applications in ethical development, equity and accessibility issues (e.g. web scraping) o Future-proofing o Economics o Project management and team software processes o Communication skills o Conflict resolution o Individual software processes, resiliency, self-reflection, self-assessment o Revision control and use of tools o Innovation and creative capacity o Entrepreneurial mindsetOverall, students and faculty both wanted to see more diversity in senior technical electives.These courses also allow students to specialize in specific areas of interest since it is not possibleto cover the immense breadth of software engineering in a limited timeframe. Students, faculty,and
multinational companies, obscuring the“sociopolitical implications, relevance, and ultimately, liberatory possibilities of teaching andlearning CS” [12, p. 27)] Unlike some K-12 and university coding education models, the codingworkshops studied here are framed within more nuanced conversations about equity and ethics intechnology, countering deficit discourses about marginalized learners and offering a vision ofcoding education grounded in “antiracism and justice” [12, p.36].One of the first inclusive pedagogy strategies we noticed was the relatively expansive view ofaccessibility held by coding workshop organizers and instructors. In our experience, traditionaluniversity software engineering education generally thinks of accessibility in terms
: Teamwork Project Management Research & Development CommunicationThese are the most important skill areas to the success of an engineer.Capstone I Course (ECE 4900)The course description for ECE 4900 is as follows:This course focuses on team-oriented design projects and technical writing by incorporatinggroup projects, oral presentations and written reports. Incorporates engineering standards andrealistic constraints including economic, environmental, sustainability, manufacturability,ethical, health and safety, social, and political. Emulates the problems encountered by engineersworking in commercial, industrial, and governmental entities.The Course Objectives for this course is as follows: Plan an engineering project
ofAI tools raises concerns about plagiarism rates and the ethical use of technology in academicsettings. Educational institutions are actively crafting policies to navigate the complexities ofGenAI usage while maintaining academic integrity [2], [3].Recent advancements in GenAI have ushered in a new era for educational methodologies,offering innovative tools for learning and teaching. Integrating GenAI tools such as ChatGPTand MidJourney into educational practices is becoming increasingly common, with these toolspredicted to become as ubiquitous as traditional software like Microsoft Excel in the near future[4]. The emergence of GenAI necessitates reevaluating pedagogical strategies, suggesting a shifttowards technology-integrated learning
researchand academic writing, as well as for idea and code generation [5].Similar to any technological advancement, there are educational challenges inherent in the use ofLLMs, including students' overreliance, issues of plagiarism, and biases in the generated content[21]. These challenges highlight the need for policies and guidelines towards the responsible useof LLMs. Educational institutions are actively engaged in deliberations to determine the mosteffective strategies for incorporating LLMs into their curricula [8]. As institutions grapple withthis decision, there have been noteworthy efforts to provide guidance on the ethical use of LLMs[8],[22],[9],[23]. Researchers caution against implementing "one-size-fits-all" policies butadvocate for the
chatbot functionality to specific course requirements to maximize effectiveness.Earlier work by Bender et al. [8] provided the groundwork for understanding the limitations ofgeneral-purpose chatbots in specialized learning environments.Future of Education: AI and MOOCsVerma et al. [9] examine the role of AI in enhancing MOOCs, emphasizing personalized learningand automated feedback. AI-powered tools have been shown to significantly improve learnerretention and engagement by tailoring content to individual needs. However, ethical concernssuch as data privacy and algorithmic bias remain critical. Verma et al. (2024) draw on theanalytics framework proposed by Kumar et al. [10], which emphasizes proactive interventionstrategies in MOOC platforms.In
engineer.Capstone I Course (ECE 4900)Capstone I is the first course in the two-part senior design sequence. In this course, studentscomplete several key milestones: 1. Team formation 2. Project proposal and approval 3. Project planning 4. Procurement of hardware components 5. Proof of concept for critical circuits 6. Preliminary Design Review (PDR)Topics covered include: Engineering design methodology Project selection and need Identification Requirement specification development Concept generation and evaluation Team dynamics and collaboration Ethics and legal considerations in engineering Basics of Engineering EconomicsEach team meets weekly with a faculty advisor to review progress
computer science and engineering from the University of Minnesota. Next, she was a Postdoctoral Fritz Family Fellow with the Massive Data Institute of McCourt School of Public Policy at Georgetown University, Washington, DC. She is involved in projects in the intersection of education, data mining, machine learning, ethics, and fairness. Her research interests include data mining, recommender systems, predictive models within educational contexts, and the fairness concerns that arise from their use. Her goal is to help students succeed using data and machine learning models.Dr. Christine Lisetti, Florida International University Christine Lisetti is an Associate Professor at Florida International University (FIU) in
of cybersecurity principles and concepts,as well as cybersecurity tools. The course was offered in Spring 2019 and Spring 2020. The cy-bersecurity LOs were integrated into the coursework and contributed to students’ overall grades.The student learning outcomes for the course are as follows. The students should be able to: • Describe the fundamental cybersecurity principles, protocols, and standards; • Identify some of the common problems and solutions in the cybersecurity domain; • Use cybersecurity tools and operations to implement cybersecurity principles and protocols; • Analyze cybersecurity breaches and provide appropriate solutions; • Describe cybersecurity hygiene, ethics, auditing, and management of software
rubrics.MethodsThis paper is part of an ongoing project to investigate how systems thinking can be used incombination with popular threat modeling frameworks like STRIDE to teach and assesscomponent-level and system-level threat modeling to upper-level software engineering students.In this section, we provide an overview of the methods we used in our study. We begin bydescribing the software engineering course where we piloted our study. Next, we discuss our datacollection strategy, introduce the pilot version of our rubric, our data analysis approach (scoringstrategy using our rubric), and ethical considerations.Data collectionTo answer our research question, we collected data on the students’ team projects. In the project,student teams had to deliver the