Hoda Ehsan, Purdue University, West Lafayette (College of Engineering) Hoda is a Ph.D. student in the School of Engineering Education, Purdue. She received her B.S. in me- chanical engineering in Iran, and obtained her M.S. in Childhood Education and New York teaching certification from City College of New York (CUNY-CCNY). She is now a graduate research assistant on STEM+C project. Her research interests include designing informal setting for engineering learning, and promoting engineering thinking in differently abled students in informal and formal
Excellence as an Associate Engineer conducting energy audits to helpcompanies reduce their energy consumption. He used his experience designing plumbing systems tocreate his lesson for the students. Summary of the Lesson Objectives: In this lesson, students learn about the different uses ofindustrial steel pipes used in plant design and how the process is supported by computer-aided designsoftware. Students design a building’s pipe layout with different kinds of constraints, e.g., differentkinds of rooms, pipes, and pipe contents. The example is based on actual layout used for Hixson’sKitchens of Sara Lee project. Materials: two worksheets: (1) Case File: Building Floor Plan, and (2) Case File: Rules andRouting Information; PowerPoint
, splines, numericalmethods), with programming as a means to an end (14, 18-20, 29)? Or should the coursebe designed to learn a specific computer language, such as MATLAB, as an example ofan engineering tool (5, 6, 10, 22, 26, 28)? Alternatively, the course could be structured toteach algorithmic thought processes (10, 14, 20, 31-34). No one way is best and anycomputing course should address all three to some extent. The implementation of acomputing course, however, does need to be tailored to the objectives and backgroundsof the students. For example, the lecture-homework-test progression may be excellent ataddressing an applied math objective, while short programming assignments may addressthe learning of syntax. Here we present a semester-long
such software packages can be used to improve power engineering education.The advantages and disadvantages of the use of symbolic computations in power engineeringcourses are also discussed. Lessons learned are included and feedback and suggestions fromother educators are welcomed.1. Introduction, Power Engineering Education Issues and ChallengesExcellence in engineering education comes from innovative teaching and effective instructionalmaterials, requiring often changing the traditional way of delivering engineering courses. In thetraditional teaching methods, lecturers offer course materials in a classroom where studentslisten, take notes, copy materials, execute homework and complete assignments. Quite oftenlecturers fail to transfer
opinion on particular question, which is quite common in most surveys. We also see someobvious errors made by students in completing these surveys. However overall we think theresults match our expectation well. In particular, from question 1 to question2, we see a Page 14.352.8significant number of students moved from Scale 1~3 to Scale 3~4, which means that thissoftware was indeed effective in helping students in learning computed tomographic imagingmethods. From question 3~5 results we also see that most of the students seem satisfied with theimplementation and usability of the software.Obviously this evaluation was our first attempt, and the
of Memphis. c American Society for Engineering Education, 2020 Self-Efficacy Study in Computing Among College FreshmenAbstractComputer Science (CS) is not introduced equitably across K-12 schools, yet it is increasingly anecessary skill regardless of vocational pathway. Co-curricular activities such as summer campshave become a popular way to introduce CS to K-12 students. Researchers at our institution,through partnerships with other educational institutions and practitioners, developed atransdisciplinary approach of teaching CS in K-12 informal learning environments. Building onpositive results in the K-12 informal learning environment, researchers are exploring theapplicability of the transdisciplinary
Session 3532 COMPUTER INTERFACES FOR TEACHING THE NINTENDO® GENERATION Thad B. Welch, Brian Jenkins Department of Electrical Engineering U.S. Naval Academy, MD Cameron H. G. Wright Department of Electrical Engineering U.S. Air Force Academy, CO1. IntroductionThe utilization of the computer in the classroom is well documented and continues to grow in bothavailability and capability. The number of papers, e.g. (1-3
, theybecome tedious and cumbersome when structures are made of many members or whencalculations have to be repeated during the design iterations. Under such circumstances, the useof commercial structural analysis software greatly simplifies calculations, gives freedom tochange loads and support conditions, and visualize deformations, internal forces, and stresses.This paper discusses the computer structural analysis learning modules that were developed andutilized in the three courses in our architectural technology program.IntroductionThe finite element analysis (FEA) is a powerful simulation method used for many applications inthe industry. Commercial FEA software can perform structural analysis of both discrete andcontinuous structures. Several
so that a deeper explorationof concepts and their connections may be enabled through more hands-on but flexible explorationfor sampling and analyses.Introduction The 21st century has been characterized by an explosion of knowledge and rapidtechnological advancement. The extent to which technology is integrated into engineering practicerequires new engineering graduates to be well-versed with computer tools. Meanwhile, anincreased demand for remote learning opportunities, due to globalization of education and the on-going COVID-19 pandemic, has forced virtual education to be incorporated into undergraduatecurricula regardless of the mode of instruction. Traditional lecture-based teaching can easily bedelivered in a remote environment
. Algorithm Design is based on the three skillsdescribed above which help to solve a problem using abstractions and the transformation suchabstractions as described by the algorithm. When the algorithm is described through a computerlanguage, then the problem can be solved efficiently by a computer. An example of an algorithmthat generates the sequence of numbers above is depicted in figure 1. In the figure, the algorithmis written in the Python computer language. Page 24.531.4Figure 2. A sequence of numbers and a Python code that generates the sequence2.2 Learning Computational ThinkingLee et al. 6 describe the learning of computational thinking in
appointment at Purdue, Kyle worked for 16 years as a software engineer and developed systems for such industries as banking, telecommunications, publishing, healthcare, athletic recruiting, retail, and pharmaceutical sales.Alka Harriger, Purdue University Alka Harriger joined the faculty of the Computer and Information Technology Department (CIT) in 1982 and is currently a Professor of CIT and Assistant Department Head. Professor Harriger's current interests include reducing the IT gender gap, web application development, and service learning. Since January 2008, she has been leading the NSF-ITEST SPIRIT project that seeks to rekindle enthusiasm for information technology disciplines as a career
the futureof parallel and distributed computing through their own presentations. Furthermore, students inthis course get opportunities to do independent research by selecting a topic of his interest relatedto parallel and distributed computing, researching the topic in technical journals and otherpublications, and submitting a research report detailing the findings. Finally, students obtainhands-on experience through a sequence of programming projects involving the state-of-the-artsoftware technologies.To achieve the best learning result, the students taking this course are given a sequence of fivesmall laboratory projects and one term project. These projects are implemented on the Unixworkstations in DSL using Java [10]. The Java programming
represented among Research & Development (25%),Plant and Process Support (18%), and Process Design and Analysis (29%), with other roles alsorepresented. The respondents with PhDs had more years of experience (35% over 21 years),while those with a BS degree tended to have less experience (45% less than 5 years).After the initial questions on the background of the respondent, the first question asked, “whichof the following is most appropriate for your industry?” Using computer applications was themost important computing tool for industry with over 60% choosing this option. The otheroptions of statistics/analytics, programming, and machine learning were ranked as less important.As seen in the 1997 and 2003 surveys nearly all chemical engineers use
Agree: 53 Neutral 23 Disagree: 24 Strongly disagree: 31The computed average is: 3.12.The university also reports the following summary information for this question:75% of all courses answered this question with score above 3.63. 50% of all courses answeredthis question with score above 4.00. 25% of all courses answered this question with score above4.21.Taken together, the two questions suggest that students felt they learned a lot, but were workedfar too hard, given the number of credits received. That coupled with the results of our surveythat 86% were happy they took EECS 100, gives us some cause for gratification.6. Final thoughts
, including robotics, computerprogramming, agriculture, food science, unmanned aerial vehicles, clean energy, andconstruction science. Professional educators are paired with small groups (2-4) of pre-serviceteachers to run each class (maximum size of 18). This allows pre-service teachers to getpractical, hands-on experience, as well as to learn new STEM activities to include in theirown future classrooms. This also gives an excellent teacher to student ratio, providing a one-on-one learning experience for program participants. We focus, however, on measuring theimpact of two classes on the program participants. Each class employed similar pedagogyand the Scratch (2009)17 programming language. One relied heavily on computer sciencetheory and space
) develop strategies to besuccessful in computing, and iii) develop career plans and explore resources. To achieve theseobjectives, we designed a set of course-specific mentoring activities. In our initiative, we formeda group of mentors composed of successful alumni, graduate students, senior students, industrialpersonnel, and faculty of different races, genders, and ethnicities.We performed anonymous surveys, interviews, and reflections to answer our second researchquestion. We also analyzed students' course performance. Results show that mentoring improvesthe sense of belonging and confidence for both groups of students. Data also indicates first-yearstudents prefer mentoring to succeed academically (e.g., learning programming). On the otherhand
racial and cultural backgrounds.Course DescriptionHere is the catalog description: “CS 1200 - Introduction to Computer Science and SoftwareEngineering (2 semester credit hours) Introduction to the computing professions; overview ofComputer Science (CS) and Software Engineering (SE) curricula, connections with ComputerEngineering, other Engineering and Computer Science fields, and Arts and Technologyprograms; problem solving and other skills needed to succeed as a CS or SE major. Introductionto quantitative methods; team projects designed to replicate decision processes and problemsolving in real-world situations; additional preparatory topics for CS and SE majors.”The key course learning objectives are as follows: • Awareness of the areas
Paper ID #19132Secure Cloud Computing Infrastructure for K-12 EducationDr. Connie Justice, Indiana University Purdue University, Indianapolis Dr. Connie Justice is a Clinical Associate Professor in Computer and Information Technology (CIT) at the Purdue School of Engineering and Technology at Indiana University Purdue University Indianapolis (IUPUI) and a faculty member of the Center for Education and Research in Information Assurance and Security (CERIAS) at Purdue University. Professor Justice has over 20 years experience in the computer and systems engineering field. Professor Justice is a Certified Information
various transform methods (Fourier,Laplace and z), modeling of signals & systems in time/frequency domains, discrete powerspectrum, energy spectral density, bandwidth, filter input/output relations, Parseval’s theorem,convolution, signal-to-noise ratio, and transfer (system) function. Software packages likeMATLAB, MATHCAD and WFilter are useful computer IT tools so problems, examples can bepresented in the class and simulations discussed after analytical results are obtained for a givenproblem. Our experience has shown that use of these tools will enhance student learning and isan effective way of teaching the subject.I. Introduction. In our schools we have offered a Signals & Systems course with pre-requisitesof circuits I & II as
spend many staff hours for each student hour spentstudying it was not felt to be an effective use of staff time to attempt develop such a system.6.5 Computer-Based TutorialsThe computer based tutorial was not felt to be a possibility or from a staff point of view thedevelopment of Computer Based Learning (CAL) materials was considered to represent alarge input of extra effort that could be better spend in developments with respect to theseunits. However there are many commercially available packages that could have been of use. Page 6.702.5 Proceedings of the 2001 American Society for Engineering Education Annual Conference & Exposition
intensive, hands-on, motivationalexperience where each student would build, program, and develop the interface between theprogramming board and the robot hardware. We hoped that along the way the students wouldlearn about different engineering fields, computer science, and also the basics of computerprogramming and interfacing. The course concluded with a robot competition. Studentscompeted to see which robot could go through an unknown maze without bumping into mazewalls in the shortest time. The course objectives included: 1) Take the mystery out ofengineering and computing, 2) Show that engineering and computer science is fun and exciting,3) Demonstrate that engineering is for both women and men, 4) Emphasize hands-on, learn bydoing exercises
Session 3213 Session 3213 Framework for a Computer Based Corrosion Course M.A.A. Tullmin and P.R. Roberge Department of Chemistry and Chemical Engineering Royal Military College of Canada, Kingston, Ontario, Canada, K7K 7B4AbstractA framework for a computer based corrosion course has been developed, with a view todistance learning applications. Potential advantages of the computer based learningapproach over a conventional course offering include access to a larger target populationand optimization of the shrinking expert instructor pool
2005 American Society for Engineering Education Annual Conference & Exposition Copyright © 2005, American Society for Engineering Education # % of Goal Responses Responses Students learn computing concepts that can be applied 11.6 34% to other tools Students can use this particular tool in future classes 10.3 30% Students learn a formal method of problem solving 9.6 28% Students learn proper documentation of a solution