leaders have called for incorporating thedevelopment of professional skills, like problem-solving for open-ended engineering designproblems, across all the different engineering courses. Following such a call, I, the author of thispaper, incorporated an engineering design project into the Computer Programming for Engineerscourse taught at University of Florida for two semesters, hoping that such instructionalintervention positively impacts students' problem-solving skills.2. Frameworks2.1 Conceptual Framework2.1.1 Social Problem-solvingThere are many ways in which literature has defined problem-solving; still, assessment tools formeasuring such skills are scarce. In this study, I used a model developed by D'Zurilla et al. [1] inwhich their team
, demographic surveys, and three tasks. Descriptive statistics and statistical tests provide insights.Performance discrepancies between IT and non-IT backgrounds are statistically significant. Feedback indicatespositive perceptions of low code. 1. Introduction In recent years, the intersection of technology and education has undergone a profound transformation, withemerging paradigms reshaping traditional approaches to teaching and learning. One such paradigm that hasgarnered increasing attention is low-code development—a revolutionary approach to software creation thatempowers individuals, regardless of their technical background, to design and deploy fully functional applicationswith minimal coding expertise. Low-code platforms provide
selecting VS Code and our approach to introducing it to engineering students. To assist students with diverse programming backgrounds, we provide comprehensive guidance with hierarchical indexing. By seamlessly integrating VS Code, known as a rich text editor, with a selection of extensions, our aim is to streamline the learning process for students by enabling it to function as an IDE. We perform an experimental evaluation of students' programming experience of using VS Code and validate the VS Code together with guidance as a promising solution for CS1 programming courses. 1. IntroductionIntegrated Development Environments (IDEs) play an important role in learning a
programming language has long been a staple in college computing education. AlthoughJava and Python are popular languages, C is still a top programming language of instruction [1], [2].Even if the introductory courses are taught in other languages, many programs still provide coursesthat teach the languages, typically in systems programming courses or operating systemcourses [3]–[5].However, unlike Java or Python where there is a single authorative compiler, C programming issupported by many compilers, editors, and other tools. In addition, installing a C developmentenvironment has traditionally been challenging for Windows systems. As a result, some institutionsopt for installing the C development environment in a server and have the students
instructor. However, often, a student would not complete the assignment during lab hours, so would have to wait for office hours to get an instructor's help. To submit, a student would upload the developed program files, then wait a week or more for grading to be completed and feedback to be provided.I n the last decade, many auto-graded programming assignment systems have been developed, both in academia and commercially [1–4]. Such systems are often web-based, save instructor's time with grading, and provide students more rapid feedback. Such systems have enabled instructors to switch from assigning one-large-program to many-small-programming assignments each week, wherein each assignment was more focused on a
-Computer Science (non-CS) major students. This demand is more paramount as many studentsmay not have been introduced to fundamentals of programming in high schools. According to anational survey, only 53% of high schools offer computer science courses. The scarcity of theavailability of courses at high school level results in more difficulties, and no prior computerprogramming experience. For such students the deficit in base continues to grow in college withtwo important facets: 1) such students are reluctant to pursue engineering and computing majorsand 2) these students find typical college programming courses more challenging and harder thanmany others who took programming in their high school, leaving them behind in courses.Considering
Cornerstone Projects will becompared. Project 1 took place during the spring of 2022 and was comprised of a windmillpower generation system. Students constructed this windmill and used Arduino programming tointerpret sensor data and calculate system performance. Project 2 took place during the 2022summer semester and was comprised of a water filtration system. In this project, students utilizedthe Arduino to both observe system information and control its behavior.At the end of each of these semesters, students took a survey in which they provided theirperceptions of the programming instruction they received, in addition to expressing theirconfidence in programming. Results of these questions from Spring 2022 (Project 1) andSummer 2022 (Project 2
into the fine-grained differences in planning betweennovice and experienced learners. We discussed these differences and how they can be used toguide meaningful interventions that focus on megacognitive skills in computing education.1 IntroductionA deep understanding of the difference between students who had experience in programmingand those who had no prior experience can help instructors make informed decisions in how toteach both groups in the same course more effectively. As more students are exposed toprogramming in high school, it is increasingly likely for an introductory programming course tohave both students who had experience in programming and those who had no prior experience.Undoubtedly, students with no prior experience in
devices andtechnology in their education. The first cohort of Gen Alpha is expected in university classroomsaround 2028. Generation Z describes those born from ~1997–2009, and Generation Alpha refersto people born in or after 2010. The Gen Alpha student will be one who is truly a “digitalnative”—they will not have known a world without pervasive touchscreen devices. Colleges andUniversities must be ready for possible changes in the learning methodologies required to meetthis new generation. Use of computing devices in primary and secondary education has growntremendously through the use of one-to-one (1:1) device or technology programs. A 2017 report[13] found that more than 50% of K-12 teachers taught in 1:1 classroom environments and ameta
with a quiz on the previous week’smaterial and then have students work on the lab individually. A faculty member would bepresent to field questions and troubleshoot issues students may face while they are working ontheir solution to the lab assignment. This proved to be problematic though as it resulted in somesignificant issues as enrollments increased in entry level Computer Science classes: 1. Students would not be able to get the attention they needed 2. Students would “fall through the cracks” since there was only one faculty member in the classClearly, there was room for improvement. In redesigning the lab experience for this introductoryclass, there were a few goals in mind: 1. Teach students how to work with
incorporating computational tasksinto statistics education is one of them [11,12]. The mini-lecture and active learning model wasused by [11] in a data science course taught by faculty in statistics, while [12] recommended anemphasis on applications in a data analytics course. The use of real-world applications was alsorecommended by [13] in a physics programming course. In an inter-disciplinary course thatincluded students from "business, liberal arts, and engineering and computer science,” [14, p.1]reliance on cross-disciplinary collaboration and business applications was used to increasestudent interest. In their work to incorporate data science modules into multiple STEM courses,[15] encouraged data collection activities as well as visualization
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
research is to help in shaping a safe pathway to AI-based learning environmentsfor human progress.AI is expected to lead the new revolution in the social, economic, health, and technology areas.Currently, the fast development of AI-based products is accompanied by huge investments fromlarge companies and governments. In the U.S., both the previous and the current administrationfully support AI research and development efforts. For example, on February 11, 2019, PresidentTrump issued Executive Order 13859 to maintain American leadership in artificial intelligence[1]. With respect to this executive order, France A. Córdova, Director, National ScienceFoundation (NSF), included the following statements [2]. "NSF has a long and rich history of
, lectures, examples, and assignments.Participants in this study were recruited for a free introductory Python coursethrough LinkedIn and Twitter. Participants were randomly assigned either to theinstructor-led or the self-paced versions of the course. It appears that based on thescores and lower attrition rates, a student-driven approach using Colab notebooksis at least approximately as effective in helping students learn the concepts.1. IntroductionThe supply of workers capable of performing effectively in software development is not keepingup with industry demand; unfortunately, the supply of instructors capable of training those futuresoftware developers is also likely to fall short of what is necessary. Growth in softwaredevelopment jobs is
positive psychological and socialoutcomes 1 . Computer-Supported Collaborative Learning (CSCL), grounded in the SocialConstructivism Theory, leverages technologies to facilitate and encourage interactions amongstudents across domains 2 . Although CSCL has been incorporated into education by variousstudies 3,4,5,6 , teachers and policymakers may lack understanding of how group collaboration canbe effectively integrated into instructional strategies 7 . The use of CSCL technologies,pedagogies, and curricula by both teachers and students requires further investigation.Past CS education research has attempted to detect individual-level problem-solving behaviors toassist struggling students, including identifying error-fixing patterns 8 and latent
cybersecurity education approach on high school student cybersecurity learning Sai Suma Sudha1, Sai Sushmitha Sudha1, Ahmad Y Javaid1, Quamar Niyaz2, Xiaoli Yang3 1 EECS Department, The University of Toledo, Toledo, OH, United States 2 ECE Department, Purdue University Northwest, Hammond, IN, United States 3 CS Department, Fairfield University, Fairfield, CT, United States {saisuma.sudha, saisushmitha.sudha, ahmad.javaid}@utoledo.edu, qniyaz@pnw.edu, xyang@fairfiled.eduIntroductionThe need for cybersecurity is growing as we become more dependent on digital tools and programsto run our daily lives
study. Theconsent and questionnaire was sent out via Qualtrics. Students in Spring 2022 received extracredit for completing the questionnaire. This partially explains the difference in responserate between semesters.2.2 Data CollectionStudents were asked to volunteer and answer a questionnaire with 60 questions that weretaken from the following validated instruments: the Index of Learning Styles [8], the IntrinsicMotivation Inventory [1], the Growth Mindset Scale [2], and sense of belonging questionnaire[5].2.3 InstrumentsThe Intrinsic Motivation Inventory is an instrument that assesses participants’ intrinsicmotivation based on the following six subscale scores related to performing an activity: In-terest/Enjoyment, Perceived Competence
to deliver lectures and supplement instruction has been onan upswing for a number of years. This trend showed a tremendous growth over the pandemic asexpected with the transition to some variation of online delivery whether it was remote teachingor via the development of high quality online courses. A dominant mechanism for lecturedelivery in engineering disciplines at a large university in the southwest has been the use ofvideo. A short survey of faculty identified 3 dominant strains in video production (1) Videocontent captured using Zoom (2) Video content captured in professional studio settings and (3)Video content captured in classrooms using existing lecture capture technologies built in class.The second strain of video creation has
feasibility of HyFlex in the engineering disciplines.Given that HyFlex is a relatively new approach to teaching, there is a small but growing set ofliterature on its efficacy. Initial research indicates that HyFlex neither greatly helped nor hinderedstudents’ learning [1], but rather provides flexibility with managing school, work, and homelife[2]. This format also has potential benefits for retaining students who face challenges with in-person attendance. Studies suggest that students are reasonably satisfied with HyFlex classrooms[3] and valued their options of choosing the mode of instruction for each session. Graduatebusiness students reported appreciating that they could still be engaged with the instructor andpeers even when remote [4]. Miller
: (1) Water quality analysis; (2) Lake front development and remediation (3) Development of MOOCs; (4) Accreditation, academic quality framework and academic auditing; (5) Learning Spaces – Blended approach; (6) Active and experiential learning; (7) Sustainable Development and Education; (8) Urban Environment Management and Smart city; (9) solid and hazardous waste management and landfill engineering; and (10) life cycle assessment and sustainable construction materials. His research and train- ing programme is funded by the ITEC, DST, World Bank, MEA, MoE, PWD and several prominent state governments and industries. Dr. Jana published around 50 research articles in international and national journals and conferences
in Cyber Security (BSCS) and Master of Science inInformatics (MSIN) whereby each degree is broken down into embedded stackable credentials,with a fast-track 4+1 option for students to complete both degrees in 5 years. This paperprovides a blueprint of the bridged undergraduate and graduate curricula integrated to provideembedded stackable credentials with fast-track 4+1 option bridging the two degrees. Most of themajor-core of BSCS is divided into three embedded stackable credentials, namely, CyberSecurity Basics Certificate, Cyber Security Systems Certificate, and Cyber Security AdvancedCertificate. After completing the three credentials, a student needs only 9 hours to complete themajor-core for the BSCS degree. Similarly, most of the MSIN
use automated grading in programming classes. Institutions havedeveloped graders for C++, Java, MATLAB, and many other programming languages.LabVIEW is a graphical programming language that people frequently use for data acquisition.Since there were no automated grading programs for LabVIEW, a computerized grading systemhas been developed. With the grading program, students email the LabVIEW files they havewritten, and the program provides their assignment score and feedback concerning missingprogram functions or wires. Students then can resubmit their work until the due date. Thegrading program was implemented in a LabVIEW programming course at California BaptistUniversity using NI’s LabVIEW Core 1 and Core 2 curricula. When using the
sections. In the Fall 2022 semester, we piloted aself-paced, mastery-learning model for the online section, while the in-person sections continuedto follow a traditional format.Mastery LearningThe mastery learning approach was articulated in the 1960s by Bloom [1], who saw it asenabling nearly all students to achieve mastery of a subject, despite variations in aptitude andlearning styles. The essential idea, which derives from Carroll [2], is that variations in aptitudedo not imply differences in the capacity to master the material, only to differences in the timerequired to achieve mastery. Mastery learning is therefore closely linked to self-pacedinstruction.A review of prior work on mastery learning in computer science education is given in [3
learning and robotics together withthe specific machine learning and robotics applications in autonomous systems, the first author hasexplored the Machine Learning Course and Robotics Course currently available in differentUniversities [1-7]. Especially, during her 8 weeks summer visiting at Stanford University, shealso had a chance to explore resources to integrate into the course. Based upon all these works, shesuccessfully adapted/developed course EGR 391- Intermediate Research Topic Course to aResearch-based Course on Machine Learning and Robotics by combining teaching, research,and engagement. This course is especially designed for the team of junior undergraduate studentswho are participating in the NSF EIR and NASA ULI projects.The
IoT concepts to remotely located students and helpthem learn how to use the components of the IoT learning kits. The exercises start with the basicsof connecting and reading data from sensors and progress through logging data to a website andthen utilizing it to control an IoT enabled device remotely. The IoT learning kits provide theopportunity for remotely learning students to engage with hands-on learning. Thus, students gaina better understanding of IoT concepts and technologies and how they might be integrated intotheir capstone projects.IntroductionProblem based learning (PBL) is an area of research that has been shown to increase studentinterest on engineering topics [1]-[3]. Internet of Things (IoT) enabled devices present an
class. Sense of belonging was measured by surveysat the beginning and end of the course. Students were asked to respond to questions about their per-ceived comfort in the classroom, perceived isolation, and perceived support from course staff andother students. We note that the whole class’s sense of belonging statistically increases from thebeginning to the end of the semester in both sections. Furthermore, the increased sense of belong-ing is more pronounced in the in-person section. Based on our findings, we conclude that onlinesections for on-campus students may be an effective way to accommodate large class sizes, in-creased enrollment pressure, and students’ need for flexibility, while not disadvantaging students’learning outcomes.1
schedulesresulted in fewer students completing the formative assessments. More students completed thehomeworks before the exam date in the Strict semester, motivated by the partial credit deadline.Completion of formative assessments before the exams correlated with better performance, evenwhen controlling for student GPA.1 IntroductionThe blended teaching format has been rapidly popularized over the past years, especially duringthe COVID pandemic time. This form of combining online and in-class instructions providesstudents with an opportunity to learn how to distribute their time independently [1, 2]. It isimportant for instructors to understand how online engagement on assignments outside theclassroom affects students’ overall course performance, so
highlighting, etc.), stress, and interruption [3]. Theobjective of this article is to provide a survey of literature that shows current efforts that haveaddressed the need to showcase the importance of the technical interview process in academicTECHNICAL INTERVIEW INTEGRATIONsettings, and highlight the need to further alleviate the awareness deficiency of its overallimportance to CS and related majors who aspire to have careers in tech.2. Literature ReviewTo better understand the current efforts involving interview preparation in academia, notableactivities seen in literature are categorized into four descriptions below (Table 1). The followingsubsections provide example case studies and initiatives that fall into one of these categories,respectively
interests are on studentsˆa C™ problem-solving disposition and instructional strate- gies to advance their ways of thinking. Dr. Lim is particularly interested in impulsive disposition, stu- dentsˆa C™ propensity to act out the first thing thatLisa Garbrecht, University of Texas, AustinPhilip B. Yasskin ©American Society for Engineering Education, 2023Introduction Mathematics has historically been taught in ways that are a barrier to minority studentspursuing advanced STEM courses in high school and college [1] while current teaching methodsare heavily reliant on spoken and written language, which can be particularly problematic forbilingual students [2]. Consequently, too few underserved students such as
. (Engineering Education) graduate student at Utah State University. His M.S. research is in experimental fluid dynamics, his Ph.D. work ex- amines student social support networks in engineering education, and his other research activities include developing low-cost technology-based tools for improving fluid dynamics education. ©American Society for Engineering Education, 2023 Uncovering Student Social Networks: Entity Resolution Methods for Ambiguous Interaction DataIntroduction Over the last century, cognitive psychologists have proposed that social interactions are akey component of student learning [1]–[4]. For example, Albert Bandura’s Social LearningTheory [5] posits