programmedto do so. Machine learning algorithms are used in a wide variety of applications, such as inmedicine, email filtering, speech recognition, and computer vision, where it is difficult todevelop conventional algorithms to perform the tasks needed [1-3].ML is an emerging area of importance for a wide range of applications. ML has become arevolutionary modern engineering tool to solve real-world engineering problems. It is essentialfor engineers to know how to apply machine learning algorithms to their large amount of datathat is generated by the sensors. Because of the availability of computing power, more and moreengineering problems have been reformulated and solved using this data-driven approach.The field of machine learning is growing
transition away from fossil fuels and towards renewable energy technologies is now fullyunderway. The most widely used renewable energy types are solar energy, wind power, andhydropower. A large majority of worldwide newly installed electricity capacity is nowrenewable [1-4]. In 2022, renewables accounted for 30% of global electricity generation and areprojected to reach over 42% by 2028 [5].The recent rapid global growth in renewable energy production has given rise to a demand forengineers with experience in this area. It is recognized the need for a large, well-trainedworkforce that can conduct research and development projects in renewables. Currently,renewable energy courses are not well represented in undergraduate academic programs.To prepare
produce the correct output) but ignore the qualityof their work. The traditional grading system lacks an emphasis on program logic, style, anddocumentation that is necessary for students to grow as programmers and succeed in their futurecareers.Alternative grading systems provide ways for instructors to create a feedback loop in theirclassroom that improves the quality of student work [1]. These systems include standards-basedgrading, specifications grading and ungrading [2], [3], [4], [5], [6], [7] . Each of these systemsattempts to change the meaning of grades and encourage students to produce higher qualitywork. The application and analysis of alternative grading in CS classrooms is still in its earlydays and additional work is needed to see
. This article will provide a snapshot of the state of the practice of how security isintegrated into program curricula by analyzing a subset of the ABET accredited ComputerScience programs. The article will identify at a high-level scope the topics that are covered inthe programs, as well as provide an overview of other aspects of the institutions which impactthe depth and breadth of security coverage available to undergraduate students.IntroductionThe term Computer Science first came about in 1961, coined by numerical analyst andcomputing pioneer George Forsythe [1]. The first computer science department was establishedat Purdue University in 1962, with other programs being created at Miami, Wisconsin, Illinois,and North Carolina shortly
and reconnect students in grades 7-14with in-person, hands-on activities in computing. The objectives were to: 1. Facilitate stronger identification of professional pathways in computing. 2. Facilitate stronger connection with the campus. 3. Educate those who may have a peripheral interest in computing as to the: a. Range of computing disciplines and professions. b. Real nature of computing. Our anecdotal observation is that present-day students are far more computer and technology literate as users of computer applications and technology, but have a surprisingly poor understanded of how computers work, are connected, and their information managed
pursue graduateeducation. Overall, this paper introduces a replicable methodology for analyzing curricula anddemonstrates its application through a case study of one institution’s computing programs.1 IntroductionThe rapid evolution of the job market, driven by artificial intelligence (AI) and automation, along-side shifting economic demands, underscores the need for an adaptable education system. Al-though educational institutions strive to equip students with the necessary knowledge for success-ful careers, many graduates struggle to land jobs that match their qualifications, even with the highdemand for tech talent. A 2024 study conducted by Hanson et al. [21] found that approximately37% of students in fields such as computer science (CS
engagementstrategies (LESs). These LESs include collaborative learning, gamification, and social interac-tion.We present the objectives of the project, describe how the objectives were met, briefly describeSEP-CyLE, and provide data showing students’ interactions with SEP-CyLE. The data retrievedfrom SEP-CyLE provides insight into how the learning environment was used, students’ perfor-mance on the learning objects, and the impact of the LESs on students’ overall performance in anintroductory cybersecurity course.Keywords: Cybersecurity Education, Cyberlearning Environment, Learning and Engagement Strate-gies, Learning Objects.1 IntroductionThe ubiquitous nature of information and communication technology (ICT) in the 21st centuryhas resulted in an upsurge
editing tool. We conducted an evaluation of this i360ºVR module with engineering studentson four key metrics: immersion, interactivity, the creation of a tangible learning environment,and student perception of coastal erosion. The results of this study offer valuable insights into therole of interactive, authentic VR environments in enhancing student engagement and learningoutcomes in engineering education. In addition, we discussed frameworks of applying theproposed i360oVR approach into two other STEM education contexts, including proposing aremote VR lab for the mechanical engineering program; and enhancing student learning inphysics education through an accident analysis of the August 2020 port explosion in Beirut,Lebanon.1. Background and
2017. ©American Society for Engineering Education, 2025 Immerse Students in AI-Infused Cybersecurity Through Software Process1. IntroductionCyberspace and the Internet have become an integral part of every nation, such as cities andcoastlines. They serve as the backbone for today's economy because we perform all of our dailyactivities, including shopping and banking, on the Internet. [1]. Due to the COVID-19 pandemic,all organizations were compelled to transition online and must now adjust to the "always-on"environment to maintain connectivity with their consumers [2].The expansion of the Internet, the rapid growth of cyberspace, and the always-on world have allplayed a significant role in the remarkable
experience and practical skills inthis critical area.IntroductionThe history of electric vehicles (EVs) dates to the early 19th century when inventors in Europeand the United States began experimenting with battery-powered transportation [1]. In the late1800s and early 1900s, EVs gained popularity, particularly in urban areas, due to their quietoperation and ease of use compared to gasoline-powered cars [2]. In that time, electric taxiswere used in cities like New York and London. However, the mass production of affordablegasoline vehicles, such as the Ford Model T, along with the expansion of fuel infrastructure, ledto the decline of EVs by the 1920s [3]. Interest in electric mobility resurged during the 1970s oilcrisis and again in the 1990s with
effectivehad tasks that were not easily divisible or lacked a clear goal to work towards. When a Scrumteam’s tasks were often small and took little time, it felt as though the Scrum overhead took moretime than the tasks themselves, making Scrum frustrating to follow.Keywords – Scrum, faculty service committees, departmental project managementI. IntroductionScrum has gained popularity for improving team productivity and customer satisfaction with thefinished product [1, 2]. It came to popularity in a software development environment, but thiswork explores its effectiveness for other teams that could benefit from a productivity boost,namely, university faculty service committees. Service is a key component of faculty positions inacademia that can
CS education. We recommend educatorsguide students in leveraging custom, context-specific assistants to improve learning and developcritical AI application skills.IntroductionLarge Language Models (LLMs) enable educational platforms to support students throughadvanced tools with real-time personalized feedback, guidance, and engagement mechanisms.By employing methods like retrieval-augmented generation (RAG), LLMs are increasingly ableto overcome challenges related to scalability and handling unexpected or unforeseen inputs, asare often experienced with intent-based chatbots [1]. RAG-powered assistants demonstratesignificantly improved performance in terms of response accuracy, adaptability, and studentsatisfaction [2].This study examines
improvements in code structure, readability, anddesign adherence, while also identifying limitations in current LLM capabilities.1. IntroductionOpen-source software (OSS) projects play a pivotal role in software engineering education by offeringstudents real-world coding experience. However, these projects often suffer from poor design and highmaintenance costs due to students' limited engagement and adherence to software design principles.Students, constrained by time and struggling to understand the codebase, often structure code poorly andplace functionality in the wrong classes, making the codebases harder to interpret and maintain. Thisstudy investigates the application of Large Language Models (LLMs), such as GPT-4, in enhancing OSSprojects. We
support reflective learning andcommunication in computing courses [1].The goal of this work is twofold: 1. Provide a retrospective analysis of a novel instructional model, offering sufficient detail for other educators to adopt, adapt, or extend the approach. 2. Demonstrate the effectiveness of this modified instructional approach in addressing stagna- tion in content delivery, preparing students for the rapidly evolving field of computer science.In a field as rapidly changing as computer science, modifications to the methods of instruction mayhelp intrinsically prepare students for this rapidly changing ecosystem.Theoretical FrameworkConstructivism as an educational theoretical framework has often been applied to the sub-field
exploration of non-traditional educational environments1. IntroductionSoftware engineering and STEM fields face persistent challenges with diversity, equity, andinclusion. For example, while women make up 56% of students enrolled in undergraduatedegrees, women account for only 22% of the students in engineering programs. This numberdrops even further in the workforce, where women comprise only 15.9% of the engineeringindustry [1]. Consequently, the industry of equity-focused coding education grew rapidly fromthe 2010s until 2024, leading to the creation of coding bootcamps, workshops, and community-based coding education specifically designed to increase the participation of women in tech [2],[3]. Recent funding shortages in 2024 and anti-DEI
Paper ID #45882GPS Spoofing on UAV Simulation using ArdupilotDavid Li, University of Maryland College ParkProf. Houbing Herbert Song, University of Maryland Baltimore County Houbing Herbert Song (M’12–SM’14-F’23) received the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, VA, in August 2012. He is currently a Professor, the Founding Director of the NSF Center for Aviation Big Data Analytics (Planning), the Associate Director for Leadership of the DOT Transportation Cybersecurity Center for Advanced Research and Education (Tier 1 Center), and the Director of the Security and