) 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
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 #37815Developing Post-pandemic Learning Community on an Urban CommuterCampusProf. Lily R. Liang, University of the District of Columbia Dr. Lily R. Liang is a Professor of Computer Science and the Director of the Master of Science in Com- puter Science Program at the University of the District of Columbia. Her research areas include computer science education, cybersecurity, artificial intelligence, and digital image processing. She has mentored dozens of graduate and undergraduate students in research and K-12 outreach activities. She is a fellow of the Center for the Advancement of STEM Leadership program (CASL
learning, enabling students to comprehend, reflect, and apply their learning toward solving new problems. Al- though critical thinking could be used toward solving challenging problems, it is sometimes considered as a similar concept of “challenging level” among students and instructors. This study aims to investigate this similarity issue by evaluating students’ opinions based on critical thinking, and challenging level of course as- signments in computer and software engineering courses. Students are asked to rank each assignment based on how much each assignment stimulated their critical think- ing, and how much it challenged them. Moreover, instructors provide their opinions about critical components of each course assignment for
Technological University (NTU) in Singapore. He is an affiliated faculty member of the NTU Centre for Research and Development in Learning (CRADLE) and the NTU Institute for Science and Technology for Humanity (NISTH). Additionally, he is the Director of the World MOON Project, the Associate Editor of the IEEE Transactions on Education, and the upcoming Program Chair-Elect of the PCEE Division at ASEE. His current research interests include STEM+C education, specifically artificial intelligence literacy, computational thinking, and engineering.Dr. Cristina Diordieva, Nanyang Technological University Cristina Diordieva is currently the Project Coordinator for the World MOON Project. She was a Post- doctoral Research Fellow in
Paper ID #37288”Just a little bit on the outside for the whole time”: Social belongingconfidence and the persistence of machine learning and artificialintelligence studentsKatherine MaoSharon Ferguson, University of Toronto Sharon is a PhD student in the department of Mechanical and Industrial Engineering at the University of Toronto. She previously completed her Bachelors in Industrial Engineering also at the University of Toronto. Her research focuses on the Future of Work on online communication. She is passionate about supporting women in Engineering and STEM more broadly, both within and outside of her research. She has
Paper ID #42117Mapping the Landscape of Digital Accessibility in Computer Science Education:A Mapping Literature ReviewMs. Morgan Haley McKie, Florida International University Morgan H. McKie is a 2nd year doctoral student in the School of Universal Computing Construction and Engineering Education (SUCCEED) at Florida International University (FIU). Morgan also holds a master’s degree in Engineering Management from FIU and is particularly interested in computer science for all. Her research interests include teaching and learning computer science in the Metaverse.Dr. Alexandra Coso Strong, Florida International University
interdisciplinary learning in computational modeling and simulation projects.Dr. Alejandra J. Magana, Purdue University Alejandra J. Magana, Ph.D., is the W.C. Furnas Professor in Enterprise Excellence in the Department of Computer and Information Technology and Professor of Engineering Education at Purdue University.Elsje Pienaar, Purdue University ©American Society for Engineering Education, 2024 Characterizing Teamwork Dynamics and Computational Model-Based Reasoning in Biomedical Engineering ProjectsAbstract—:Background: In STEM professions, teamwork is a fundamental aspect of the job. As aresult, it becomes imperative for STEM graduates to possess a comprehensive set ofprofessional
take place for students. Many times,however, these practices can be difficult for engineering students to learn [4] and for engineeringfaculty to teach [1]. As such, computational modeling skills and practices are often undertaughtby instructors and underdeveloped among graduating students.Fortunately, work in engineering and physics education has started to document effective waysfor delivering computation instruction through scaffolding, e.g., [4]–[7]. Even with these strides,research has indicated that incorporating computational modeling and simulation can lead to"cognitive overload" from having to learn and model different representations, such as physical,mathematical, and algorithmic, on top of the programming challenges. [8], [9].This
research focuses on designing the user experience of machine learning systems, particularly in social computing contexts.Prof. Joe Gibbs Politz, University of California San Diego Joe Gibbs Politz is an Associate Teaching Professor of Computer Science & Engineering at University of California San Diego. His research interests and experience include programming languages and systems as well as education for computer science and adjacent fields. He teaches broadly across the computer science curriculum with a focus on introductory programming and programming languages, and develops tools for teaching computing in both collegiate and secondary school settings. When not programming or teaching, he goes to the dog beach
qualitative research. AI, as a general field, has a wide range of definitions, but the agreed upon goal is to developcomputer programming to perform tasks, make decisions, and understand or perceive situations asa human would [2]. Considering AI as an umbrella, shown in Figure 1, its breadth of focusencompasses multiple subfields that each attempt to understand human-related phenomenathrough computers. One important sub-field of AI is a computer’s ability to enhance its intelligenceby learning from previous decisions, actions, consequences, etc. through machine learning (ML)[3]. As indicated by Figure 1, ML is a direct sub-field of AI that most closely aids in developingintelligence through its iterative processes. ML can be a helpful addition
’ interactions with faculty play ininfluencing the nature of their experiences in engineering and computer science programs.Bjorklund et al. [1] delved into the dynamics of student learning and emphasized the impact ofnear-constant instructor feedback. According to their findings, students reported experiencing themost substantial gains in their academic performance when they received regular andconstructive feedback. Additionally, Briody et al. [2] extend the discussion on faculty-studentinteractions by highlighting students’ desires beyond traditional classroom learning. Theyidentify how students who seek more personalized engagement with faculty, whetherindividually or within small groups. Throughout the literature, a consistent thread emerges
in makerspaces is not the only thing that got the attention of researchers, asother studies investigated the many outcomes of learning in makerspaces. With an understandingof what people learn in makerspaces, the learning experiences can be better tailored to fosterthose outcomes. In their literature review focused on making with computational tools,Timotheou & Ioannou [3] define three major categories of outcomes that have been explored inthe literature: (1) Knowledge outcomes, in terms of disciplinary knowledge [29], [31], [32]; (2)Attitudes, in terms of feelings towards learning [33], [34], [35]; and (3) 21st century skills,related to information literacy and professional skills [31], [34]. In parallel, Vossoughi & Bevan[16
Paper ID #41263Enhancing Thermodynamics Learning with a Modified Lab ExperimentDr. Ziliang Zhou, California Baptist University Ziliang Zhou is a professor of Mechanical Engineering at California Baptist UniversityDr. Xiuhua Si, California Baptist University Dr. Xiuhua (April) Si is a Professor of aerospace and mechanical engineering at California Baptist University. Her research interests include developing special functional composite materials, facemask effectiveness, respiratory disease and drug delivery, heat transfer enhancement and electromagnetic crystalline materials. Some of her publications include ”J. Xi, Z
pressure on students is expected.Applications of self-determination theory support this assertion. In a study applying the theory ina redesign of a computer engineering course, researchers found that student motivation and theirperception of the course was improved when autonomy was offered in evaluations [12]. By givingstudents choice in how they were assessed, students’ self-perception of their competency wasimproved. Overall, course coordinators can promote student motivation by reducing unnecessarystress from assessments.As seen in Figure 2, students also desire clear communication in their laboratories. Figure 5shows that students identified a wide array of learning outcomes for their laboratories. Takentogether, these results indicate that
California, San Diego Dr. Sandoval is the Associate Director of the Teaching + Learning Commons at the University of Cali- fornia, San Diego. She earned a PhD in Adult Education-Human Resource Development. Her research interests include adult learning and development, faculty deProf. Curt Schurgers, University of California San Diego Curt Schurgers is a Teaching Professor in the UCSD Electrical and Computer Engineering Department. His research and teaching are focused on course redesign, active learning, and project-based learning. He also co-directs a hands-on undergraduate research program called Engineers for Exploration, in which students apply their engineering knowledge to problems in exploration and
STUDENT RETENTION AND SATISFACTION IN COMPUTER SCIENCE SERVICE COURSES WHEN USING COMPETENCY-BASED GRADING AND ASSIGNMENT CHOICEAbstractEnrollment in introductory engineering courses, for non-Computer Science majors, often evokesapprehension, particularly when faced with the prospect of learning programming. The presenceof peers with prior coding experience can further compound these concerns. This study,applicable to a broad spectrum of engineering service courses, centers on student assignmentchoice within an undergraduate CS-1 curriculum. Guided by Self Determination Theory, weimplement assignment choice as a mechanism for students to chart a tailored path, selectingassignments aligned with course
paper, we share details about the equity-focused, collaborative codebook, the use of the codebook in our current RPP project, lessons learned, and recommendations for improving the process in the future.Keywords: Research practice partnership, program evaluation, team dynamics, computer scienceeducation, qualitative1 IntroductionThere are many models for partnership collaborations focused on systems change. One suchmodel is known as Research Practice Partnerships (RPPs). RPPs have been used in several fields,including education, with the goal of working collaboratively towards implementing solutions todirectly address problems of practice [2]. In the context of K-12 computer science (CS)education, problems of practice often focus on
Paper ID #36958A Study Report in the Web Technologies Course: What Makes FeedbackEffective for Project-based Learning?Alaa Jaber Alaa Jaber is currently pursuing her Master’s degree in Computer Science from the University of Michigan Dearborn. She has always been passionate about technology and its potential to transform the world. She is excited about the possibility of continuing her studies by pursuing a Ph.D. in Computer Science.Dr. Kimberly Lechasseur, Worcester Polytechnic Institute Dr. Kimberly LeChasseur is a researcher and evaluator with the Worcester Polytechnic Institute. She holds a dual appointment with the
Paper ID #39363Exploring the relationship between key constructs of self-assessmentcomponents, motivation, and self-regulation in engineeringTaiwo Raphael Feyijimi, University of Georgia Taiwo is a current Master’s student in the College of Engineering with an emphasis in Electrical and Computer Engineering at the University of Georgia, Athens GA. He had is Bachelors degree in Physics education from the Obafemi Awolowo University (O.A.U.), Ile-Ife, Osun, and an associate degree in Elec- trical and Electronics Technology Education from the Federal College of Education (Technical), Akoka, Lagos, Nigeria.Mr. Olanrewaju Paul
Paper ID #40272Undergraduate Student Experience with Research Facilitated by ProjectManagement and Self-regulated Learning ProcessesMs. Sakhi Aggrawal, Purdue University Sakhi Aggrawal is a Graduate Research Fellow in Computer and Information Technology department at Purdue University. She completed her master’s degree in Business Analytics from Imperial College Lon- don and bachelor’s degree in Computer and Information Technology and Organizational Leadership from Purdue University. She worked in industry for several years with her latest jobs being as project manager at Google and Microsoft. Her current research focuses
GAME model on students' PISA scientific competencies," Journal of Computer Assisted Learning, vol. 36, pp. 359-369, 2020/06/01 2020.[12] M. Sailer, J. U. Hense, S. K. Mayr, and H. Mandl, "How gamification motivates: An experimental study of the effects of specific game design elements on psychological need satisfaction," Computers in Human Behavior, vol. 69, pp. 371-380, 2017/04/01/ 2017.[13] J. Palmore, "Evaluation of evidence-based teaching techniques in a graduate fluid dynamics course," in 2020 ASEE Virtual Annual Conference, 2020.[14] R. Cutri, L. R. Marim, J. R. Cordeiro, H. A. Gil, and C. C. T. Guerald, "Kahoot, a new and cheap way to get classroom-response instead of using clickers," in 2016
expertise for the future.Pre-college education has been putting effort into improving STEM attitudes in STEM fields[18] and designing various learning approaches and interventions in STEM [19] to sparkstudents’ positive attitudes. Studies exploring elementary students’ STEM attitudes found thatSTEM integrated robotics curriculum resulted in students’ positive attitudes toward math [19]and positive STEM attitudes relating to computational thinking skills [20].Engineering education positively motivates students to learn STEM and develop an interest inSTEM careers [21]; [22]. Although exposure to engineering concepts in STEM should start at anearly age, a limited number of studies have examined the degree of impact engineering educationhas in
Pontificia Universidad Cat´olica de Chile (PUC-Chile). Isabel received a BEng and PhD in Engineering Sciences from PUC-Chile, and an MA in Policy OrganizatiDr. Jorge Baier, Pontificia Universidad Catholica de Chile He is an associate professor in the Computer Science Department and Associate Dean for Engineering ˜ Education at the Engineering School in Pontificia Universidad CatA³lica de Chile. Jorge holds a PhD in Computer Science from the University of Toronto in CaSof´ıa Helena Mar´ıa Olmedo Saavedra, Pontificia Universidad Catholica de Chile ©American Society for Engineering Education, 2023 Social ties, mental well-being, and self
ITiCSE conference on Innovation and technology in computer science education (pp. 9-12), June, 1999.[5] J. Mills & D. Treagust, “Engineering education—Is problem-based or project-based learning the answer,” Australasian Journal of Engineering Education, 3(2), 2-16, 2003.[6] V. Servant‐Miklos, G. Norman, & H. Schmidt, “A Short Intellectual History of Problem‐Based Learning,” In The Wiley handbook of problem‐based learning (pp. 3–24). John Wiley & Sons, Inc., 2019.[7] W. Zwaal & H. Otting, “The impact of concept mapping on the process of problem-based learning,” Interdisciplinary journal of problem-based learning, 6(1), 2012.[8] W. Hung, M. Moallem, & N. Dabbagh, The Wiley Handbook of Problem-Based Learning
, respectively. He also has extensive experience in working collaboratively with several universities in Asia, the World Bank Institute, and US- AID to design and conduct workshops promoting active-learning and life-long learning that is sustainable and scalable. Dr. Lawanto’s research interests include cognition, learning, and instruction, and online learning.Dr. Angela Minichiello, Utah State University Angela Minichiello is an associate professor in the Department of Engineering Education at Utah State University (USU) and a registered professional mechanical engineer. Her research examines issues of access, diversity, and inclusivity in engineering.Mr. Zain ul Abideen, Utah State University Logan Utah, USA Zain ul
Cognitive and Affective Development : Foundations of Constructivism. White Plains, N.Y. :Longman Publishers USA, 1996.18. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes Cambridge, Mass.: Harvard University Press.19. Gerber, E., & Carroll, M. (2012). The psychological experience of prototyping. Design studies, 33(1), 64-84.20. Houde, S., & Hill, C. (1997). What do prototypes prototype?. In Handbook of human- computer interaction (pp. 367-381). North-Holland.21. Foster, C., Wigner, A., Lande, M., & Jordan, S. S. (2018). Learning from the parallel pathways of Makers to broaden pathways to engineering. International journal of STEM education, 5(1), 1-16.22. Larson, J., Jordan, S
ability, and complex problem-solving ability. Her works have been published with journals like Higher Education Research & Development, Educational Research in China, and been released by Springer and Sense. Recently she is working closely with engineering teachers to enhance the connection between research and engineering education practice.Wangqi Shen, Tsinghua University Wangqi Shen is a PhD student in the Institute of Education, Tsinghua University, majoring in Engineer- ing Education. He got his master’s and bachelor’s degree in Educational Technology and has published some academic outcomes in Interactive Learning Environments and at international conferences such as AECT and SITE. Recently his works include
Paper ID #41620Effectiveness of Peer Led Team Learning in Online Engineering CoursesDr. David Paul Harvie, Embry-Riddle Aeronautical University David Paul Harvie is an Assistant Professor in the College of Aviation Graduate Studies Department at Embry-Riddle Aeronautical University – Worldwide Campus. David has a Ph.D. in Computer Science from the University of Kansas, a M.S. in Computer Science from North Carolina State University, and a B.S. in Computer Science from the United States Military Academy. He is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and a Member of the Association for
William Castillo-Garsow, Eastern Washington University ©American Society for Engineering Education, 2024 Analyzing Grading Criteria for Linear Graphs: Implications for Advanced Mathematical Learning Xiaojin Ye Department of Computer Systems Farmingdale State College, SUNY Carlos William Castillo-Garsow Department of Mathematics Eastern Washington UniversityAbstractWe conducted research to identify what features of a graph are important for college teacherswith the intention of eventually