Paper ID #42251Board 44: CampNav: A System for Inside Buildings and Campus NavigationMr. Jiping Li, University of Toronto Jiping Li is an ECE undergraduate at the University of Toronto.Zhiqiang Yin, University of TorontoDr. Hamid S Timorabadi P.Eng., University of Toronto Hamid Timorabadi received his B.Sc, M.A.Sc, and Ph.D. degrees in Electrical Engineering from the University of Toronto. He has worked as a project, design, and test engineer as well as a consultant to industry. His research interests include the applicati ©American Society for Engineering Education, 2024 Work In Progress: CampNav: A
Paper ID #41872Board 47: A Mentor-Mentee Matching Algorithm to Automate Process ofFinding an Ideal Mentor for StudentsMs. Sweni ShahDr. Hamid S Timorabadi P.Eng., University of Toronto Hamid Timorabadi received his B.Sc, M.A.Sc, and Ph.D. degrees in Electrical Engineering from the University of Toronto. He has worked as a project, design, and test engineer as well as a consultant to industry. His research interests include the applicatiSanjana DasadiaSamreen Khatib SyedDoaa Muhammad, University of Toronto ©American Society for Engineering Education, 2024 Work In Progress: MentorMate: A Platform to
JavaScript.Dr. Hamid S Timorabadi P.Eng., University of Toronto Hamid Timorabadi received his B.Sc, M.A.Sc, and Ph.D. degrees in Electrical Engineering from the University of Toronto. He has worked as a project, design, and test engineer as well as a consultant to industry. His research interests include the applicati ©American Society for Engineering Education, 2024 WIP: Immersive Learning: Maximizing Computer Networks Education Based on 3D Interactive AnimationsAbstractThe potential of 3D animation models can enhance the learning process, making it morevivid and clear by capturing students' attentions. As concepts related to computer networksare often abstract and intricate, educators commonly
. She also serves as Director of the Craig and Galen Brown Engineering Honors Program. She received her BS, MS, and PhD from the College of Engineering at Texas A&M. Kristi works to improve the undergraduate engineering experience through evaluating preparation in areas, such as mathematics and physics, evaluating engineering identity and its impact on retention, incorporating non-traditional teaching methods into the classroom, and engaging her students with interactive methods.Dr. Michael S Rugh, Texas A&M University Michael S. Rugh is an Associate Research Scientist for the LIVE Lab at Texas A&M University. He has a B.S. and M.S. in Mathematics and a PhD in Curriculum and Instruction. He received the
credibility of the subject matter before wider dissemination andimplementation.References[1] M. H. Temsah, I. Altamimi, A. Jamal, K. Alhasan, & A. Al-Eyadhy, ChatGPT surpasses 1000 publications on PubMed: envisioning the road ahead. Cureus, 15(9) 2023.[2] G. Conroy, Surge in number of extremely productive authors’ concerns scientists. Nature, 625(7993), 14-15. 2024.[3] R. Van Noorden and J. M. Perkel, AI and science: what 1,600 researchers think. Nature, 621(7980), 672-675, 2023.[4] M. Binz, S. Alaniz, A. Roskies, B. Aczel, C. T. Bergstrom, C. Allen, C. and E. Schulz, How should the advent of large language models affect the practice of science?. arXiv preprint arXiv:2312.03759, 2023.[5] E. M. Bender, T. Gebru, A. McMillan-Major, S
program. {problem_description} Buggy Program: ```{buggy_program} ``` Can you fix the above buggy program?” Instructors may find theseprompts useful to share with students to model using LLMs responsibly.Moving away from programming, Arndt [38] delves into the application of LLMs in explainingconcepts from system thinking and system dynamics, in addition to creating visualizations suchas causal loop diagrams (a model showing causal relationships between variables with +'s and –'sto denote the direction of the relationship). Leveraging the ability of tools like ChatGPT to writescripts in Python (and other languages), it was found that creating such visualizations waspossible by running the output outside of the LLM's interface – albeit with
regarding the eligibility of ChatGPT as an author [31], [32]. These ethicalconcerns play a valuable role by offering opportunities to steer the implementation of GAI inethically responsible ways.Research Questions a) What are students’ and instructors’ perceived literacy of GAI (e.g. knowledge, skills, and abilities)? b) How do students and instructors experience the usefulness and effectiveness of GAI in their course(s)?Theoretical FrameworkThere are many theoretical lenses that one can consider when investigating the experiences ofstudents and instructors using GAI. This paper is primarily interested in the participant literacyregarding GAI and their perceived usefulness and effectiveness of the technology. To explorethis, we
s sections of theengineering course at a large Midwestern university. Over the semester, students were asked toreflect after each lecture on two aspects of their learning experience, i.e., what they found 1)interesting and 2) confusing in the lecture? In total, we collected reflections from 42 lectures, andthe average class size was 80 students in each section. To inform the study, we generated areflection summary for all reflection submissions in each lecture using both NLP approaches andhuman annotators. Furthermore, we evaluated the quality of reflection summaries by assessingthe ROUGE-N measure for each lecture’s reflection summary generated by all three approaches.These summaries were then aggregated for each approach by averaging
) Reichelderfer Endowed Chair awarded in June 2022 to Dr. Estell.We would like to thank the 2024 Program Chair for the Computers in Education Division(CoED), Dr. Jean Mohammadi-Aragh, for allowing the body of this paper to extend beyondCoED’s 10-page limit as specified in the Call for Papers for this Annual Conference.References[1] C. Thompson, "The Secret History of Women in Coding," The New York Times, 13 February 2019. [Online]. Available: https://www.nytimes.com/2019/02/13/magazine/women-coding-computer-programming.html. [Accessed 11 January 2024].[2] S. Cheryan, A. Master and A. Meltzoff, "There Are Too Few Women in Computer Science and Engineering," Scientific American, 27 July 2022. [Online]. Available: https://www.scientificamerican.com
. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung, “Survey of hallucination in natural language generation,” ACM Computing Surveys, vol. 55, no. 12, pp. 1–38, 2023. [2] H. Alkaissi and S. I. McFarlane, “Artificial hallucinations in chatgpt: implications in scientific writing,” Cureus, vol. 15, no. 2, 2023. [3] B. McMurtrie, “Teaching: Will chatgpt change the way you teach,” The Chronicle of Higher Education, 2023. [4] J. Rudolph, S. Tan, and S. Tan, “Chatgpt: Bullshit spewer or the end of traditional assessments in higher education?” Journal of Applied Learning and Teaching, vol. 6, no. 1, 2023. [5] B. S. Bloom, M. D. Engelhart, E. Furst, W. H. Hill, and D. R. Krathwohl, “Handbook i: cognitive domain,” New
e.CRN, 2 (SELECT SUM(Score * Credits)/SUM(Credits) 3 FROM Enrollments e2 4 WHERE e.CRN = e2.CRN) AS CourseAvgScore, 5 s.NetId, 6 e.Score 7 FROM Students s 8 JOIN Enrollments e ON s.ID = e.CourseID -- Error: Incorrect JOIN condition, should be based on a valid relational key 9 WHERE (SELECT SUM(Score * Credits)/SUM(Credits)10 FROM Enrollments e211 WHERE e.CRN = e2.CRN) >= 8012 AND e.Score > 8513 ORDER BY e.CRN DESC, e.Score DESC; Instructor Query: 1 SELECT e.CRN, 2 (SELECT SUM(Score * Credits)/SUM(Credits) 3 FROM Enrollments e2 4 WHERE e.CRN = e2.CRN) AS CourseAvgScore, 5 s.NetId, 6 e.Score 7 FROM
technical, relating to the stream content, technology in general, technicalemployment, or general encouragement and suggestions from viewers. Some streams had mixedsocial and technical interactions where streamers might go off-topic due to a chat message orbuilt-in social time.RQ2 Knowledge Transfer Knowledge transfer in streams occurs most generally through thethink-aloud nature of streamers who, at a high level, talk through what they are working on orplan to work on during stream. In most streams, a viewer may pose a question to the streamerseeking information about what they are working on or something entirely different, but stilltechnology related. In S3’s stream, a viewer asks why the streamer works on a particular project,S3 responds:7 “I
practice,” Engl. Specif. Purp., vol. 23, no. 4, pp. 425– 445, Jan. 2004, doi: 10.1016/j.esp.2004.01.002.[2] Eun Gyong Kim and A. Shin, “Seeking an Effective Program to Improve Communication Skills of Non-English-Speaking Graduate Engineering Students: The Case of a Korean Engineering School,” IEEE Trans. Prof. Commun., vol. 57, no. 1, pp. 41–55, Mar. 2014, doi: 10.1109/TPC.2014.2310784.[3] Y.-R. Tsai, C.-S. Ouyang, and Y. Chang, “Identifying Engineering Students’ English Sentence Reading Comprehension Errors: Applying a Data Mining Technique,” J. Educ. Comput. Res., vol. 54, no. 1, pp. 62–84, Mar. 2016, doi: 10.1177/0735633115605591.[4] L. R. Cox and K. G. Lough, “The importance of writing skill to the engineering students
problem for sufficiently large numbers, for most introductory C programming this is not aproblem. For example, strlen returns an unsigned integer. This means the following code can leadto a compiler warning.f o r ( i n t i = 0 ; i < s t r l e n ( ” H e l l o ” ) ; i ++)Some other issues were the sluggishness of every operation. Running Ubuntu inside a virtualmachine is taxing on the graphics card, and the slow build times of catch2 was a frequent complaint.Novice students tend to write relatively small programs, so the additional compile time can slowdown their development cycle.In subsequent iterations, students running Windows were instructed to use Windows Subsystem forLinux (WSL) instead of a hypervisor. Visual Studio Code (and CLion) both
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
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
. 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
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
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
(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
. 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
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
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
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
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&
questions, the mean confidence in the response on a scale from 1 to5, with 1 indicating highest confidence, and the standard deviation of the confidence responses.The delta or difference between each performance metric computed by subtracting the discreteresults from the continuous results is perhaps the most illustrative lens for exploring the results.Table 2 - Table of objective performance results from users of the simulation tools. Question Percent Mean Median Std Dev Mean Std Dev Performance Correct [%] Time [s] Time [s] Time [s] Confidence Confidence Q1 Cont. 86.67 66.05 42.26 60.58 1.63 0.93 Q1 Discrete 71.88 81.35
from additional scaffolding in office hours.References [1] M. Ball, J. Hsia, H. Pon-Barry, A. DeOrio, and A. Blank, “Teaching TAs To Teach: Strategies for TA Training,” in Proceedings of the 51st ACM Technical Symposium on Computer Science Education, (Portland OR USA), pp. 477–478, ACM, Feb. 2020. [2] E. McDonald, G. Arevalo, S. Ahmed, I. Akhmetov, and C. Demmans Epp, “Managing TAs at Scale: Investigating the Experiences of Teaching Assistants in Introductory Computer Science,” in Proceedings of the Tenth ACM Conference on Learning @ Scale, (Copenhagen Denmark), pp. 120–131, ACM, July 2023. [3] S.-H. Ko and K. Stephens-Martinez, “What Drives Students to Office Hours: Individual Differences and Similarities,” in Proceedings
require careful calibration of new algorithms created withinTranscriptto and other AI APIs that would allow us to perform that complex functionality.References[1] F. Martin, A. Ritzhaupt, S. Kumar, and K. Budhrani, “Award-winning faculty online teaching practices: Course design, assessment and evaluation, and facilitation,” Internet High. Educ., vol. 42, pp. 34–43, Jul. 2019, doi: 10.1016/j.iheduc.2019.04.001.[2] M. Kebritchi, A. Lipschuetz, and L. Santiague, “Issues and Challenges for Teaching Successful Online Courses in Higher Education: A Literature Review,” J. Educ. Technol. Syst., vol. 46, no. 1, pp. 4–29, Sep. 2017, doi: 10.1177/0047239516661713.[3] C.-S. Li and B. Irby, “An Overview of Online Education: Attractiveness
Laboratories,Los Alamos National Laboratory and the Mozilla Foundation.REFERENCES [1] Forcael, E., Glagola, C., and González, V. (2012). ”Incorporation of Computer Simulations into Teaching Linear Scheduling Techniques.” J. Prof. Issues Eng. Educ. Pract., 138(1), 21–30 [2] Adams, W.K., Reid, S., LeMaster, R., McKagan, S.B., Perkins, K.K., Dubson, M., and Wieman. C.E. (2008a). A study of educational simulations part I—Engagement and learning. Journal of Interactive Learning Research, 19(3), 397-419.[3] Adams, W.K., Reid, S., LeMaster, R., McKagan, S.B., Perkins, K.K., Dubson, M., and Wieman, C.E. (2008b). A study of educational simulations part II—Interface design. Journal of Interactive Learning
pressure of an exam situation. This also readiedstudents to be able to interact and carefully evaluate responses by the AI. For some students,ChatGPT-3.5’s initial response did not satisfy the requirements of the test question. This actuallyproduced a very high level of engagement. By this stage, students had developed expertise of theproblem, and had to work toward nudging the AI to get a correct response. Because of theirprevious knowledge of the problem, students were better able to identify differences andsimilarities with their code. While engaged in this careful comparison, several students gainednew insights, or even new methods. The process of nudging the AI toward the correct answer isreminiscent of improving one’s learning by teaching or