courses, as well as a preview for future course material. Because these courses are pre-requisite for most CS courses in academic curricula, reinforcing the importance of the conceptsthey have learned - and tying them to future concepts - is critical for setting students up forsuccess. We present Stevie Wonder’s use of synthesizers, drum figures, ostinato, and cadentialprogression in Superstition as a form of “musical computer programming”. These comparisonsprovide introductory students insights into advanced computing concepts, including machinelearning algorithms, hardware side-channel attacks, and the importance and career benefits ofdiversifying computing skills at several levels of abstraction.KeywordsLevels of Abstraction, Metaphors
careers. Python is popular, accordingto the PYPL index [25] and Stackoverflow Developer Survey 2021 [30], Python is the mostpopular and the fastest-growing programming language (10.4%) in the world as of June 2022.2.2. Pedagogical ApproachesProgramming language plays an important role in computing education. Many systematic studiesexplore the teaching in introductory-level programming classes [14, 21, 27, 28]. Through ourobservation, a tremendous amount of research has been done on how the programmingenvironment can improve students' coding experience in recent years, especially theprogramming environment for Java and Python, which are the most taught entry-levelprogramming languages in college. The selection of the programming environment is
the IBM AI Course, an intensive course that uses many resources, like videos or/andslides, to teach the student the different complex subjects surrounding AI. Summer REU atStanford University was a program hosted to introduce 9 undergraduate students from HamptonUniversity and the University of New Mexico to robotics and machine learning. During thisprogram, we participated in research guided by graduate students and career-building activitiesguided by an advisement counselor. We were given the opportunity to tour multiple lab facilitiesin the area and network. Personally, the program gave me a whole new perspective on AI/ML androbotics.”Feedback #3: “By taking the course and the continuing REU program at Stanford University, I aminspired to
problem-solving and critical thinking, developing an understanding ofwriting unambiguous instructions and understanding syntax and semantics [3-5]. Most studentstry to overcome these challenges using additional help [6, 7], which could involve more practiceopportunities, watching YouTube or online materials, or taking guidance from the instructionalteam or peers. However, such additional help could easily require more effort and time. Priorstudies suggest that not mitigating the challenges could result in developing a dislike for thecourse, dropping or withdrawing from the course, or leaving a career trajectory that involvesprogramming or intrinsically hard concepts[8, 9].Over the years, many efforts have been reported emphasizing the importance
thehighest job fatality rate were structural iron and steel workers, roofers, and electrical power-lineinstallers and repairers [3]. These data indicate that, Fall is still a major safety issue both on thejob and outside of worksite. Students and trainees will be the future worksite safety leaders therefore they need to have indepth knowledge about Fall related safety, hazard identifications, and mitigation. A Fall safetytraining conducted by VR simulation can save a lot of money to do it in house [4]. It is veryimportant for the students to get training by VR simulation before joining to work force andstarting their careers. This work used VR simulation and analysis of Fall safety inspection andprevention which is a common hazard in industries
languages due to its widespread use globally. In theUSA, the engineering disciplines use language as a major means of communication [1] . Becauseof engineers' heavy usage of English, engineering students studying in the United States whowish to pursue their careers in the USA must have a strong grasp of the language in both oral andwritten form [1] .However, engineering students from countries that do not have English as their primary languageoften struggle to comprehend or use English effectively in their work [1][2][3]. One of theproblems with students not being able to use English fluently is that when writing in English,engineering students fail to effectively and accurately utilize sentence structures [4]. The use ofthe English language is a
the success of any technology depends on theend users [16], we must investigate learning technologies that characterize foundationalengineering courses to ensure effective implementation in preparing engineering learners forfuture careers. Furthermore, there is a need to develop and make available validatedinstruments for measuring technology acceptance-related factors towards a standardizedunderstanding of the literature.Presently, we observed adoption and acceptance to be used interchangeably in most of thereviewed studies and propose that a clear distinction be made by researchers in theirpublications. This distinction enables instructors to identify literature relevant to theirclassroom or institutional phase, yielding positive impacts
in medical research isprocessed to yield more concise, readable text. This article was chosen because it was publishedbefore LLM-based applications existed [21]. 1a. Input Text 1b. Tuned for Conciseness TEXT TEXT Figure 1: WordTune Text Results, Tuned for Conciseness.Module 2: LLM-assisted Summaries for Comprehension – Explainpaper, CopyaiSometimes, early career engineering writers are blocked by fear of the blank page, fear of failing,and general anxiety around writing. It can be helpful to jumpstart the process. Copyai [22] is atool that essentially trains users in high quality prompt engineering, adopting a visual graphicuser interface that narrows the writer’s needs down by interest area. For example, in Figure 2below
projects can foster the inclusion of students with learning disabilities (Daniela and Lytras, 2019; Nanou and Karampatzakis, 2022). In the case of tertiary education, industrial-scale robots are used to prepare students for careers in industry by emphasizing aspects such as hardware, software, and human-machine interfaces (Nagai, 2001; Brell-Çokcan and Braumann, 2013). However, industrial-scale robots are expensive to purchase. In addition, there is usually some oversight over their usage due to time-sharing and to prevent damage, which prevents "free-play" by students. Some solutions to this include the use of miniature robots and the use of online labs (Mallik and Kapila, 2020; Stein and Lédeczi, 2021). Though these reduce the cost of the setups
course. • RQ1: How do students perceive a time-restricted lab submission policy versus a point-restricted lab submission policy? • RQ2: How do these policies affect when students work on assignments and on students’ submission of bug-free code?2 Background and Related WorkMany in computing education are calling for more instruction on testing 7 . There have been anumber of approaches taken to address this need in the CS curriculum. Approaches taken toaddress this need include better tool support for teaching testing 8 , web-based tutorials 9 andgames 10 , and a vision for a test-driven development (TDD)-centered CS curriculum 11 .Introducing testing concepts early in a student’s programming career (i.e., in CS1) may
with numbers to find the hidden treasure. Additionally, an alternative encryption approachinvolved Secret Decoder Wheel created by INL, where letters were matched with symbols, allow-ing for encoding messages to describe the treasure locations in symbols for students to decode andfind.Similarly, in 14 was developed exclusively for grades third to eight where the students had to solveCaesar shift encryption algorithm. The author designed a worksheet and organized a scavengerhunt for an all-girls STEM-careers camp, catering to ages 6-12. They facilitated the completion ofthe worksheet collectively and split the participants into two age-based groups for the scavengerhunt. The author reflects that the activity effectively introduces children to
how many students know about CSopportunities in the community and their schools.Experience, the final component of CAPE, relates to students’ outcomes from CS courses andactivities. Examples of these outcomes include cognitive gains, interest, and awareness ofcomputing careers. Equitable Experience means that these outcomes are equitable across studentsubgroups [8]. Prior research in Experience investigated student content in an introductory CScourses [26–28], interest in computing [29], attitudes [24], and relevance of computing in thelives of underrepresented students [30]. Although this is the most studied component of CAPE,there are also gaps in areas that have been shown to impact academic achievement [31].3 Research MethodsTo answer our
feedback from faculty, instructional designers, andother experts in the field, ensures its alignment with real-world educational needs.Keywords: AI, Instructional Design, Online Transition, Online Education, e-learning, OpenAI,Python, Time Saving Strategies for Faculty, Lecture Capture, Repurposing Video Lectures.IntroductionThe landscape of higher education, particularly in engineering, is indeed undergoing a significanttransformation, with a marked increase in the demand for online engineering education. Thissurge is driven by professionals seeking career advancement in a rapidly evolving technicallandscape and the need for upskilling and reskilling among experienced engineers. However, thecreation of top-tier online courses presents
allowed students tocatch up on any issues that arose during the labs. Sometimes, the TA created extra hands-on whenstudents were not clear on some of the concepts the professor covered in class. Feedback fromcourse evaluation was positive overall, and many liked the course split between theory andpractice, between professor of instruction and lab TAs. We are currently collecting data from thecourse evaluations, and we hope to analyze the data and present the results in future work.Conclusions and Future WorkThis paper presented a comprehensive framework for teaching cloud computing in highereducation. The framework forces the student to use the Cloud throughout his academic career insuch a way that prepares him for certification as well as entry
immersive training and learning in medicine, advanced Manufacturing engineering and space systems He is a Pioneer in the creation of virtual and mixed reality based cyber learning approaches to support STEM and Engineering learning at both k12 and University levels. He directs The long-running Soaring Eagle program which targets underrepresented and minority students and encourages them towards STEM programs and careers. For his work in mentoring under-represented students, he was awarded the presidential PAESMEM award by the White House. He has published more than 150 refereed conference and journal papers ©American Society for Engineering Education, 2024 Innovative Next Generation
and integrated into a first-year introductory engineering course toexplore the possible benefits of providing students with AI-generated feedback. This course is anintroduction to engineering as a career, including problem solving, engineering disciplines,design, teamwork, and communication. It also serves as an introduction to multiple tools andtechniques used by engineers, including data analysis, numerical methods, error analysis, and theuse of computers for solving problems in physics and engineering. The course is structured inchronological order according to the following learning outcomes: 1. Understand the basics of the engineering profession, including problem solving, design, teamwork, and creativity. 2. Develop skills
Paper ID #43386Moving from Matlab to Python in a First-Year Engineering ProgrammingCourse: Comparison of Student Achievement and Assessment of Self-LearningDr. Robert Scott Pierce P.E., Western Carolina University Robert Scott Pierce is an Associate Professor of Engineering and Technology at Western Carolina University. He received his Ph.D. in mechanical engineering from Georgia Tech in 1993. Prior to his teaching career, he spent 14 years in industry designing automated positioning equipment.Dr. Chaitanya Borra, Western Carolina University ©American Society for Engineering Education, 2024
Engineering Education (ASEE) fellow, ASEE Electrical and Computer Engineering Division Distinguished Engineering Educator, Grace Hopper Celebration Educational Innovation Abie Award, Institute of Electrical and Electronic Engineers Undergraduate Teaching Award, Indiana Business Journal Women of Influence, and Society of Women Engineers Distinguished Engineering Educator.Katie Nicole Faith Collins, Rose-Hulman Institute of TechnologyAlejandro Marcenido Larregola, Rose-Hulman Institute of Technology Alejandro Marcenido is a senior Mechanical Engineer with minors in Robotics, Computer Science, Economics, and Entrepreneurial Studies. He is an international student from Spain, Madrid, and will be pursuing a career in robotics
that prepare early learners to become problem solvers in the computer science and engineering domains, skills that are necessary to meet future industry requirements. To address this gap, this paper proposes a framework and models to help educators identify available CT experiences to incorporate them into their lessons. The framework includes nine pedagogical experiences: (1) Unplugged, (2) Tinkering, (3) Making, (4) Remixing, (5) Robotics+, (6) Engineering, (7) Coding, (8) Dataying, and (9) Artificial Intelligence (AI).IntroductionThe growth of computational careers worldwide means that students of all ages, includingchildren in early childhood, must be consistently exposed to various problem
Faculty in Residency at Google during the summer of 2018 to learn more about this company’s culture, practices, and to understand the expectations for candidates (e.g. aspiring CS majors) who pursue career opportunities at this company and related prominent companies in tech.Krystal L. Williams, University of GeorgiaAshley Simone Pryor, Morgan State University College junior and Vice President of the Society for the Advancement of Computer Science, a Morgan State University ACM chapter. Active member of Morgan State’s Women in Computer Science organization.Theodore Wimberly Jr., Morgan State UniversityMariah McMichael, Morgan State UniversityAbisola Mercy ArowolajuDonald Bernard Davis, Morgan State UniversityToluwanimi Ayodele
, The Behrend College. Dr. Ashour received the B.S. degree in Industrial Engineering/Manufacturing Engineering and the M.S. degree in Industrial Engineering from Jordan University of Science and Technology (JUST) in 2005 and 2007, respectively. He received his M.Eng. degree in Industrial Engineering/Human Factors and Ergonomics and a Ph.D. degree in Industrial Engineering and Operations Research from The Pennsylvania State University (PSU) in 2010 and 2012, respectively. Dr. Ashour was the inaugural recipient of William and Wendy Korb Early Career Professorship in Industrial Engineering in 2016. Dr. Ashour’s research areas include data-driven decision-making, modeling and simulation, data analytics, immersive
succinctly: “based on decades of researchand analysis, racial disparities in STEM careers do not rest on individual deficiency in candidatesor even primarily on the individual racism of institutional and organizational gatekeepers. Racismis embedded in our society” [11].4 Proposed definition of equity in CS educationIn this section, we present our proposed definition of equity adapted for computing educationfrom a definition of equity in health [9] proposed by Braveman and Gruskin [9], presented insection 2.1, and in parallel with the other definitions of equity discussed in section 2.2.Paraphrasing the health definition for computing education gives us:Equity in CS Education is the absence of systematic disparities in educational outcomes
. His working experience started back in 2002 and over the years he had the opportunity of getting involve in different technological areas, such as: appliance repair, telecommunications and internet, biomedical, design and construction of electronic equipment and education in engineering and technologies. Since 2014, he works as an Engineering Professor and Researcher at the Technological University of Santiago. In addition, he has also been coordinating the engineering faculty as well in the university. Since 2019 he has been a member of the National Research Career, and the GITECI-UTESA Research Group since 2016. He is also a member of the Scientific Advisory Council and Reviewer of the UTESA Engineering Journal
Paper ID #43159Optimizing Database Query Learning: A Generative AI Approach for SemanticError FeedbackAbdulrahman AlRabah, University of Illinois Urbana-Champaign Abdulrahman AlRabah is a Master of Science (M.S.) in Computer Science student at the University of Illinois at Urbana-Champaign. He holds a Graduate Certificate in Computer Science from the same institution and a Bachelor of Science in Mechanical Engineering from California State University, Northridge. He has experience in various industries and has served in multiple roles throughout his professional career, including in oil and gas and co-founding a food &