Paper ID #44303Reflections on 10 years of Operating a Computer-based Testing Facility: LessonsLearned, Best PracticesDr. Jim Sosnowski, University of Illinois Urbana-Champaign Jim Sosnowski is the Assistant Director of the Computer-Based Testing Facility (CBTF) at the University of Illinois Urbana-Champaign.Dr. Julie M Baker, University of Illinois Urbana-Champaign Julie Baker is a Learning Design Specialist for the Applied Technologies for Learning in the Arts and Sciences (ATLAS) group in the College of Liberal Arts and Sciences (LAS). She helps LAS faculty implement best practices for computer-based assessment and
://CSEdResearch.org.[26] Mazyar Seraj, Eva-Sophie Katterfeldt, Serge Autexier, and Rolf Drechsler. Impacts of creating smart everyday objects on young female students’ programming skills and attitudes. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education, pages 1234–1240, 2020.[27] Monique M Jethwani, Nasir Memon, Won Seo, and Ariel Richer. “i can actually be a super sleuth” promising practices for engaging adolescent girls in cybersecurity education. Journal of Educational Computing Research, 55(1):3–25, 2017.[28] Lauren E Margulieux, Briana B Morrison, Baker Franke, and Harivololona Ramilison. Effect of implementing subgoals in code. org’s intro to programming unit in computer science principles. ACM
Associate Director of Educational Innovation and Impact for UGA’s Engineering Education Trans- formations Institute (EETI). In addition to coordinating EETI’s faculty development programming, Dr. Morelock conducts research on institutional change via faculty development, with an emphasis on innova- tive ways to cultivate and evaluate supportive teaching and learning networks in engineering departments and colleges. He received his doctoral degree in Engineering Education at Virginia Tech, where he was a recipient of the NSF Graduate Research Fellowship. His dissertation studied the teaching practices of engineering instructors during game-based learning activities, and how these practices affected student motivation.Dr
outcomes and improve student engagement. The integration of AI tools has the potential to significantly impact student learning, bridging the gap between theoretical knowledge and practical application. This paper explores the impact of AI tools on student learning in engineering education, particularly in civil engineering. AI tools offer numerous benefits in engineering education, providing students with interactive and immersive learning experiences. These tools enable students to apply their theoretical knowledge in real-world scenarios, enhancing their understanding and problem-solving skills. A survey was distributed to engineering students in civil engineering courses to gather feedback on the effectiveness of using AI tools, allowing for
wide range of courses across the computer science curriculum and supervised undergraduate and graduate research. ©American Society for Engineering Education, 2024 Assessing the Impact of Open-Resource Access on Student Performance in Computer-Based Examinations Zulal Sevkli Computer Science and Software Engineering Miami University Oxford, OH sevkliaz@miamioh.eduAbstractThis study explored the effects of permitting digital resource access during computer-basedexams in the context of System Programming course. Two
differing perspectives based on thedocumented experiences of women along the Oregon and similar Overland Trails in the late1840s and early 1850s. Games were implemented using the Inform programming language,characterized by coding statements taking the form of complete sentences. This approachprovided a natural language syntax environment, making it inclusive for individuals outsidetraditional programming disciplines. To assess the course's effectiveness, pre- and post-activitysurveys with a Diversity, Equity, and Inclusion (DEI) focus were designed and administered. Thesubsequent statistical analysis revealed a significant positive impact, with a large effect sizedemonstrated in raising students' awareness of gender representation
Paper ID #37639Board 64: Work in Progress: Update on the Impact of Secure and UpgradeComputer Science in Classrooms through an Ecosystem with Scalability &Sustainability (SUCCESS)Prof. Lynette Michaluk, West Virginia University PI, is a social sciences researcher at the West Virginia University Center for Excellence in STEM Edu- cation. Her research interests include broadening access to and participation in STEM. She is Co-PI of the National Science Foundation KY-WV Louis Stokes Alliance for Minority Participation and Research Scientist for Secure and Upgrade Computer Science in Classrooms through an Ecosystem with
-2013), and a Postdoctoral Researcher at Clemson University (2013-2014) and the University of Florida (2014-2016). His research focuses on human-centered computing, computer science education, social computing, and broadening participation in computing. Dr. Dillon has received >$750k in research funding and awards from external agencies and non-profit organizations, including the National Science Foundation (NSF), the Maryland Pre-Service Computer Science Teacher Education Program (MCCE), and the Collaborative Research Experience for Undergraduates (CREU - CRA-WP). Dr. Dillon currently serves as a Co-PI for the STARS Computing Corps, which recently has been renewed for funding by NSF. He has also conducted a
Consortium. He is a Senior Member of the IEEE.Dr. Bruce R Maxim, University of Michigan, Dearborn Bruce R. Maxim has worked as a software engineer, project manager, professor, author, and consultant for more than forty years. His research interests include software engineering, human computer interaction, game design, virtual reality, AIXiaohong Yuan, North Carolina A&T State University Dr. Yuan is a professor in the Department of Computer Science at NCA&T. Her research interests include AI and machine learning, anomaly detection, software security, cyber identity, and cyber security education. Her research has been funded by the National Security Agency, the National Centers of Academic Excellence in
Mass Communica- tions from the University of South Florida and her B.S. in Global Strategic Communications from FIU. ©American Society for Engineering Education, 2023 Virtual Interview Training: Perceptions and Performance using Digital Hiring ManagersAbstractInterviewing for a job can be an intimidating experience for students and recent graduates. Manyindividuals may feel unprepared for their first interview and uncertain about what they could beasked. Having confidence and strong interview skills is very important for professionaldevelopment and career attainment. In this work, we describe a web-based platform designed toprovide experiential learning and interview practice for
upper-levelundergraduate and graduate students at the University of Illinois Urbana-Champaign. The datasetcontains a mix of 100 correct and 400 incorrect submissions and underwent an extensivefine-tuning process with OpenAI’s advanced GPT-3.5-turbo-1106 model [15]. Therefore, ourresearch questions include: • RQ1: How can a proof of concept be designed and implemented to assess the feasibility of utilizing a generative AI model for providing semantic error feedback in educational settings, ensuring that the system avoids disclosing correct answers while enhancing the learning experience? • RQ2: How does the feedback from the fine-tuned GPT model differ in specificity and relevance compared to standard GPT models in the
, “A Practical Strategy for Training Graduate CS Teaching Assistants to Provide Effective Feedback,” in Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1, (Turku Finland), pp. 285–291, ACM, June 2023.[10] D. Mirza, P. T. Conrad, C. Lloyd, Z. Matni, and A. Gatin, “Undergraduate Teaching Assistants in Computer Science: A Systematic Literature Review,” in Proceedings of the 2019 ACM Conference on International Computing Education Research, (Toronto ON Canada), pp. 31–40, ACM, July 2019.[11] E. Patitsas and P. Belleville, “What can we learn from quantitative teaching assistant evaluations?,” in Proceedings of the Seventeenth Western Canadian Conference on Computing Education
engineering from the New York Institute of Technology, Old Westbury, NY, USA, in 2016, and the B.S. degree in intelligent transportation engineering from Shanghai Maritime University, Shanghai, China, in 2014. He was Graduate Teaching Assistant for ECE1013 Foundations in ECE, ECE1022 Foundations in Design, ECE4713/6713 Computer Architecture, and ECE4753/6753 Introduction to Robotics at the undergraduate level and as a guest lecturer delivered graduate-level courses, ECE 8743 Advanced Robotics and ECE8833 Computational Intelligence. He received the ECE Best Graduate Researcher Award from the Department of Electrical and Computer Engineering, Mississippi State University in 2023. He received the Research Travel Award
working while in college.” U.S. News and World Report. (2020, December 30). https://www.usnews.com/education/best-colleges/paying-for- college/articles/weighing-the-pros-and-cons-of-working-while-in-college. Retrieved February 2, 2023.[15] A. Lerner. “The Technical Interview Practice Gap, and how it keeps underrepresented groups out of software engineering.” Interview.io. https://interviewing.io/blog/technical- interview-practice-gap. Retrieved February 1, 2023.TECHNICAL INTERVIEW INTEGRATION[16] S. Lunn, M. Ross, Z. Hazari, M. A.Weiss, M. Georgiopoulos, & K. Christensen. “The impact of technical interviews, and other professional and cultural experiences on students' computing identity.” Proceedings of the 26th ACM
Practices and Processes,” Hollylynne S. Lee etel. developed a framework using the work of statistics educators and researchers to investigatehow data science practices can inform work in K–12 education. Their framework buildsfundamental practices and processes from data science [19]. The math field has contributed to data science research via the Common Core StateStandards Initiative (CCSSI), which is a joint project to develop common K–12 reading andmath standards designed to prepare students for college and careers. The CCSSI includes a datascience section for elementary students that focuses on data collection, data type, function,analysis type, and sample [20]. Similarly, the Launch Years Data Science Course Frameworkprovides broad
) tools come online, technical writing instruction is poised tocreate new applied projects, teaching students to use ML constructively, objectively evaluate MLoutput, and refine final products faster. STEM researchers are already publishing their use ofChat GPT-adjacent language tools in high impact scientific outlets like Nature. Engineeringstudents need exposure and to develop competency in using these tools. ML can supporttechnical writing by proofreading content; suggesting novel syntactic structures; producingusable content faster; and upskilling writers in the process. This paper presents the use of fourML tools, applied in service to a series of technical writing and communication projectsappropriate for sophomore-junior level students
vision centeredon outcomes for students and support for faculty is needed to ensure that HyFlex is as effectiveas possible.7. References[1] A. Miller, “Institutional Practices That Facilitate Bachelor’s Degree Completion for Transfer Students,” in Collegiate transfer: Navigating the new normal, San Francisco, CA: Jossey-Bass, 2013, pp. 39–49.[2] M. M. Abdelmalak and J. L. Parra, “Expanding learning opportunities for graduate students with HYFLEX course design,” International Journal of Online Pedagogy and Course Design, vol. 6, no. 4, pp. 19–37, 2016.[3] S. Binnewies and Z. Wang, “Challenges of Student Equity and Engagement in a HyFlex Course,” in Blended Learning Designs in STEM Higher Education: Putting Learning
Paper ID #38310Engaging Engineering Students through Environmental Data ScienceDr. Mary Kay Camarillo, University of the Pacific Dr. Mary Kay Camarillo is an Associate Professor of Civil Engineering at the University of the Pacific in Stockton, CA. She has a PhD in Civil & Environmental Engineering from the University of California, Davis and is a licensed Professional Engineer in California (Civil). Prior to working in academia, Dr. Ca- marillo worked in the consulting industry, designing and overseeing construction of water and wastewater infrastructure. Her research interests include environmental impacts of energy
example,[11] conducted a comprehensive survey of 65 collaboration researchers around the world. Itelicited diverse perspectives on the evolving role of AI in team collaboration, emphasizing theneed for a systematic understanding of team, task, and work practice design in the context ofhuman-AI collaboration. Furthermore, it calls for AI systems that can proactively capture, adjust,and coordinate their responses according to complex contextual nuances, similarly raised by otherrecent works [12, 13, 14, 15, 16].While the present state of AI, including genAI, may not fully embody the ideal envisioned bythese works, it is crucial to recognize that genAI’s generation capability, empowered by largetraining data and pre-trained models, stands as a
technology is chosen as a medium for teaching CT, it is recommended that teachers beaware of the best practices for using technology around children. This will help ensure that thetechnology used is safe, age-appropriate, and aligns with the curriculum's learning goals [32].Limitations and Future WorkThe CTPF+ frameworks based on the systematic review collected from ProQuest. Therefore,works that can provide different insight into this research may have been missed. Also, mostliterature reviews build their work on Brennan and Resnick, which can lead to bias as itinfluences all the author’s views. Other limitations are the limited work for data science, and AIinfers the need to have more research to influence the judgments, and the inclusion of CT
broaderset of educational applications for ChatGPT – including areas like finance, language, medicine,and law – and catalogued several applications of ChatGPT, including identifying student needs,scaling assessment, personalized tutoring, and generating material. Although the findings fromthese reviews – including others such as [14], [15], [16], [17], [18] – can help conceptualize thedifferent possibilities, guidance about how to implement LLM-powered tools like ChatGPT isunderstandably sparse across these literature reviews.The best practices for using LLM-powered tools in educational research are developing as well,specifically how we go about unlocking their proclaimed benefits. The key is determining whatprompts and practices can be used to
number of intended exercises.4. The experimentThe research design was quasi-experimental with a quantitative approach, where an experimentalgroup (EG) and two control groups (CGs) carried out a post-test. Time Number Average Average grade consumed to Group Teacher of Question grade per for the two solve each students exercise exercises
technological age, the need to study and understand computation and the scholarship andteaching employed to prepare the next generation of engineers has become a priority for currenteducation researchers. The National Academies of Sciences, Engineering, and Medicine,reported in a 2018 report by stating, “It is a time for institutions to consider their missions andconstituencies they serve and to determine what role computing should play in the experience,knowledge, and skills of its graduates 2025 and beyond,” [1]. Computing has been identified as anecessary skillset for engineers entering the workforce to employ computational solutions tocomplex global issues. Computing educational researchers have embarked on the journey touncover the evidence-based
in Science and as Associate Director, Engineering Education Research Center at the University of Pittsburgh; Director of Research & Development for a multimedia company; and as founding Director of the Center for Integrating Research & Learning (CIRL) at the National High Magnetic Field Laboratory. His current efforts focus on innovation of teaching practices in STEM fields and systemic change within higher education.Dr. Ibukun Samuel Osunbunmi, Pennsylvania State University Ibukun Samuel Osunbunmi is an Assistant Research Professor, and Assessment and Instructional Specialist at Pennsylvania State University. He holds a Ph.D. degree in Engineering Education from Utah State University. Also, he has BSc and
, research, and teaching.Considering these challenges, there is an urgent need for empirical studies to assess the impact ofGAI on engineering learning experiences to address the potential challenges and concerns relatedto their implementation. This study aims to inform the field about the best practices forintegrating GAI tools into engineering education pedagogy and assessment.Purpose of this studyThis work-in-progress paper aims to describe our efforts to explore the impact of integrating GAIas a tool for enhancing engineering education. In this paper, we will discuss the methodology weplan to use to assess the impact of GAI tools on engineering learning experiences, including theselection of participants, data collection methods, and analysis
students in the United States.Despite this growing interest, retention and graduation rates are a concern for many regional publicuniversities such as Farmingdale State College (FSC). Educational researchers have demonstratedthe benefits of increasing student sense of belonging (SoB) and academic self-concept (ASC) onacademic outcomes. This study explores the interaction between implementing collaborativelearning techniques (CoLT) in a CSC 101 Introduction to Computing course with students’ SoBand ASC. Given the social constructivist perspective that frames CoLTs and these techniques’ability to engage students authentically in course content, the implementation of CoLTs ishypothesized to positively impact students’ SoB and ASC. Students in the
, B. M. (2009, October). Examining science and engineering students' attitudes toward computer science. In 2009 39th IEEE frontiers in education conference (pp. 1-6). IEEE.[15] Guzdial M. Does contextualized computing education help? ACM Inroads. 2010 Dec 1;1(4):4–6.[16] Yardi, S. and Bruckman, A. 2007. What is computing?: bridging the gap between teenagers' perceptions and graduate students' experiences. In Proceedings of the Third international Workshop on Computing Education Research (Atlanta, Georgia, USA, September 15 - 16, 2007). ICER '07. ACM, New York, NY, 39-50. DOI=http://doi.acm.org/10.1145/1288580.1288586[17] Jonassen, D. H. (2000). Revisiting activity theory as a framework for designing student-centered learning