is increasingly being woven into the fabric of everyday life. It isbecoming more and more a seemingly necessary and somewhat trusted component of society forboth personal and non-personal day-to-day interactions. Developing such intelligent systemsrequires technical expertise, such as an in-depth knowledge of natural language processing ormachine learning. However, in addition to technical expertise, a deep awareness andunderstanding of ethics and societal impact are also essential. Mastering knowledge of ethics andsocietal impact falls on the shoulders of computer professionals and programmers, whose role isto design and implement the decision-making component of intelligent systems. Thedevelopment of intelligent systems with embedded
improve teaching and student learning. She is currently working with several engineering faculty assessing the impact of in-class use of technology on teaching and student learning. Dianne has also worked as an education consultant for a number of organizations conducting program evaluations and is currently serving as external evaluator on several grants. Her research interests focus on faculty development, action research, the role of technology in teaching and learning, and assessment in higher education. Page 22.366.1 c American Society for Engineering Education, 2011 Computing
Self-Confidence • Exercise of Judgment and Discretion • Predominantly Intellectual Work • Regulated/License Usually required • Dedication beyond Pecuniary and Personal Considerations.ACM and IEEE Viewpoints on IT CertificationsAs an important organization, IEEE promotes concepts of certification to educationalinstitutions. IEEE categorizes occupational certification into three areas: • Certifications granted by organizations or professional associations, such as the IEEE Computer Society • Industry or product-related certifications, such as Novell Certified Engineer • Certifications granted by government agencies that train for specific jobs then validate that learning and ability to perform that job can be
AC 2009-1705: EMBEDDED SYSTEMS CAPSTONE PROJECTS IN THECOMPUTER ENGINEERING AREA OF SPECIALIZATION WITHIN THECOMPUTER SCIENCE DEPARTMENTAfsaneh Minaie, Utah Valley UniversityReza Sanati-Mehrizy, Utah Valley University Page 14.527.1© American Society for Engineering Education, 2009 Embedded Systems Capstone Projects in the Computer Engineering Area of specialization within the Computer Science DepartmentAbstractThe purpose of a capstone design project is to provide graduating senior students the opportunityto demonstrate understanding of the concepts they have learned during the course of theirstudies. In order to provide our students
one-to-one proportion to lecture courses. Furthermore,lecture courses tend to emphasize the application of techniques in solving engineering problems.Table 1 below shows the approximate core lecture/lab breakdown at the University of Houston,College of Technology’s Department of Engineering Technology illustrating one example of theextent of experiential learning that is typically embedded in ET programs.Table 1 Approximate Breakdown of ET Core Lecture/Lab Courses at UH TAC/ABETaccredited B.S. degrees in Computer ET (CET) and Electrical Power ET (EPET). (53 SemesterCredit Hours) Lecture Lab Capstone CET 13 courses (54%) 9 courses (38%) 2 courses (8
instructions within computer programs that direct how theseprograms read, collect, process, and analyze data. We use the term bias to refer to computeralgorithms that systematically discriminate against certain content, individuals, or groupswithout a sound basis [1].As automated systems become an integral part of many decisions that affect our daily life,civil rights, and public discourse, there is concern among social scientists and computerscientists about the presence of bias in machine learning and big-data algorithms. A body ofwork has appeared in popular as well as scholarly literature addressing algorithm bias. In2018, then visiting assistant professor at the University of Southern California, Safiya Noble[2], who also holds a faculty
. Specific skills developed include computerprogramming in Python, basics of electrical circuits, integrating computer hardware andsoftware, computer networking, and cyber security. Campers were introduced to computingcareers and majors through presentations and guest speakers during the Lunch and Learn time.At the end of the week, teams of campers applied these skills to an Internet of Things-themedCapstone project, which they presented to their peers and parents.Pre- and post-surveys, daily reflections, and structured interviews were collected to establishcontinuous improvements for the program and to further our understanding of how to betterprepare high school students to choose disciplines of study. Triangulation of the multiple sourcessupports
Paper ID #19018Group-Based Cloud Computing for Secondary STEM EducationDr. Anthony J Petrosino Jr, University of Texas, Austin Anthony Petrosino is a learning scientist and an associate professor of STEM education and the Eliz- abeth G. Gibb endowed fellow at The University of Texas at Austin. He was a seven-year member of the National Science Foundation (NSF) funded VaNTH (a consortium of Vanderbilt University, North- western University, University of Texas, and Harvard-MIT Health Sciences and Technology), ERC, and a principle investigator of a U.S. Department of Education funded PT3 (Preparing Tomorrow’s Teach- ers to
) campus.They found six themes emerging from their participants’ experiences: finding comfort, buildingcommunity, fitting in, experiencing frustration, overcoming imposter syndrome, and valuingmentorship [2].No studies were identified within the United States where all goals of photovoice (listed above)were implemented. One study, conducted in Scotland, was found in which researchers were usingphotovoice to study the transition journey of transfer students transitioning from 2-year collegedegrees to the School of Computing at a university [7]. Perez has proposed that the communityshould consider conducting a study that uses photovoice to understand the experiences of youthand their families in integrating computing into their learning schedule [8]. The
approach, based on these paradigms, thatis specially designed for engineering courses. We have developed interactive, self-pacedcomputer-based lectures in which students learn abstract concepts on their own. In this approach,classrooms have been allocated for problem solving, student-teacher interaction, and industry-related applications. The proposed teaching methodology combines the constructivistapproach—which enables students to acquire knowledge meaningful to them through interaction—and the objectivist approach—in which students passively receive information via computer-based lectures. Computer Based Virtual Classroom (CBVC)—a computer program that mimicstraditional classrooms by presenting lectures in chronological order, an approach
Society for Engineering Education, 2006 Using Computer Animations in Teaching Statics ConceptsAbstractIn many institutions, Statics is one of the first purely technical courses that most engineering andengineering technology students encounter. This places a considerable burden on the courseinstructor to present engineering concepts in a way that will not only enhance learning, but alsoattract and retain the interest of students who are looking into pursuing engineering relatedcareers. If not well presented, the Statics course can be an intimidating experience thatdiscourages learning. This intellectually demanding course is frequently taught in a lectureformat that makes it difficult for students to make the connection between the
Microsoft’s Technology Education and Learning Support (TEALS)1,Google’s CS Research Mentorship Program (CSRMP)2 and Meta University3, among others.Motivated by prior works’ calls for additional research on effective diversity programs intechnology [21] and the links between programs’ design choices and students’ affectiveoutcomes [22], our work investigates how specific features of a CS-specific support programcontributed to the social capital and persistence in CS of students whose identities areunderrepresented in CS. More specifically, we investigate the impact of students’ participation inGoogle’s Computer Science Summer Institute (CSSI): a 3-week-long program where graduatinghigh school students from historically underrepresented groups in CS
Paper ID #37917A Module on Ethics and Social Implications of Computingfor Introductory Engineering Computing CoursesBrooke Odle (Assistant Professor) Dr. Brooke Odle is an Assistant Professor in the Engineering Department at Hope College. She and her team of undergraduate researchers are interested in developing interventions to reduce risk of musculoskeletal injury associated with manual patient-handling tasks. Courses she teaches include “Engineering Computing,” “Biomechanical Systems,” “Dynamic Systems Laboratory,” and “Mechanics of Materials Laboratory.” Prior to joining Hope College, Dr. Odle was a
camera interfacing module for a microprocessor course, andseveral programming modules for use in a data structures course. We have also found that thesemodules can be used effectively at the community college level and can provide resources tocommunity college faculty that they might not otherwise have readily available. In fact, the useof computer graphics and image processing programs as teaching and motivational tools isbecoming common at all levels of education. As an example, one of our modules used in CS1provides a brief background in computer vision concepts and allows students to write an imageprocessing program with applications in computer vision. Using concepts learned in a firstprogramming course students can read in a two
this paper, the hybrid format is a course format which includes meetings inperson in the traditional classroom based setting, and also makes use of web support over theInternet. Hybrid courses are also known as blended learning or blended learning environments.[1] Blended approaches base their pedagogy on maximizing the benefits in face-to-faceinteraction and online learning; finding a harmonious balance in blending the benefits inherentin face-to-face interaction and inherent advantages in online access to knowledge. [1] Blendedlearning and hybrid class formats are also discussed in the ECAR Research bulletin BlendedLearning [2].The blended learning approach has become a more common method of delivering coursecontent. The Computer Graphics
’ expectations, nature of the course, and the course’scontent. It is expected that the institutions developing a master’s degree program in Technologyof any nature will benefit from developing a course in computer applications.Master’s program objectives and students in the programStudents’ learning goals and objectives for graduate courses should meet the program goals andobjectives. As an example, the objectives of the programs at Northern Kentucky University andPurdue University are studied.The four program objectives for the Master of Science in Technology at Northern KentuckyUniversity1 are:1) To enhance the ability of graduates to move into technical management
areas of Machine Learning and applications with special emphasis on neural network and neuro-evolutionary algorithms, and their applications. He has published more than 60 journal papers and more than 170 conference papers in a variety of conference and journal venues. He has been an Associate Editor of the IEEE Transactions on Neural Networks from 2002 to 2006, and an Associate Editor of the Neural Networks journal from 2006 to 2012. He has served as the Technical Co-Chair of the IJCNN 2011.Dr. Ken Christensen P.E., University of South Florida Ken Christensen (christen@csee.usf.edu) is a Professor in the Department of Computer Science and Engi- neering at the University of South Florida. Ken received his Ph.D. in
AC 2007-1203: DEVELOPMENT OF A STANDALONE COMPUTER-AIDEDTUTORIAL TO INTEGRATE COMPUTATIONAL TOOLS INTO AMECHANICAL DESIGN CURRICULUMFernando Class-Morales, Cessna Aircraft Company Fernando Class-Morales earned his B.S. in Mechanical Engineering from the University of Puerto Rico at Mayaguez in 2002, and his M.S. in General Engineering from the University of Illinois at Urbana-Champaign in 2007. He worked as an intern for UTC – Pratt & Whitney, and is currently a Mechanical Systems Engineer at Cessna Aircraft Company in Wichita, KS. In his free time, Fernando enjoys playing paintball and working on obtaining his pilot license.Jim Leake, University of Illinois-Urbana Champaign James Leake joined
computer networks • Students learn how to develop network applications using a programming language • Students understand general architecture of computer networks and how layered protocols of computer networks work • Students are able to identify and explain current topics in computer networks, such as security and quality of service, among others Page 9.206.5 “Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition Copyright 2004, American Society for Engineering Education”While some course descriptions give more detailed goals or outcomes, most stated
Paper ID #41707Work in Progress: Understanding Differential Experiences of Identity in ComputingEnvironments Using a Computing Privilege InventoryCecil´e Sadler, Massachusetts Institute of Technology Cecil´e Sadler is a PhD student at the Massachusetts Institute of Technology in the MIT Media Lab with the Lifelong Kindergarten group. Her interests lie at the intersection of computing and education in designing equitable learning environments that cultivate creativity through technology-mediated creative learning experiences. She focuses on investigating how computing can be leveraged to create spaces for Black and brown
). In this case,the time constant has been provided to the student as an input. These two examples of multi-partproblems implemented in Blackboard show how a self-graded assessment can be created thatallows assessment of each part of the problem. This means that a certain level of partial credit ispossible while still being self-graded. 4. External Tools to Limit CheatingIn addition to providing problems with multiple sets of inputs and solutions, there are other toolsthat can help mitigate cheating on exams. Each of these include limiting/monitoring studentactions on their computer during the exam. 4.1. Respondus Lockdown BrowserThe Resondus LockDown Browser integrates with several learning management systems,including Blackboard. The
itself. On the other hand, the framework’s application will serveas a first step for further educational research in context with the use cases. Examining andunderstanding the use cases’ socio-technical context and the adoption processes will help tofurther develop the respective tools and offer insights into future technology innovationprocesses.References [1] J. Radianti, T.A. Majchrzak, J. Fromm and I. Wohlgenannt. „A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda.” Computers & Education, 147, 2020, 103778. https://doi.org/10.1016/j.compedu.2019.103778 [2] M. Borrego, J. E. Froyd and T.S. Hall. “Diffusion of
mind. The researchersfocused on the skills aspect of habits of mind framework to analyze how students use mentalmodels and tools that indicate their computational, observational, and manipulation skills [10],[14]. Table 3. Habits of mind definition based on Project 2061 [8], [9] Habits of mind Definition Indicators Making decisions about what concepts are Values relevant to their understanding and how to gauge conceptual scientific knowledge Student’s learned scientific concepts How past knowledge and experiences
Paper ID #28735Using a Pedagogical Agent to Support Students Learning to ProgramDylan Keifer Finch, Virginia Tech I am a Master’s student researching computer science education and human-computer interaction at Vir- ginia Tech.Prof. Stephen H Edwards, Virginia Tech Stephen H. Edwards is a Professor and the Associate Department Head for Undergraduate Studies in the Department of Computer Science at Virginia Tech, where he has been teaching since 1996. He received his B.S. in electrical engineering from Caltech, and M.S. and Ph.D. degrees in computer and informa- tion science from The Ohio State University. His research
Paper ID #19102Assessment of Student Learning Experience in Two Exemplary EngineeringProjectsDr. Wookwon Lee, Gannon University Wookwon Lee, P.E. received the B.S. degree in electronic engineering from Inha University, Korea, in 1985, and the M.S. and D.Sc. degrees in electrical engineering from the George Washington University, Washington, DC, in 1992 and 1995, respectively. He is currently an associate professor and department chair of the Department of Electrical and Computer Engineering at Gannon University, Erie, PA. Prior to joining Gannon, he had been involved in various research and development projects in industry and
: Integrating Robotics in School Curriculum. 2012.7. Grandgenett, Neal, et al. “Robotics and Problem-Based Learning in STEM Formal Educational Environments.” Robots in K-12 Education: A New Technology for Learning: A New Technology for Learning 94 (2012).8. George, Sébastien, and Pascal Leroux. “Project-based learning as a basis for a CSCL environment: An example in educational robotics.” First European Conference on Computer-Supported Collaborative Learning (Euro-CSCL 2001). 2001.9. Ramos, Fernando, and Enrique Espinosa. “A self-learning environment based on the PBL approach: an application to the learning process in the field of robotics and manufacturing systems.” International Journal of Engineering Education 19.5 (2003): 754-758.10
c American Society for Engineering Education, 2015 Work-in-Progress: A Software Based Robotic Vision Simulator for Use in Teaching Introductory Robotics CoursesWith the rising popularity of robotics in our modern world there is an increase in engineeringprograms that offer an introductory course in robotics. This common introductory roboticscourse generally covers the fundamental theory of robotics including robot kinematics,dynamics, differential movements, trajectory planning and basic computer vision algorithmscommonly used in the field of robotics. To teach robotic vision the student is generallyexposed to a variety of vision algorithms where they learn how to combine them along withthe selection of their parameters to
Paper ID #12908Work-in-Progress: Conflict-Driven Cooperative Learning in Engineering CoursesDr. Neelam Soundarajan, Ohio State University Dr. Neelam Soundarajan is an Associate Professor in the Computer Science and Engineering Department at Ohio State University. His interests include software engineering as well as innovative approaches to engineering education.Mr. Swaroop Joshi, Ohio State University Swaroop is a Ph.D. student in Computer Science and Engineering at The Ohio State University. His interests include a range of problems in software engineering as well as the use of technology in the classroom.Dr. Rajiv
AC 2007-2341: TRANSFORMING THE MICROPROCESSOR CLASS:EXPANDING LEARNING OBJECTIVES WITH SOFT CORE PROCESSORSLynne Slivovsky, California Polytechnic State University Lynne Slivovsky received her B.S. in Computer and Electrical Engineering and her M.S. and Ph.D. in Electrical Engineering from Purdue University in 1992, 1993, and 2001, respectively. She worked with the Engineering Projects In Community Service (EPICS) Program from 2001 to 2003. In Fall 2003, she started a tenure-track assistant professor position in Electrical Engineering and Computer Engineering at California Polytechnic State University, San Luis Obispo. She received a Frontiers In Education New Faculty Fellow Award in 2003. In
AC 2007-2504: INTRODUCING MICROFLUIDICS TO ELECTRICALENGINEERS: AN INTEGRATED PROBLEM-BASED LEARNING EXPERIENCEIan Papautsky, University of Cincinnati IAN PAPAUTSKY received his Ph.D. in bioengineering from the University of Utah in 1999. He is currently a tenured Associate Professor of in the Department of Electrical and Computer Engineering at the University of Cincinnati. His research and teaching interests include application of MEMS and microfluidics to biology and medicine.Ali Asgar Bhagat, University of Cincinnati ALI ASGAR S. BHAGAT received his M.S. in electrical engineering from the University of Cincinnati in 2006, and is currently pursuing his Ph.D. His research interests include