Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Christopher Allen Calhoun, University of Cincinnati; David Reeping, University of Cincinnati; Siqing Wei, University of Cincinnati; Aarohi Shah, University of Cincinnati

Tagged Divisions

Computers in Education Division (COED)

&lr=&id=GkaTDAAAQBAJ&oi=fnd&pg=PP1&dq=Qualitati ve+Research+in+STEM:+Studies+of+Equity,+Access,+and+Innovation.&ots=WBqDJY5uim&sig=_ 772GNzIiWHfP7IzvI1SPbQH6Pk[10] S. W. Tabsh, H. A. El Kadi, and A. S. Abdelfatah, “Faculty perception of engineering student cheating and effective measures to curb it,” in 2019 IEEE Global Engineering Education Conference (EDUCON), IEEE, 2019, pp. 806–810. Accessed: Jan. 09, 2025. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8725199/?casa_token=iN2QmiVVNVMAAAAA:X1G RG9aX8BkH2Jg3d56YHarCGv8k_9IlMwNVOO545dyQqfklqb5MsKyRxPLQJB3CSEcoW-HNSw[11] E. F. Gehringer and B. W. Peddycord, “Teaching strategies when students have access to solution

Conference Session

Computers in Education Division (COED) Track 6.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Abdulrahman AlRabah, University of Illinois at Urbana - Champaign; Zepei Li, University of Illinois at Urbana Champaign; Meredith Blumthal, University of Illinois at Urbana - Champaign; Sotiria Koloutsou-Vakakis, University of Illinois Urbana-Champaign; Volodymyr Kindratenko, University of Illinois Urbana-Champaign; Tomasz Kozlowski, University of Illinois Urbana-Champaign; Abdussalam Alawini, University of Illinois Urbana - Champaign

Tagged Divisions

Computers in Education Division (COED)

the code for HW4.5” (EPS) df (x) • solve dx = −a · f (x) + b (NEF) 2. Assignment Copy-Paste Queries: Students often pasted entire assignment questions or instructions directly into the AI-bot, seeking solutions. Due to the length and specificity of these queries, as well as their nature, it is not feasible to provide detailed examples here. However, this behavior was observed frequently across multiple courses.5 DiscussionIn our research, we investigated the use of a Generative AI for educational support, our researchincludes four primary questions. For RQ1, we explored which type of category students usuallylook for help. In comparison with programming

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Thomas Rossi, University of New Haven; Ekaterina Vasilyeva, University of New Haven; Ren Oberdorfer, University of New Haven; Jhansi Sreya Jagarapu, University of New Haven

Tagged Divisions

Computers in Education Division (COED)

the physical safety of a person or others around them, whichincludes an option for if the user is unsure. Finally, the list of resources is categorized by student,faculty, and emergency resources. The user can navigate this information by using tabs at the topof the page, which includes a tab that lists all resources. Some of the app functionality can beseen in the figures below. (a) Warning Signs (b) Emergency ResourcesFigure 1: App Functionality to Identify Warning Signs and Direct User to Appropriate Resources Figure 2: Resources SectionWhile the existing features of ChargerCare represent significant progress in solving the issuesidentified among the university

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kangxuan Rong, Cornell University; Campbell James McColley, Cornell University; Ted Karanja Mburu, University of Colorado Boulder; Alexandra Werth, Cornell University

Tagged Divisions

Computers in Education Division (COED)

Appendix B for the complete transcript). Despite the benefits of AI-generated prompts, some issues persist, notably withrepetitious phrases. As seen in Summary of Reflection 3, repetitive use of similar languagereduced student involvement and the depth of responses. In Question 2, the word “equalcontribution” produced a detailed and thoughtful response (R2) with specific examples.However, the AI used the same phrase in future questions (their remarks in Q4: “This answer isshort because I have typed a very similar response for the past 3 question Q3-Q5), phrasing it toquestions associated with collaboration, decision-making, consensus-building, andproblem-solving. This lack of variance resulted in repeated responses, restricting the

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jinyi Jiang, Nanyang Technological University; Ibrahim H. Yeter, Nanyang Technological University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

., Moro, A., Bergram, K., Purohit, A., Gillet, D., & Holzer, A. (2020). Bringing Computational Thinking to non-STEM Undergraduates through an Integrated Notebook Application. https://ceur-ws.org/Vol-2676/paper2.pdfFunk, C. (2018, January 9). Women and Men in STEM Often at Odds Over Workplace Equity. Pew Research Center. https://www.pewresearch.org/social-trends/2018/01/09/women-and-men-in-stem-often-at -odds-over-workplace-equity/Jackson, C., Mohr-Schroeder, M. J., Bush, S. B., Maiorca, C., Roberts, T., Yost, C., & Fowler, A. (2021). Equity-Oriented Conceptual Framework for K-12 STEM literacy. International Journal of STEM

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Zifeng Liu, University of Florida; Yukyeong Song, University of Florida; Qimao Yang, University of Florida; Wanli Xing, University of Florida; Jing Guo, University of Florida

Tagged Divisions

Computers in Education Division (COED)

) gates, whichare shown in Figure 1a. These simulations are essential for understanding the foundationaloperations of QC, as they form a universal gate set. Figure 1b shows the user interface of the toolwhich allows users to manipulate parameters such as magnetic field values, dephasing times, andinitial quantum states, enabling a comprehensive exploration of quantum phenomena. Thesimulations are based on the Lindblad Master Equation (LME) [41], which accounts for thedecoherence effects that occur in quantum systems. (a) (b)Figure 1: Spin qubit array and device parameter configuration interface. Note: (a) The spin qubitarray consists of a single-qubit rotational gate and a two

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Joel Nirupam Raj; Ashwath Muppa, Thomas Jefferson High School for Science and Technology; Rhea Nirmal; Teo W. Kamath; Achyut Dipukumar; Aarush Laddha; Mihai Boicu, George Mason University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

. Sample code initiallysubmitted by researcher 1 is provided in Appendix B Initial Python Solution Sample [13]. This isthe simplest problem and is provided as an example and has around 42 lines including code andcomments. These programs were submitted to Claude Sonnet 3.5 to receive a grade and feedbackin the following categories: “Correctness,” “Efficiency,” “Data Structures,” “Code Readability,”and “Testing.” The prompt used to call Claude Sonnet 3.5 is provided in Appendix D. Promptspecifying the grading rubric included in Appendix C. Rubric (provided as a PDF document withthe prompt). The prompt is very specific with respect to how the feedback must be provided,following the rubric specifications and has 72 lines. The sample feedback received

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

MALEK EL KOUZI, Queen's University; Omar I.M Bani-Taha, Carleton University; Richard Reeve, Queen's University

Tagged Divisions

Computers in Education Division (COED)

Applications,” Educ. Technol. Soc., vol. 17, no. 4, pp. 1176–3647, 2014.[4] L. Johnson, S. Adams Becker, V. Estrada, and A. Freeman, “The NMC Horizon Report: 2015 Higher Education Edition.,” New Media Consortium. 6101 West Courtyard Drive Building One Suite 100, Austin, TX 78730. Tel: 512-445- 4200; Fax: 512-445-4205; Web site: http://www.nmc.org, 2015.[5] P. D. Petrov and T. V. Atanasova, “The Effect of augmented reality on students’ learning performance in stem education,” Inf., vol. 11, no. 4, Apr. 2020.[6] O. B. Petrovych, A. P. Vinnichuk, V. P. Krupka, I. A. Zelenenka, and A. V Voznyak, “The usage of augmented reality technologies in professional training of future teachers of Ukrainian

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Eesha tur razia babar; Ahmed Ashraf Butt, University of Oklahoma

Tagged Divisions

Computers in Education Division (COED)

work will expand toregression problems and incorporate local interpretability techniques like LIME and Eli5.References [1] O. Scheuer and B. M. McLaren, “Educational data mining,” in Encyclopedia of the Sciences of Learning, Boston, MA: Springer US, 2012, pp. 1075–1079. [2] F. Alshareef, H. Alhakami, T. Alsubait, and A. Baz, “Educational Data Mining Applications and Techniques,” International Journal of Advanced Computer Science and Applications, vol. 11, 2020. [3] T. Zarsky, “Transparency in data mining: From theory to practice,” in Studies in Applied Philosophy, Epistemology and Rational Ethics, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 301–324. [4] S. Roy and A. Garg, “Predicting academic performance of

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Johannes Kubasch, University of Wuppertal; Dominik May, University of Wuppertal; Doha Meslem, Bergische Universität Wuppertal

Tagged Divisions

Computers in Education Division (COED)

tools in chemical engineering education: The needs and thedesires," Education for Chemical Engineers, vol. 44, pp. 63–70, 2023. [Online]. Available:https://doi.org/10.1016/j.ece.2023.05.002J. M. Gayed, M. K. Carlon, A. M. Oriola, and J. S. Cross, "Exploring an AI-based writingassistant’s impact on English language learners," Computers and Education: ArtificialIntelligence, vol. 3, 100055, 2022. [Online]. Available:https://doi.org/10.1016/j.caeai.2022.100055J. P. Bernius, S. Krusche, and B. Bruegge, "Machine learning based feedback on textualstudent answers in large courses," Computers and Education: Artificial Intelligence, vol. 3,100081, 2022. [Online]. Available: https://doi.org/10.1016/j.caeai.2022.100081L. Appels, S. De Maeyer, and P. Van

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Rawan Adnan Alturkistani, Virginia Tech Department of Engineering Education; Mohammed Seyam, Virginia Polytechnic Institute and State University

Tagged Divisions

Computers in Education Division (COED)

Paper ID #48429BOARD # 76: Pedagogical Agents in the age of Generative Artificial Intelligence:Opportunities and Challenges in K-12 STEM Education.Mrs. Rawan Adnan Alturkistani, Virginia Tech Department of Engineering Education Rawan Alturkistani is a Ph.D. student in Computer Science at Virginia Tech. She holds a Master’s degree in Computer Science from Bowling Green State University (BGSU). Her research focuses on the intersection of artificial intelligence and education, with a particular interest in the design and impact of pedagogical agents in technology-enhanced learning environments. She is currently exploring the

Conference Session

Computers in Education Division (COED) Track 3.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xiangqun Zhang, Syracuse University; Ziyang Jiao, Syracuse University; Farzana Rahman, Syracuse University; Bryan Kim, Syracuse University

Tagged Divisions

Computers in Education Division (COED)

0.8 0.6 0.6 CDF CDF 0.4 0.4 0.2 0.2 0.0 0.0 0 20 40 60 80 100 0 20 40 60 80 100 (a) Lines of Code (LoC) (b) Assignment/Exam GradeFigure 2: CDF of LoC and grades. Note: exam grade CDFs contain grades of empty responses,whereas assignment grade CDFs only contain the grades of those who submitted. 3,4the 18 students who finish

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ratana Prinyawiwatkul; brian Warren, Southern University and Agricultural & Mechanical College; Shuju Bai, Clayton State University; Albertha Hilton Lawson, Southern University and A&M College

Tagged Divisions

Computers in Education Division (COED)

266 100.0* Final grades: pass (A, B, and C) / fail (D, F, and W)Data AnalysisThe data was coded and input into SPSS 23.0 and diverse statistical techniques were performed asspecified in Table 5.Table 5. Research Questions and Statistical Analyses Used in the Study Research Question Statistical Analyses RQ1: Is there a relationship between • Conduct a Chi-square test for students’ ages and students’ achievements independence in Computer Literacy courses? • Independent variable: age • Dependent variable: Computer Literacy course final grade

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ahmed Ashraf Butt, University of Oklahoma; Saira Anwar, Texas A&M University; Asefeh Kardgar, Texas A&M University

Tagged Divisions

Computers in Education Division (COED)

. 1–26. doi: 10.4018/978-1-4666-1809-1.ch001.[12] R. M. Harden, “Learning outcomes and instructional objectives: is there a difference?,” Med. Teach., vol. 24, no. 2, pp. 151–155, 2002, doi: 10.1080/0142159022020687.[13] M. Lamm, “Know Where You Are Going! Simple Steps to Writing SMART Learning Objectives.” [Online]. Available: https://ctl.jhsph.edu/blog/posts/SMART-learning- objectives/#:~:text=Learning%20objectives%20should%20be%20a,time%2Dbound%20(SMA RT)[14] J. B. Biggs and C. Tang, Teaching For Quality Learning At University. Maidenhead: McGraw- Hill Education, 2011.[15] D. Kennedy, Writing and using learning outcomes: a practical guide. Cork: University College Cork, 2006. [Online]. Available: https

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Duncan Johnson, Tufts Center for Engineering Education and Outreach; Ethan E Danahy, Tufts University; Elliot Benjamin Roe, Georgia Institute of Technology

Tagged Divisions

Computers in Education Division (COED)

students a UV flashlight to reveal the writing on cards one at a time, asshown in Figure 3 (a). Second, we generate secret anaglyph messages that obscure words in a seaof colors [6]. Students will then reveal data by looking at a card through a red-tinted magnifyingglass, as shown in Figure 3 (b). Another planned improvement is to replace the string connectorswith rigid, hinged connectors. (a) Shining a UV light reveals the word ”Apple” (b) Looking through a red-tinted magnifying glass written in invisible ink on the card. reveals the word ”Earth” from the sea of colors. Figure 3. Future ideas for obscuring all but one card at a time.To extend the “unplugged” nature of the activity, we plan to use

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Maria Elena Truyol, Universidad Andres Bello, Santiago, Chile; Monica Quezada-Espinoza, Universidad Andres Bello, Santiago, Chile; Genaro Zavala, Tecnologico de Monterrey, Monterrey, Mexico; Universidad Andres Bello, Santiago, Chile; Claudia Bascur, Universidad Andres Bello, Santiago, Chile

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

institutions.AcknowledgmentThe authors gratefully acknowledge the leadership and financial support of the School ofEngineering at the Universidad Andres Bello, Chile.References[1] H. C. Chu, G. H. Hwang, Y. F. Tu, and K. H. Yang, “Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most- cited articles,” Australasian Journal of Educational Technology, vol. 38, no. 3, pp. 22–42, 2022, doi: 10.14742/ajet.7526.[2] H. Crompton and D. Burke, “Artificial intelligence in higher education: the state of the field,” International Journal of Educational Technology in Higher Education, vol. 20, no. 1, p. 22, 2023, doi: 10.1186/s41239-023-00392-8.[3] T. Pham, T. B. Nguyen, S. Ha, and N. T. Nguyen Ngoc

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Michaela Harper, Utah State University; Cassandra J McCall, Utah State University; Daniel Kane, Utah State University; Wade H Goodridge, Utah State University; Linda Davis Ahlstrom, Utah State University; Oenardi Lawanto, Utah State University

Tagged Divisions

Computers in Education Division (COED)

Pitfalls of Generative AI for Education," in 2023 IEEE Global Engineering Education Conference (EDUCON), Kuwait, 2023.[4] B. du Boulay, "Artificial Intelligence as an Effective Classroom Assistant," IEEE Intelligent Systems, vol. 31, no. 6, pp. 76-81, 2016.[5] J. Dempere, K. Modugu, A. Hesham and L. K. Ramasamy, "The impact of ChatGPT on higher education," Frontiers in Education, vol. 8, 2023.[6] C. Karthikeyan, "Literature Review on Pros and Cons of ChatGPT Implications in Education," 2023.[7] D. Mhlanga, "Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning," SSRN, 2023.[8] I. Tuomi, Y. Punie, R. Vuorikari and M. Cabrera, "The Impact of Artificial Intelligence on Learning, Teaching, and

Conference Session

Computers in Education Division (COED) Track 5.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Nathan H Bean, Kansas State University; Joshua Levi Weese, Kansas State University; Russell Feldhausen, Kansas State University; Michelle Friend, University of Nebraska - Omaha; Friday Emmanuel James, Kansas State University; David S. Allen, Kansas State University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

several contextsto help foster and grow their interest [10].This perspective provides a good framing mechanism for exploring disparities in computerscience learning opportunities between rural and non-rural populations. A weakness in onecontext might not have a large impact, but issues across multiple learning contexts will likelyhave an outsized effect on students’ opportunities and goals. Thus, if we find disparities inmore than one learning context, we make a stronger case for recognizing rural populations asunderserved. With this understanding, our research question becomes: RQ1: Are rural US students provided fewer opportunities to engage with computer science through: a) the school context? b) the community context? c) the distributed

Conference Session

Computers in Education Division (COED) Track 5.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Friday Emmanuel James, Kansas State University; Joshua Levi Weese, Kansas State University; Nathan H Bean, Kansas State University; Russell Feldhausen, Kansas State University; Michelle Friend, University of Nebraska - Omaha; Robert Stewart, Kansas State University; Carrie Grace Aponte; David S. Allen, Kansas State University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

chil- dren’s cognition.” Journal of educational psychology, vol. 76, no. 6, p. 1051, 1984. doi: 10.1037/0022-0663.76.6.1051 . [7] M. U. Bers, L. Flannery, E. R. Kazakoff, and A. Sullivan, “Computational thinking and tinkering: Exploration of an early childhood robotics curriculum,” Computers & education, vol. 72, pp. 145–157, 2014. doi: 10.1016/j.compedu.2013.10.020 . [8] T. Camp, W. R. Adrion, B. Bizot, S. Davidson, M. Hall, S. Hambrusch, E. Walker, and S. Zweben, “Generation cs: the growth of computer science,” ACM Inroads, vol. 8, no. 2, p. 44–50, May 2017. doi: 10.1145/3084362 . [9] J. R. Warner, J. Childs, C. L. Fletcher, N. D. Martin, and M. Kennedy, “Quantifying disparities in computing education: Access

Conference Session

Computers in Education Division (COED) Track 4.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xiuhua Si, California Baptist University; Keith Hekman, California Baptist University; Jinxiang Xi

Tagged Divisions

Computers in Education Division (COED)

Paper ID #47485Enhancing Engineering Learning through MathCADDr. Xiuhua Si, California Baptist University Dr. Xiuhua (April) Si is a Professor of aerospace and mechanical engineering at California Baptist University. Her broad research interests include engineering education, thermal fluid science, and composite materials application. She has published over fifty peer-reviewed journal and conference papers and had multiple presentations at engineering conferences and meetings.Dr. Keith Hekman, California Baptist University Dr. Keith Hekman is a full professor in Mechanical Engineering. He has been at California Baptist

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Marshall Ismail, Worcester Polytechnic Institute; Devin Kachadoorian, Worcester Polytechnic Institute; Sahil Mirani, Worcester Polytechnic Institute; D. Matthew Boyer, Clemson University; Tim Ransom, Clemson University; Ahmet Can Sabuncu, Worcester Polytechnic Institute

Tagged Divisions

Computers in Education Division (COED)

chatbot, its value in resolving complex engineering problems,and its potential for scalability across different courses. The questions below guide itsdevelopment and refinement: 1.​ In what capacity can Socratic questioning and other engineering troubleshooting techniques (such as decision trees) be used to develop a chatbot to assist students in engineering classes? 2.​ In what ways do students use the AI chatbot in a classroom setting? 3.​ How practical and useful is an AI chatbot when used as an engineering tool in a classroom setting, and can future implementations be further developed? a.​ How can it be used in the same course? b.​ How can it be expanded upon to be used in different courses

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Hannah Oluwatosin Abedoh, Morgan State University; Blessing Isoyiza ADEIKA, Morgan State University; Pelumi Olaitan Abiodun, Morgan State University; Oludare Adegbola Owolabi P.E., Morgan State University; Abiola Olayinka Ajala, Morgan State University; OLUWATOYOSI OYEWANDE, Morgan State University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

Paper ID #48972BOARD # 77: Perception of the Impact of Generative Artificial Intelligenceon EducationMrs. Hannah Oluwatosin Abedoh, Morgan State University Hannah Abedoh is a highly motivated doctoral student in Business Management, specializing in Information Science and Systems. She is actively engaged in advanced research, focusing on the impact of Generative Artificial Intelligence on learning.Blessing Isoyiza ADEIKA, Morgan State University Blessing Isoyiza ADEIKA is a Ph.D. student in Computer and Electrical Engineering at Morgan State University, with a strong focus on neuroscience and artificial intelligence. She

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Joshua Coriell, Louisiana Tech University; Ankunda Kiremire, Louisiana Tech University; Krystal Corbett Cruse, Louisiana Tech University; William C. Long, Louisiana Tech University

Tagged Divisions

Computers in Education Division (COED)

Section Conference, Universityof Nebraska-Lincoln, Lincoln, Nebraska. 10.18260/1-2-119-46353[3] Haikal, T., & Lightfoot, R. H. (2024, March), Enhancing Education Through ThoughtfulIntegration of Large Language Models in Assigned Work Paper presented at 2024 ASEE-GSW,Canyon, Texas. 10.18260/1-2--45377[4] Paustian T and Slinger B (2024) Students are using large language models and AI detectorscan often detect their use. Front. Educ. 9:1374889. doi: 10.3389/feduc.2024.1374889[5] F. A. Pirzado, A. Ahmed, R. A. Mendoza-Urdiales and H. Terashima-Marin, "Navigating thePitfalls: Analyzing the Behavior of LLMs as a Coding Assistant for Computer ScienceStudents—A Systematic Review of the Literature," in IEEE Access, vol. 12, pp. 112605-112625,2024, doi

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Sumaya Binte Zilani Choya, George Mason University; Mihai Boicu, George Mason University

Tagged Divisions

Computers in Education Division (COED)

analysis will improvescalability and facilitate wider use in online learning.References[1] A. Aristovnik, K. Karampelas, L. Umek, and D. Ravšelj, "Impact of the COVID-19 pandemicon online learning in higher education: a bibliometric analysis," Front. Educ., vol. 8, Article1225834, Aug. 2023, doi: 10.3389/feduc.2023.1225834.[2] O. L. Holden, M. E. Norris, and V. A. Kuhlmeier, "Academic Integrity in OnlineAssessment: A Research Review," Front. Educ., vol. 6, Art. no. 639814, Jul. 2021, doi:10.3389/feduc.2021.639814.[3] E. Toprak, B. Özkanal, S. Aydin, and S. Kaya, "Ethics in E-Learning," Turkish OnlineJournal of Educational Technology, vol. 9, no. 2, pp. 78, Apr. 2010.[4] W. Alsabhan, "Student Cheating Detection in Higher Education by Implementing

Conference Session

Computers in Education Division (COED) Track 6.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Sujan Poudyal, Cleveland State University

Tagged Divisions

Computers in Education Division (COED)

reduce any potential biases.The findings suggest that educational institutions can utilize CNN-based models to predictstudent outcomes and implement targeted interventions to improve academic success. Thepromising results of this study motivate further exploration of deep learning techniques in thepedagogical field, aiming to enhance the predictive accuracy and broaden the scope of EDMapplications.References[1] J. Burrus, A. Betancourt, S. Holtzman, et al., "The predictive validity of high school GPA for postsecondary outcomes: A meta-analysis," Journal of Educational Psychology, vol. 115, no. 4, pp. 672-690, 2023.[2] A. Esteva, A. Robicquet, B. Ramsundar, et al., "Deep learning in healthcare: Current applications and emerging

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Colin Jacob Soule, Bucknell University; Lea Wittie, Bucknell University; Sing Chun Lee, Bucknell University

Tagged Divisions

Computers in Education Division (COED)

. Kirtani, S. Agrawal, and P. Chakraborty, “Pavt: a tool to visualize and teach parsing algorithms,” Education and Information Technologies, vol. 23, no. 6, pp. 2737–2764, Nov 2018. [Online]. Available: https://doi.org/10.1007/s10639-018-9739-x[4] J. L. Rodr´ıguez, I. Romero, and A. Codina, “The influence of neotrie vr’s immersive virtual reality on the teaching and learning of geometry,” Mathematics, vol. 9, no. 19, 2021. [Online]. Available: https://www.mdpi.com/2227-7390/9/19/2411[5] H. A. Malik, R. Ferdinand, B. Aurelius, M. Fajar, and P. A. Suri, “Applications of virtual reality in computer sciences education: A systematic literature review,” in 2023 International Conference on Informatics, Multimedia, Cyber and Informations

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Linda Davis Ahlstrom, Utah State University; Oenardi Lawanto, Utah State University; Cassandra J McCall, Utah State University; Michaela Harper, Utah State University; Wade H Goodridge, Utah State University; Daniel Kane, Utah State University

Tagged Divisions

Computers in Education Division (COED)

: 10.1109/EDUCON60312.2024.10578667.[2] S. Isaac Flores-Alonso et al., “Introduction to AI in Undergraduate Engineering Education,” 2023 IEEE Front. Educ. Conf. FIE Front. Educ. Conf. FIE 2023 IEEE, pp. 1– 4, Oct. 2023, doi: 10.1109/FIE58773.2023.10343187.[3] B. A. Becker, P. Denny, J. Finnie-Ansley, A. Luxton-Reilly, J. Prather, and E. A. Santos, “Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation,” in Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, Toronto ON Canada: ACM, Mar. 2023, pp. 500–506. doi: 10.1145/3545945.3569759.[4] G. V. Joseph, A. P., A. T. M., D. Jose, T. V. Roy, and M. Prasad, “Impact of Digital Literacy

Conference Session

Computers in Education Division (COED) Track 5.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Russell Feldhausen, Kansas State University; Nathan H Bean, Kansas State University; Joshua Levi Weese, Kansas State University; David S. Allen, Kansas State University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

responses to the RIS survey for each student. The Shapiro-Wilk normality test on the resulting data produced p = 0.80, much larger than the p > 0.05standard; therefore the new WRI measure is normally distributed. The mean WRI valuewas 1.40 with a standard deviation of 0.38. Larger WRI values indicate a stronger ruralidentity. Table 2: Significant Factor Loadings Item 1 Factor 2 Factor A 2 Factor B Q1 0.46 0.61 Q2 Q3 0.65 Q4 0.48 0.63 Q5 0.47 0.58 Q6 0.45

Conference Session

Computers in Education Division (COED) Track 3.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Sierra Zoe Bennett-Manke, United States Military Academy; Maria R. Ebling, United States Military Academy

Tagged Divisions

Computers in Education Division (COED)

://doi.org/10.1145/1453775.1453792. [9] L. P. Maia, F. B. Machado, and A. C. Pacheco. “A Constructivist Framework for Operating Systems Education: A Pedagogic Proposal Using the SOsim”. In: Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education. ITiCSE ’05. Caparica, Portugal: Association for Computing Machinery, 2005, pp. 218–222. ISBN: 1595930248. DOI: 10.1145/1067445.1067505. URL: https://doi.org/10.1145/1067445.1067505.[10] L. N. Paschoal et al. “Towards an Educational Simulator to Support the CPU Scheduling Algorithm Education”. In: 2019 International Symposium on Computers in Education (SIIE). Tomar, Portugal: IEEE, 2019, pp. 1–6. DOI: https://doi.org/10.1109

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Priyanshu Ghosh, Mission San Jose High School; Mihai Boicu, George Mason University

Tagged Divisions

Computers in Education Division (COED)

. J. Devlin, M.‑W. Chang, K. Lee, and K. Toutanova, “BERT: Pre‑training of deep bidirectionaltransformers for language understanding,” Proceedings of the NAACL‑HLT Conference,Minneapolis, MN, Jun. 2019, pp. 4171‑4186.6. L. Zhang, S. Wang, and B. Liu, “Deep learning for sentiment analysis: A survey,” WileyInterdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, e1253, 2018.7. P. Xie, H. Gu, and D. Zhou, “Modeling sentiment analysis for educational texts by combiningBERT and FastText,” Proceedings of the International Conference on Computer Science andTechnologies in Education (CSTE), Xi’an, China, Jul. 2024, pp. 1‑6.8. X. Li, H. Zhang, Y. Ouyang, X. Zhang, and W. Rong, “A shallow BERT‑CNN model forsentiment analysis on