Conference Session

Spotlight on Diverse Learners

Collection

2024 ASEE Annual Conference & Exposition

Authors

Minkyung Lee, Pennsylvania State University; Stephanie Cutler, Pennsylvania State University; Sarah E Zappe, Pennsylvania State University; Sam Spiegel, Colorado School of Mines; Ibukun Samuel Osunbunmi, Pennsylvania State University

Tagged Divisions

Computers in Education Division (COED)

variety of instructional modes. Future studies could benefit from a designwhere students experience each mode of instruction for different subjects to provide a moreaccurate measure of preference and performance. Such research would offer a deeperunderstanding of how different instructional modes influence learning outcomes and couldpotentially inform more effective educational practices. References[1] Freeman, S., et al. 2014. Active learning increases student performance in science,engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23),8410-8415.[2] Prince, M. 2004. Does active learning work? A review of the research. Journal ofEngineering Education, 93(3), 223-231.[3

Conference Session

Computers in Education Division (COED) Track 4.E

Collection

2025 ASEE Annual Conference & Exposition

Authors

Gabriella Marie Haire, University of Florida College of Education; Pavlo Antonenko; Christine Wusylko, University of Florida; STEPHANIE KILLINGSWORTH, University of Florida; Brian Abramowitz, University of Florida; Jeremy A. Magruder Waisome, University of Florida

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Diversity

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Computers in Education Division (COED)

participate but also to explain theimportance of AI in science to their peers and community. This enabled scholars to feel apersonal connection as their scientific project was envisioned within a real-world context. Figure 2. Google Teachable Machine [16].Measures and data sourcesThe self-reports of the children’s self-efficacy for AI were collected via a survey administeredon Qualtrics before and after the Shark AI program. Self-efficacy for AI was assessed using anadapted version of the original Science subscale (9 items) and the Technology and Engineeringsubscale (9 items) of the widely used 37-item S-STEM questionnaire developed by NorthCarolina State University’s Friday Institute [19]. Only the Science and Technology

Conference Session

Computers in Education Division (COED) Track 5.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Maverick Berner, Marquette University; Darcy Ronan, Sacred Heart University; Dennis W Brylow, Marquette University; Maximus Berner, Marquette University

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Computers in Education Division (COED)

://code.org/educate/csf [4] “Computer science fundamentals deep dive workshop,” Code.org, 2023. [Online]. Available: https://code.org/professional-development-workshops [5] J. E. Dolan, “Splicing the divide: A review of research on the evolving digital divide among k–12 students,” Journal of Research on Technology in Education, vol. 48, no. 1, pp. 16–37, 2016. [6] E. Gellenbeck, “Integrating accessibility into the computer science curriculum,” J. Comput. Sci. Coll., vol. 21, no. 1, p. 267–273, oct 2005. [7] M. Alper, “Making space in the makerspace: Building a mixed-ability maker culture,” Proceedings of the Interaction Design and Children (IDC-13), New York, NY, USA, pp. 24–27, 2013. [8] A. Hurst and S. Kane, “Making ”making

Conference Session

The Best of Computers in Education

Collection

2023 ASEE Annual Conference & Exposition

Authors

Andrew Rukangu, University of Georgia; John Ray Morelock, University of Georgia; Kyle Johnsen, University of Georgia

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Computers in Education Division (COED)

extent to which students be- Self-determination theory (Deci and Ryan, 2000), par- lieve they have meaningful control ticularly the importance of autonomy to intrinsic mo- over their learning. tivation (Reeve and Jang, 2006). (U)sefulness The extent to which students be- Future time perspective theory (Simons et al., 2004) lieve the material will be useful to and the utility value construct of expectancy-value them. theory (Wigfield and Eccles, 2000). (S)uccess The extent to which students be- Ability beliefs, including self-efficacy and com

Conference Session

COED: Grading Systems

Collection

2023 ASEE Annual Conference & Exposition

Authors

Marko V. Lubarda, University of California, San Diego; Alex M. Phan, University of California, San Diego; Aidan Daniel Carrigg; Karthik Srinivasan; Josephine Relaford-Doyle, University of California, San Diego

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Diversity

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Computers in Education Division (COED)

Conference Session

Computers in Education Division (COED) Best of CoED Paper Session (Track 1.B)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jean Louis, University of Florida; Nadia Simone Jean Morrow, University of Florida; Juan E Gilbert, University of Florida

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Computers in Education Division (COED)

/worldwide [6] [Online]. Available: https://www.mordorintelligence.com/industry-reports/internet-of-things-iot-market [7] [Online]. Available: https://www.indeed.com/career-advice/finding-a-job/cloud-computing-careers [8] B. Burd, L. Barker, F. A. F. P´erez, I. Russell, B. Siever, L. Tudor, M. McCarthy, and I. Pollock, “The internet of things in undergraduate computer and information science education: exploring curricula and pedagogy,” in Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, 2018, pp. 200–216. [9] C. Servin, S. Aly, Y. Cheon, E. Eaton, C. Guevara, A. Kumar, T. Pirtle, and M. Scott, “Cs2023: Acm/ieee-cs/aaai computer science curricula-specialized platform

Conference Session

Computers in Education Division (COED) Track 3.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Yuxuan Chen, University of Illinois Urbana-Champaign; Chenyan Zhao, University of Illinois Urbana-Champaign; Kangyu Feng, University of Illinois Urbana-Champaign; Mattox Alan Beckman, University of Illinois Urbana-Champaign; Mariana Silva, University of Illinois Urbana-Champaign

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Computers in Education Division (COED)

their helpful feedback on earlier drafts of this paper. This material isbased upon work supported by the Strategic Instructional Innovations Program in the GraingerCollege of Engineering at the University of Illinois Urbana-Champaign.References [1] M. Hertz, “What do CS1 and CS2 mean? investigating differences in the early courses,” in Proceedings of the 41st ACM Technical Symposium on Computer Science Education, ser. SIGCSE ’10. New York, NY, USA: ACM, 2010, p. 199–203. [Online]. Available: https://doi.org/10.1145/1734263.1734335 [2] G. Marceau, K. Fisler, and S. Krishnamurthi, “Measuring the effectiveness of error messages designed for novice programmers,” ser. SIGCSE ’11. New York, NY, USA: Association for Computing Machinery

Conference Session

Computers in Education Division (COED) Track 2.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Runu Proma Das, University of Georgia; Tathyana Moratti, University of Georgia; Shari Gasper, University of Georgia; Beshoy Morkos, University of Georgia

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Computers in Education Division (COED)

? The Implications of Large Language Models for Medical Education and Knowledge Assessment,” JMIR Med Educ, vol. 9, 2023, doi: 10.2196/45312.[10] D. M. Katz, M. J. Bommarito, S. Gao, and P. Arredondo, “GPT-4 passes the bar exam,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 382, no. 2270, Apr. 2024, doi: 10.1098/rsta.2023.0254.[11] M. Kaushik, P. Baligar, and G. Joshi, “Formulating An Engineering Design Problem: A Structured Approach,” 2018.[12] B.-A. Schuelke-Leech, “A Problem Taxonomy for Engineering,” IEEE Transactions on Technology and Society, vol. 2, no. 2, pp. 105–105, Apr. 2021, doi: 10.1109/tts.2021.3072213.[13] D. H. Jonassen

Conference Session

Computers in Education Division (COED) Track 4.E

Collection

2025 ASEE Annual Conference & Exposition

Authors

Michael Thomas Vaccaro Jr, University of Connecticut; Mikayla Friday, University of Connecticut; Zeynep Gonca Akdemir-Beveridge, University of Connecticut; Arash Esmaili Zaghi P.E., University of Connecticut

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Computers in Education Division (COED)

understanding how AI-powered tools can enhance learning experiences, particularlyin fields requiring tailored support, such as engineering education.AcknowledgementsThis material is based upon work supported by the National Science Foundation under MCAGrant No. 2120888. The first author (MV) was supported by an NSF Research Traineeship(TRANSCEND) under Grant No. 2152202 at the time this research was conducted. Anyopinions, findings, and conclusions or recommendations expressed in this material are those ofthe author(s) and do not necessarily reflect the views of the National Science Foundation.The authors greatly appreciate the support of Trent Alsup, Jada Vercosa, Brian Hance, andAbhiram Gunti in the initial development of the GPT platform.Finally

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Tayo Obafemi-Ajayi, Missouri State University; Naomi L Dille, Missouri State University; Dhanush Bavisetti, Missouri State University; Sherrie Ilene Zook

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Computers in Education Division (COED)

Paper ID #47009BOARD # 71: Integrating Machine Learning into Middle and High SchoolCurricula using Alzheimer’s Disease Prediction ModelsDr. Tayo Obafemi-Ajayi, Missouri State University Dr. Tayo Obafemi-Ajayi is an Associate Professor of Electrical Engineering at Missouri State University in the Engineering Program, a joint program with Missouri University of Science and Technology (S&T). She obtained her B.S and MS in Electrical Engineering and a PhD in Computer Science from Illinois Institute of Technology.Dr. Naomi L Dille, Missouri State UniversityDhanush Bavisetti, Missouri State UniversityMrs. Sherrie Ilene Zook

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Zoe Wood, California Polytechnic State University, San Luis Obispo; Miguel Manoah Refugio Greenberg

Tagged Divisions

Computers in Education Division (COED)

important for people to learn how to code because we need to understand whatChatGPT is doing. Also, no matter how advanced ChatGPT gets, it is still only getting itsinformation from the internet, yet the internet does not contain equal amounts of informationfrom every part of the world. Teachers should continue to teach coding and include ways thatChatGPT can improve learning instead of replace learning.5 AcknowledgmentsThank you to Ms. Ashley Ong, AP-CS high school teacher for teaching me CS and to our papereditor.References [1] N. Forman, J. Udvaros, and M. S. Avornicului, “Chatgpt: A new study tool shaping the future for high school students,” International Journal of Advanced Natural Sciences and Engineering Researches, vol. 7, no. 4, p

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Trini Balart, Texas A&M University; Kristi J. Shryock, Texas A&M University

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Computers in Education Division (COED)

. Haleem, R. P. Singh, S. Khan, and I. H. Khan, “Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system,” BenchCouncil Transactions on Benchmarks, Standards and Evaluations, vol. 3, no. 2, p. 100115, Jun. 2023, doi: 10.1016/j.tbench.2023.100115.[6] D. Eke, «ChatGPT and the rise of generative AI: threat to academic integrity?», Journal of Responsible Technology, vol. 13, p. 100060, abr. 2023, doi: 10.1016/j.jrt.2023.100060.[7] S. Nikolic et al., “ChatGPT versus engineering education assessment: a multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity,” European Journal of Engineering Education, vol. 48

Conference Session

Computers in Education Division (COED) Track 4.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Marlee Jacobs, Utah State University; Daniel Kane, Utah State University; Rosemary Yahne, Utah State University; Wade H Goodridge, Utah State University

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Computers in Education Division (COED)

. Guerra, and S. Duran Ballen, “ChatGPT to Support Critical Thinking inConstruction-Management Students,” in 2024 ASEE Annual Conference & ExpositionProceedings, Portland, Oregon: ASEE Conferences, Jun. 2024, p. 48459. doi: 10.18260/1-2--48459.[6] S. Vidalis, R. Subramanian, and F. Najafi, “Revolutionizing Engineering Education: TheImpact of AI Tools on Student Learning,” in 2024 ASEE Annual Conference & ExpositionProceedings, Portland, Oregon: ASEE Conferences, Jun. 2024, p. 47950. doi: 10.18260/1-2--47950[7] B. Qureshi, “Exploring the Use of ChatGPT as a Tool for Learning and Assessment inUndergraduate Computer Science Curriculum: Opportunities and Challenges”. 2023,https://arxiv.org/abs/2304.11214[8] M. O. Agbese, M. Rintamaki, R

Conference Session

Computer-Supported Pedagogy and Assessment

Collection

2024 ASEE Annual Conference & Exposition

Authors

Keith Hekman, California Baptist University

Tagged Divisions

Computers in Education Division (COED)

volume, center ofmass, and moment of inertia to a reference file. Such a comparison is similar to the CertifiedSOLIDWORKS Associate in Mechanical Design exam [7], where students generateSOLIDWORKS files and input a geometric property, such as mass or center of mass, todetermine if their drawing is correct. Bojcetic et al.’s method allows for more refined gradingcriteria, grading features, and sketches in addition to the basic geometry [8]. Overall, thedeveloped automated grading systems speed up the grading time for faculty, allowing for morehomework. Still, they do not provide quick feedback, allowing students to learn by correctingtheir mistakes. Having rapid feedback was the motivation for developing an email-based gradingsystem.Program

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Carsten Monrad Thue-Bludworth, University of Florida; Jeremiah J Blanchard, University of Florida

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Computers in Education Division (COED)

matures, we intend to introduce it to the community at large in a variety of ways,including publishing via social media, forums, maker communities on the internet, conferenceson engineering education, as well as through outreach with some of the vendors whose hardwareand software inspired Iron Coder’s conception.References [1] L. Fried, “Introducing adafruit feather.” https://learn.adafruit.com/adafruit-feather/overview. Accessed: 2024-01-20. [2] Sparkfun, “What is micromod?.” https://www.sparkfun.com/micromod. Accessed: 2024-01-20. [3] “Raspberry pi.” https://www.raspberrypi.com/. Accessed: 2024-01-20. [4] “The easiest way to program microcontrollers.” https://circuitpython.org/. Accessed: 2024-01-20. [5] S. Hodges, S. Sentance, J. Finney

Conference Session

Computers in Education Division (COED) Track 4.D

Collection

2025 ASEE Annual Conference & Exposition

Authors

Leslie Bondaryk, The Concord Consortium; Aaron Kyle, Duke University; Ido Davidesco, University of Connecticut; Chad Dorsey; Bianca Montrosse-Moorhead, University of Connecticut

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Diversity

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Computers in Education Division (COED)

in this material are those of the authors and do not necessarily reflect the views of theNational Science Foundation.The authors would like to thank the Concord Consortium software developers, in particularMichael Tirenin for user interface design, and Joe Bacal, Teale Fristoe and Scott Cytacki fordesign and implementation of the digital twin interface to Dataflow.​ References​ ​ ​ ​ [1] P. B. Henderson, T. J. Cortina, O. Hazzan, and J. Wing, “Computational thinking,” in I. Russell & S. Haller (Eds.), Proceedings of the 38th ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE ’07), pp. 195-196https://doi.org/10.1145/1227310.1227378 ​​ [2] S. Grover and R. Pea, “Computational thinking in

Conference Session

Computers in Education Division (COED) Track 3.C

Collection

2025 ASEE Annual Conference & Exposition

Authors

Eric L Brown, Tennessee Technological University; Douglas A. Talbert, Tennessee Technological University; Jesse Roberts, Tennessee Technological University

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Computers in Education Division (COED)

pathways based upon established frameworks.Traditional curriculum plans, such as those from institutions designated by the NCAE-C incybersecurity defense education (CAE-CD) and cyber operations (CAE-CO) designation tracks,involve a manual process of identifying knowledge units for specific courses. Throughout themapping process, gaps are identified in curriculum plans based on the knowledge of the subjectmatter expert(s) (SME). Performing this task for one framework is challenging enough; considerthe increased complexity and risk of error when multiple frameworks are cross-referenced intothe plan. Improvement opportunities exist in the curriculum mapping and gap analysis process.This leads to the question of whether an LLM can speed up the

Conference Session

COED: Skills for Moving from Computing Student to Professional

Collection

2023 ASEE Annual Conference & Exposition

Authors

Mary Kay Camarillo, University of the Pacific; Elizabeth Basha, University of the Pacific

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Computers in Education Division (COED)

instruction in multiple STEM disciplines,”presented at the ASEE Annual Conference, Virtual Conference, Jul 26-9, 2021. Available:https://peer.asee.org/37955.[2] C. Torres-Machi, A. Bielefeldt, and Q. Lv, “Work in progress: The strategic importanceof data science in civil engineering: Encouraging interest in the next generation,” presented at theASEE Annual Conference, Minneapolis, MN, Jun 26-9, 2022. Available:https://peer.asee.org/40713.[3] S. Grajdura and D. Niemeier, "State of programming and data science preparation in civilengineering undergraduate curricula," Journal of Civil Engineering Education, vol. 149, no. 2, p.04022010, 2023, doi: doi:10.1061/(ASCE)EI.2643-9115.0000076.[4] J. G. Hering, "From slide rule to big data: How data

Conference Session

Computer Engineering Topics

Collection

2024 ASEE Annual Conference & Exposition

Authors

Timothy Sellers, Mississippi State University; Tingjun Lei, Mississippi State University; Chaomin Luo, Mississippi State University; Gene Eu Jan; Zhuming Bi, Purdue University, Fort Wayne

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Diversity

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Computers in Education Division (COED)

(TALE). IEEE, 2015, pp. 72–76. [7] K. W. Van Treuren, “Applying active learning to an introductory aeronautics class,” in 2018 ASEE Annual Conference & Exposition, 2018. [8] C. R. Compeau, A. Talley, and P. Q. Tran, “Active learning in electrical engineering: Measuring the difference,” in 2019 ASEE Annual Conference & Exposition, 2019. [9] F. Portela, “A new and interactive teaching approach with gamification for motivating students in computer science classrooms,” in First International Computer Programming Education Conference (ICPEC 2020). Schloss Dagstuhl-Leibniz-Zentrum f¨ur Informatik, 2020.[10] G. S. Tewolde, “Effective active learning tools for an embedded systems course,” in 2017 IEEE Frontiers in Education

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Arnav Ketineni, Portland State University; Hrithik Ketineni; Kyle Liu, Portland State University; Marek Perkowski, Portland State University

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Computers in Education Division (COED)

. The GameThe method by which this paper teaches SOP minimization is a game with which students competeto capture the maximum number of true minterms. Upon capture by either player, a true minterm’ssquare or cell is highlighted with the player’s corresponding color. Once all true minterms arecaptured by either Player One or Player Two, the game is over and the player with a greater numberof true minterms covered wins. The player(s) can also capture true minterms occupied by the otherplayer to both reduce their opponents score and increase their own. However, if a player capturesa false minterm through any one of their moves via an incorrect Sum-of-Products, then the playerforfeits the game. As such, the game encourages students to naturally

Conference Session

Computers in Education Division (COED) Track 3.D

Collection

2025 ASEE Annual Conference & Exposition

Authors

Keith Hekman, California Baptist University

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Diversity

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Computers in Education Division (COED)

to increase their learning by providing the opportunity to learn from their mistakes.Automation also assists professors by reducing the time they spend grading student work.Previous work showed that students preferred using an automated grading system to having a TAgrade their work.A web-based system has been developed based on the email-based system currently used at theuniversity. Interfaces were developed to grade AutoCAD, SOLIDWORKS, Excel, and LabVIEWfiles. From the student perspective, students choose the assignment they are submitting and thenupload the appropriate file(s). The back end of the web page grades the student’s work, providingtextual and graphical feedback on their submission. On the web page, students can review

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

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Computers in Education Division (COED)

, communicate effectively, complete academic tasks,and prepare for future careers in an increasingly technology-dependent societyReferences[1] M. Pantic, A. Pentland, A. Nijholt, and T. S. Huang, “Human Computing and Machine Understanding of Human Behavior: a survey,” in Springer eBooks, 2007, pp. 47–71. doi: 10.1007/978-3-540-72348-6_3.[2] “Smartphones as Partners in Teaching and Learning,” International Journal of Academic Research and Reflection, Vol. 7, No. 1, 2019, ISSN 2309-0405. [Online]. Available: https://www.idpublications.org/wp-content/uploads/2019/02/Full-Paper-SMARTPHONES- AS-PARTNERS-IN-TEACHING-AND-LEARNING.pdf. [Accessed: 05-Mar-2023].[3] “Computer and internet use in the United States: 2018,” American

Conference Session

Programming Education 2

Collection

2024 ASEE Annual Conference & Exposition

Authors

Edward Dillon, Morgan State University; Krystal L. Williams, University of Georgia; Ashley Simone Pryor, Morgan State University; Theodore Wimberly Jr., Morgan State University; Mariah McMichael, Morgan State University; Abisola Mercy Arowolaju; Donald Bernard Davis, Morgan State University; Toluwanimi Ayodele, Morgan State University

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Computers in Education Division (COED)

programming tool. Only 41% of the CS2 students, whowere instructed to use a command line tool, completed Task 1. This percentage was much higherfor the CS2 students using this tool to complete Task 2. For both Tasks 1 and 2 most of the OOPstudents, who were instructed to use a command line tool, were able to complete them. Table 5. Assigned Tool/Editor(s) to Complete the Two Assigned Tasks (Fall 2020 - Spring 2023) Assigned Tool(s) - Command Line vs. IDE N Task 1 Task 2 CS2 77 Command Line: 41% Command Line: 71% IDE

Conference Session

Robotics and Circuits

Collection

2024 ASEE Annual Conference & Exposition

Authors

Carl Joseph Murzynski, Pennsylvania State University, Behrend College; Hussein - Abdeltawab, Wake Forest University; Omar Ashour, Pennsylvania State University, Behrend College; Ahmed Sammoud, Pennsylvania State University, Behrend College

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Diversity

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Computers in Education Division (COED)

result of the experiment performed in a Computer Science course. The lastsection provides the conclusions and future work.2. The Overall Robotic Arm PlatformThe main component of the robotic arm platform is the Dobot M1 Pro [6]. This robotic arm weighs15.7kg, can carry a maximum load of 1.5kg, has a maximum reach of 400mm, and has industrial-level repeatability of ±0.02mm. Its power supply uses 00~240 VAC at 50/60Hz, and the arm hasa rated voltage of 48 volts DC. Each joint can turn 180°/s, the end effector can turn 1000°/s, andit can move up and down 1000mm/s. The first joint can move ±85°, the second joint can move J2±135°, the vertical moves 5mm～245mm, and the end effector can move ±360°. An air pump,suction cup, and gripper are sold in a

Conference Session

Programming Education 2

Collection

2024 ASEE Annual Conference & Exposition

Authors

Tyler James Stump, The Ohio State University; Abbey Darya Kashani Motlagh, The Ohio State University; Krista M Kecskemety, The Ohio State University

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Computers in Education Division (COED)

Parsons Problems. The eleven features of interest for this transferabilityconsideration includes the following: Accessibility, Assessment, Classroom Dynamics,Difficulty, Distractors, Format, Group Dynamics, Length, Preparation, Time, and Utility Valuewere all identified as unique elements impacted student experiences. The definitions for each canbe described in Table 1. Table 1: Finalized Codebook for Parsons Problems Features Impacting Student Experiences Feature Definitions Example(s) Student Experiences regarding access to tools & Click and drag, full team seeing Accessibility resources to support the demonstration of a student's

Conference Session

Computer-Supported Pedagogy and Assessment

Collection

2024 ASEE Annual Conference & Exposition

Authors

Jim Sosnowski, University of Illinois Urbana-Champaign; Julie M Baker, University of Illinois Urbana-Champaign; Olivia Arnold, University of Illinois Urbana-Champaign; Mariana Silva, University of Illinois Urbana-Champaign; David Mussulman, University of Illinois Urbana-Champaign; Craig Zilles, University of Illinois Urbana-Champaign; Matthew West, University of Illinois Urbana-Champaign

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Diversity

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Computers in Education Division (COED)

atscale is conducted in the College of Engineering, facilitating the implementation ofresearch-based pedagogical assessment practices that are improving student outcomes [10, 11].We believe the lessons shared in this paper can serve as a template for other engineering programsabout how to effectively provide CBT at scale in a manner that positively impacts students andfaculty.References [1] S. Shadle, A. Marker, and B. Earl, “Faculty drivers and barriers: Laying the groundwork for undergraduate stem education reform in academic departments.” International Journal of STEM Education, vol. 4, 2017. [Online]. Available: https://proxy2.library.illinois.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true& amp;db=eric&

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Nicholas Cameron Amely, California State University, Fresno; Wei Wu, California State University, Fresno; Jesus Leyva, California State University, Fresno

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Computers in Education Division (COED)

valuable feedback.References[1] J. Pomerantz, "Learning in Three Dimensions: Report on the EDUCAUSE/HP Campus of the Future Project," in "ECAR research report," EDUCAUSE, Louisville, CO, August 2018 2018.[2] J. Pomerantz, "XR for Teaching and Learning: Year 2 of the EDUCAUSE/HP Campus of the Future Project," in "ECAR research report," EDUCAUSE, Louisville, CO, 2019.[3] J. Pomerantz, "Extending XR across Campus: Year 2 of the EDUCAUSE/HP Campus of the Future Project," in "ECAR research report," EDUCAUSE, Louisville, CO, 2020.[4] Y. M. Tang, K. M. Au, H. C. W. Lau, G. T. S. Ho, and C. H. Wu, "Evaluating the effectiveness of learning design with mixed reality (MR) in higher education," Virtual Reality

Conference Session

Computers in Education Division (COED) Track 4.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Reine Azzi, Lebanese American University

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Computers in Education Division (COED)

AR cycle involves a clear rationale for pedagogical intervention,reflection upon this exercise, and preparation for another cycle [30]. All faculty membersinvolved in this exercise were empowered to create similar hybrid student-AI activities in theirupcoming classes. They were aware of both the added value of such assignments and their abilityto provide critical reflection on the limitations of GenAI. Consistent with Wach et al.’s [43] emphasis on responsible use of GenAI tools, facultymembers were aware of the need to move beyond mere restriction to ethical use. Facultymembers consistently relayed their belief that the best means forward is to expose students toGenAI tools within a controlled class environment, in a manner like the

Conference Session

COED: Skills for Moving from Computing Student to Professional

Collection

2023 ASEE Annual Conference & Exposition

Authors

Stephanie Jill Lunn, Florida International University; Veon Brewster, Florida International University

Tagged Divisions

Computers in Education Division (COED)

Conference Session

COED Programming Education 1: Students, Motivation, and Mastery

Collection

2023 ASEE Annual Conference & Exposition

Authors

James E. Lewis, University of Louisville; Nicholas Hawkins, University of Louisville; Brian Scott Robinson, University of Louisville

Tagged Divisions

Computers in Education Division (COED)

thesimilarities and differences of the APL to Python. Upon completion of the “Programminglessons”, there is a series of activities designed to help the students create circuit(s) andprogram(s) that interact with each other.The programming and circuitry scaffolded modules prepare students for an end-of-semesterCornerstone Project. ENGR 111 currently has two different Cornerstone Projects. TheCornerstone Project is determined by the semester and year that the course is taken. The firstCornerstone Project (Project 1) is comprised of a windmill power generation system. Project 1has students constructing a windmill and using Arduino programming to interpret sensor dataand calculate system performance. The second Cornerstone Project (Project 2) is comprised of