Conference Session

The Best of Computers in Education Division (COED)

Collection

2024 ASEE Annual Conference & Exposition

Authors

Yutong Ai, University of Michigan; Maya Baveja, University of Michigan; Akanksha Girdhar, University of Michigan; Melina O'Dell, University of Michigan; Andrew Deorio, University of Michigan

Tagged Divisions

Computers in Education Division (COED)

% of interactions were helpful. 60.9% of survey responses said that asimilar bot would be helpful for other projects, 8.65% disagreed, and the rest were unsure.Students reported that the Bot was most helpful for answering project specification andconceptual questions, and least helpful for explaining test cases (Figure 3b).When asked if using the Bot instead of the course forum or office hours saved time, 40.1% ofstudents answered yes, while 21.8% said no. 30.5% of students reported that using the Bot helpedthem code faster, while 36.7% disagreed. The rest of the responses were unsure.(a) Number of interactions with the Bot for each stu- (b) Student-reported Bot helpfulness per prompt cat-dent group. Most student groups used the Bot at least

Conference Session

Simulations and Virtual Learning

Collection

2024 ASEE Annual Conference & Exposition

Authors

MEHMET Nasir SARIMURAT, Syracuse University

Tagged Divisions

Computers in Education Division (COED)

: dϕ A (ρϕu) A|B e = Γ A|B e + Sϕ (24) dxwhich can be written as: dϕ dϕ ρu (ϕB − ϕe ) = Γ − + Sϕ ∆x (25) dx B dx eAnalyzing each term in the equation above, ϕB is known since ϕB = ϕL , and ϕe can be calculatedas the average of ϕ at the two

Conference Session

Programming Education 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

Chi Yan Daniel Leung, zyBooks, A Wiley Brand; Joe Mazzone, zyBooks, A Wiley Brand; Efthymia Kazakou, zyBooks, A Wiley Brand; Chelsea Gordon, zyBooks, A Wiley Brand; Alex Daniel Edgcomb, zyBooks, A Wiley Brand; Yamuna Rajasekhar, zyBooks, A Wiley Brand

Tagged Divisions

Computers in Education Division (COED)

Proceedings of the 46th ACM Technical Symposium on‬ ‭Computer Science Education, SIGCSE, pp.410-415, Feb 24 2015.‬ ‭[3] G. Haldeman, A. Tjang, M. Babeş-Vroman, S. Bartos, J. Shah, D. Yucht, and T.D. Nguyen, “Providing‬ ‭meaningful feedback for autograding of programming assignments,” in Proceedings of the 49th ACM‬ ‭Technical Symposium on Computer Science Education, SIGCSE, pp. 278-283, Feb 21 2018.‬ ‭[4] H. Keuning, J. Jeuring, and B. Heeren. “Towards a Systematic Review of Automated Feedback‬ ‭Generation for Programming Exercises,” in Proceedings of the 2016 ACM Conference on Innovation and‬ ‭Technology in Computer Science Education, ITiCSE '16, pp. 41-46, Jul 2016.‬ ‭[5] Gordon, C. L., Lysecky, R

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Gerry A Pedraza, Texas A&M University; Sunay Palsole, Texas A&M University

Tagged Divisions

Computers in Education Division (COED)

1 2 Poorly Not Very Well Well Very Well Extremely Well Figure 3. Faculty's highest rated functions regarding Transcriptto's capabilities.(a) Breakdown of Lectures into Appropriate SegmentsThe responses indicate a generally positive reception towards Transcriptto's ability to segmentlectures appropriately. Most of the participants rated the tool as segmenting "Very Well" or"Extremely Well," suggesting effectiveness in its primary function of structuring contentconducive to online learning environments.(b) Quality of Polished Lecture SegmentsAfter polishing, the quality of the lecture segments was favorably received, with again a

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Jiping Li, University of Toronto; Zhiqiang Yin, University of Toronto; Hamid S Timorabadi P.Eng., University of Toronto

Tagged Divisions

Computers in Education Division (COED)

by executing a Python script with an AutoCAD driver and geospatial libraries toautomate the conversion of mapping the layout into geospatial coordinates in GeoJSON[6].Indoor Wi-Fi Access Point Strength Fingerprinting and Deep Learning (Appendix B andC)Within university buildings, there are numerous Wi-Fi access points distributed across everyfloor. The objective of this procedure is to collect data to train a deep neural network,CNNLoc[4], which is capable of processing the aggregate of Wi-Fi signals received as inputand providing users with precise positioning information as output.An access point signal detector was developed to automate our data collection.Map Visualization (Appendix D)We have opted for React Native as our application

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)

courses. In Proceedings of the 8th Australasian Conference on Computing Education-Volume 52 (pp. 157-163).8. Ericson, B. J., Denny, P., Prather, J., Duran, R., Hellas, A., Leinonen, J., ... & Rodger, S. H. (2022). Parsons problems and beyond: Systematic literature review and empirical study designs. Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education, 191-234.9. Prather, J., Homer, J., Denny, P., Becker, B. A., Marsden, J., & Powell, G. (2022, August). Scaffolding Task Planning Using Abstract Parsons Problems. In IFIP World Conference on Computers in Education (pp. 591-602). Cham: Springer Nature Switzerland.10. Sweller, J. (2011). Cognitive load theory. In The

Conference Session

ML and Generative AI Tools and Policies

Collection

2024 ASEE Annual Conference & Exposition

Authors

Alyson G. Eggleston, Pennsylvania State University; Robert J. Rabb P.E., Pennsylvania State University

Tagged Divisions

Computers in Education Division (COED)

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

Conference Session

The Best of Computers in Education Division (COED)

Collection

2024 ASEE Annual Conference & Exposition

Authors

John K. Estell, Ohio Northern University; Lisa Graham Robeson, Ohio Northern University; Ye Hong, Ohio Northern University; Stephany Coffman-Wolph, Ohio Northern University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

further narrative exploration or characterdevelopment provided, such as what it was like experiencing childbirth along the Trail [21]. Figures 1a and 1b. Screen captures of (a) player name request and (b) Fort Boise imagery from the 1990 IBM PC version of The Oregon Trail [13].Given that approximately one out of five women on the Trail were either pregnant or hadrecently given birth along their journey [22], and that many wives were young women travelingwith small children [23], more could be done to tell stories from this perspective as a fittingtestament to the fortitude of our emigrant ancestors. Finally, some of the dialogues experiencedwhen selecting the “Talk to people” option available at forts and landmarks include a

Conference Session

ML and Generative AI Tools and Policies

Collection

2024 ASEE Annual Conference & Exposition

Authors

Zifeng Liu, University of Florida; Rui Guo, University of Florida; Xinyue Jiao, New York University; Xueyan Gao, University of Florida; Hyunju Oh, University of Florida; Wanli Xing, University of Florida

Tagged Divisions

Computers in Education Division (COED)

Paper ID #43119How AI Assisted K-12 Computer Science Education: A Systematic ReviewZifeng Liu, University of Florida Zifeng Liu is a Ph.D. student and research assistant in School of Teaching & Learning, College of Education, University of Florida. Her research interests include educational data mining, artificial intelligence, and computer science education.Rui Guo, University of Florida Dr. Rui Guo is an instructional assistant professor of the Department of Engineering Education in the UF Herbert Wertheim College of Engineering. Her research interests include data science & CS education, Fair Artificial

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Samuel Opeyemi Falade, Texas A&M University; Kristi J. Shryock, Texas A&M University; Michael S Rugh, Texas A&M University; Andre Thomas, Texas A&M University

Tagged Divisions

Computers in Education Division (COED)

version III, andthe subsequent three play version IV. The next three participants are assigned to the controlgroup, and they only read the end screen for the game. To maintain anonymity, the differenttasks on the opening screen of the game are coded. The code "TB", "B", "TN", and "N"represents versions I, II, III, and IV respectively. The code "R" takes the participant to the endscreen, while "Quit" ends the game if the participant chooses not to continue with the study.After the activity, participants are asked to complete another survey which is similar to the pre-study survey to measure any changes in the previous responses. We adopted a combination of theState-trait Anxiety Inventory [18], Toronto Empathy Questionnaire [19], Game

Conference Session

Spotlight on Diverse Learners

Collection

2024 ASEE Annual Conference & Exposition

Authors

Sung Je Bang, Texas A&M University; Saira Anwar, Texas A and M University

Tagged Divisions

Computers in Education Division (COED)

-criteria/criteria-for- accrediting-engineering-programs-2022-2023/[8] E. Wheeler and R. L. McDonald, “Writing in Engineering Courses,” J. Eng. Educ., vol. 89, no. 4, pp. 481–486, Oct. 2000, doi: 10.1002/j.2168-9830.2000.tb00555.x.[9] J. Miller and R. Weinert, Spontaneous spoken language: Syntax and discourse. Oxford University Press: Oxford, 1998.[10] M. Demirezen, “The Recognition of Extended Simple Sentences as a Teaching Writing Problem,” Procedia - Soc. Behav. Sci., vol. 70, pp. 560–566, Jan. 2013, doi: 10.1016/j.sbspro.2013.01.093.[11] P. Collins, “Clause Types,” in The Handbook of English Linguistics, 1st ed., B. Aarts, A. McMahon, and L. Hinrichs, Eds., Wiley, 2020, pp. 131–144. doi: 10.1002/9781119540618.ch8.[12

Conference Session

Teaching with ML and Generative AI

Collection

2024 ASEE Annual Conference & Exposition

Authors

Anthony Cortez, Point Loma Nazarene University; Paul Daniel Schmelzenbach, Point Loma Nazarene University

Tagged Divisions

Computers in Education Division (COED)

of specifications. The text below is the problem given to the students.Create overlying lines with a randomized slope and intercept following the specificationsbelow: (a) Use rand() in combination with the appropriate math to create a random decimal value between -5 and 5 and store it in the variable m. (b) Store a random integer between 0 and 4 and store it in the variable b (c) Create a range of 20 different values stored in x between -10 and 10. (d) Create the array y of the line (using the standard formula of y = mx + b) (e) Make a plot of x and y displayed with the color green if the slope is positive (greater than 0) and red if it is negative. (f) Repeat (a)-(e), overlaying each of the plots on top of each other as long

Conference Session

Teaching with ML and Generative AI

Collection

2024 ASEE Annual Conference & Exposition

Authors

Han Kyul Kim, University of Southern California; Aleyeh Roknaldin, University of Southern California; Shriniwas Prakash Nayak, University of Southern California; Xiaoci Zhang, University of Southern California; Muyao Yang, University of Southern California; Marlon Twyman, University of Southern California; Angel Hsing-Chi Hwang, Cornell University; Stephen Lu, University of Southern California

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Computers in Education Division (COED)

. (a) Week 1 (b) Week 2Figure 6: Comparison of the pairwise Cosine distances among TF-IDF vectors of idea betweenGA and GB for Week 1 and Week 2ConclusionBased on the analyses above, we statistically validated that product ideas conceived with theassistance of ChatGPT are indeed perceived to be more creative compared to those solelyoriginating from students. While a systematic investigation of the root causes behind thisperceptual difference remains a subject for future investigation, our present finding identifies thedifferences in the linguistic patterns used in expressing these ideas as one potential reason.Notably, the ideas generated with ChatGPT tend to be more lengthy and detailed

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)

]. In another exam experiment, ChatGPT only got a 20.4 out of 40 points [3]. In a studyof generating answers to assignment questions about CS logic and theory, ChatGPT exhibited a“high degree of unreliability in answering a diverse range of questions pertaining to topics inundergraduate computer science” [4]. In another study, ChatGPT was used to completeassignments and tests for an introductory-level functional language programming course, and itonly got a B- grade [5].In another set of relevant studies, researchers investigated how ChatGPT could be used to aidstudents in computer science courses instead of how well ChatGPT itself performed in thecourses. One study investigated the effect of using ChatGPT on undergraduate

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)

an example of one the students practice materials. Please translate the following MIPS toC code. Assume that the variables f, g, h, i, and j are assigned to registers $s0, $s1, $s2, $s3, and$s4, respectively. Assume that the base address of the arrays A and B are in registers $s6 and $s7,respectively. • lw $t0, 20($s7) • lw $t1, 16($s7) • add $t0,$t0, $t1 • sll $t0,$t0,2 • add $t0,$t0,$s6 • lw $t1, 0($t0) • sub $s0,$s1,$t1 • What is the C code?Through this exercise, where students are tasked with translating MIPS instructions to C code,they not only reinforce their understanding of the MIPS architecture but also develop a deeperappreciation for the relationship between high-level programming languages and their

Conference Session

Robotics and Circuits

Collection

2024 ASEE Annual Conference & Exposition

Authors

Carlotta A Berry, Rose-Hulman Institute of Technology; Katie Nicole Faith Collins, Rose-Hulman Institute of Technology; Alejandro Marcenido Larregola, Rose-Hulman Institute of Technology

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

adjustment allowed the motorizedwheels to be lower than the caster wheels, improving the traction. However, this solution camewith a drawback: it increased the rocking motion of the robot, making it more challenging tocontrol. Thus, solution B was designed. As can be seen in Figure 4, solution B consists ofmerging the motor mount with the chassis by eliminating the gap that was causing the issue. Thisallowed all robots to have consistent traction. Gap Figure 4: (Left) Initial problem with traction due to the gap shown. (Middle) Solution A for the short-term allowing more traction but more rocking. (Right) Solution B eliminated the gap. It's important to note the reason behind having two solutions. Solution A was

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

Tagged Topics

Diversity

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Computers in Education Division (COED)

-4 20 5-20 6-20 7-20 8-20 9-20 0-20 1-20 2-20 4 -20 5-20 6-20 7-20 8-20 9-20 0-20 1-20 2-20 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 2 2 2 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 AY- AY- AY- AY- AY- AY- AY- AY- AY- AY- AY- AY- AY- AY- AY- AY- AY- AY- (a) Total number of courses (b) Total number of enrollments Figure 1: Historical CBTF GrowthThe impact of the CBTF is

Conference Session

Cybersecurity Topics

Collection

2024 ASEE Annual Conference & Exposition

Authors

Heena Rathore, Texas State University; Henry Griffith, San Antonio College

Tagged Divisions

Computers in Education Division (COED)

were asked to find flags presented in the formof a ciphertext that corresponds to a place or a person’s name. Teams members were required tocollaborate to decrypt the challenge. As soon as they decrypt the message, each team would dropa single pin (See Figure 1(c)) on the map for each deciphered location (limiting to one pin perlocation per team) on the map. The process involves saving a location pin then input the name ofthe location, followed by the “Group number.” (See Figure 1(b))Upon completing the decryption task, the group would be given a flag to another pin location andwere asked to inform the instructor. The instructor will then provide a clue to the subsequent maplocation by dropping the pin and the process continues. The amazing

Conference Session

ML and Generative AI Tools and Policies

Collection

2024 ASEE Annual Conference & Exposition

Authors

Lucas J. Wiese, Purdue University at West Lafayette; Alejandra J. Magana, Purdue University at West Lafayette

Tagged Divisions

Computers in Education Division (COED)

enough forstudents to become familiar with its use. Through the conceptual lens of Teacher Noticing Thisstudy examined (a) whether faculty saw the potential use of LLMs for teaching and learning, and(b) how they responded to the rapid impact of LLMs in the classroom before university-standardguidance. Via document analysis, we found that despite LLM chatbots being widespread forroughly 9 months before the Fall semester, only a third of faculty acknowledged its use in theclassroom. Faculty took three positions toward it: encouraged, discouraged, and prohibited. Asfound in qualitative analysis, most of the language was precautionary and discouraging. Throughthe lens of Teacher Noticing, we suggest that this is worrisome since faculty beliefs

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Deborah Moyaki, University of Georgia; Deborah Gbemisola Fabiyi, Washington State University; Nathaniel Hunsu, University of Georgia

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

mobile learning," Journal of E-Learning & Knowledge Society, Article vol. 18, no. 3, pp. 166-177, 2022, doi: 10.20368/1971-8829/1135622.[11] B. Marks and J. Thomas, "Adoption of virtual reality technology in higher education: An evaluation of five teaching semesters in a purpose-designed laboratory," Education and information technologies, vol. 27, no. 1, pp. 1287-1305, 2022 2022, doi: doi:10.1007/s10639- 021-10653-6.[12] N. N. Kuzmina, E. G. Korotkova, and S. M. Kolova, "Implementing E-Learning in the System of Engineering Students Training," ed: IEEE, 2021, pp. 818-823.[13] K. Cook-Chennault and I. Villanueva, Exploring perspectives and experiences of diverse learners' acceptance of online

Conference Session

Teaching with ML and Generative AI

Collection

2024 ASEE Annual Conference & Exposition

Authors

Abdulrahman AlRabah, University of Illinois Urbana-Champaign; Sophia Yang, University of Illinois Urbana-Champaign; Abdussalam Alawini, University of Illinois Urbana-Champaign

Tagged Divisions

Computers in Education Division (COED)

Paper ID #43159Optimizing Database Query Learning: A Generative AI Approach for SemanticError FeedbackAbdulrahman AlRabah, University of Illinois Urbana-Champaign Abdulrahman AlRabah is a Master of Science (M.S.) in Computer Science student at the University of Illinois at Urbana-Champaign. He holds a Graduate Certificate in Computer Science from the same institution and a Bachelor of Science in Mechanical Engineering from California State University, Northridge. He has experience in various industries and has served in multiple roles throughout his professional career, including in oil and gas and co-founding a food &

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Ankunda Kiremire, Louisiana Tech University; Kevin A Cherry, Louisiana Tech University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

team score webpages where all the team scores are displayed andupdated in real time, and Challenge webpages where student teams can access challenge filesduring the course of a Datastorm Challenge or event.Both the team score and Challenge webpages are designed in a gamified manner to make userinteraction with them more engaging. Figure 2 shows an example of both pages where the team isrepresented by a mouse character, and their progress through the challenges is represented by themouse progressing through a maze. The faster and more successful the team is in solving theirchallenges, the faster the mouse representing their team progresses through the maze. (a) The Team Score page shows the (b) The Challenge Page provides files

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Brayan A Díaz, North Carolina State University; Kevin Han, North Carolina State University

Tagged Divisions

Computers in Education Division (COED)

. (2021). Engineering communication in industry and cross- generational challenges: An exploratory study. European Journal of Engineering Education, 46(3), 389– 401. https://doi.org/10.1080/03043797.2020.1737646Campo, A., Michałko, A., Van Kerrebroeck, B., Stajic, B., Pokric, M., & Leman, M. (2023). The assessment of presence and performance in an AR environment for motor imitation learning: A case-study on violinists. Computers in Human Behavior, 146, 107810.Díaz, B., Delgado, C., Han, K., Lynch, C. (2023, June). Improving graduate engineering education through Communities of Practice approach: Analysis of implementation in computer science, robotics, and construction engineering courses. Papers

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Ahmed Ashraf Butt, Carnegie Mellon University; Eesha tur razia babar, University of California, Irvine; Muhsin Menekse, Purdue University, West Lafayette; Ali Alhaddad, Purdue University, West Lafayette

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

answering systems,” Applied Intelligence, vol. 53, no. 9, pp. 10602–10635, May 2023, doi: 10.1007/s10489-022-04052-8.[26] B. D. Lund and T. Wang, “Chatting about ChatGPT: how may AI and GPT impact academia and libraries?,” Library Hi Tech News, vol. 40, no. 3, pp. 26–29, 2023.[27] M. Soni and V. Wade, “Comparing Abstractive Summaries Generated by ChatGPT to Real Summaries Through Blinded Reviewers and Text Classification Algorithms,” arXiv preprint arXiv:2303.17650, 2023.[28] A. Katz, M. Norris, A. M. Alsharif, M. D. Klopfer, D. B. Knight, and J. R. Grohs, “Using natural language processing to facilitate student feedback analysis,” in 2021 ASEE Virtual Annual Conference Content Access, 2021.[29] T. Khan et al., “AR in

Conference Session

The Best of Computers in Education Division (COED)

Collection

2024 ASEE Annual Conference & Exposition

Authors

Phyllis J. Beck, Mississippi State University; Mahnas Jean Mohammadi-Aragh, Mississippi State University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

assumes an understandingof functions, function arguments, function return values, calling functions, branching, if/elif/elsestatements, variables, type conversions, input and output and output formatting.3.1.2 Exercise B: Alternating CipherExercise B was a more logically complex programming problem intended to take a beginnerprogrammer an hour to design and program. It assumes an understanding of the concepts inExercise A in addition to working with lists, loops, and working with strings. The alternatingcipher is a variation on the rail cipher and requires users to encrypt and decrypt input text byplacing each character of the message on alternating lines, ignoring spaces, and then creating asingle cipher text string by concatenating the two

Conference Session

Cybersecurity Topics

Collection

2024 ASEE Annual Conference & Exposition

Authors

Mahmoud K Quweider, The University of Texas Rio Grande Valley; Liyu Zhang; Alexis Aaron De La Cruz

Tagged Divisions

Computers in Education Division (COED)

will discuss in detail.1. Pedagogy Components: a. Cloud Computing i. Theory & Concepts ii. Lab Modules iii. Assessment iv. Q/A Sessions2. Platform Support: a. Primary: GCP (Google Gloud Platform) b. Secondary: AWS, Azure3. Degree Support Courses: a. Electives: AI/ML b. Required: Capstone Project4. Job Support Certifications: a. Primary: Cloud+ and GCP/AWS/Azure b. Secondary: Linux+We designed the CTaaS framework as a seamlessly integrated system where componentscomplement each other without requiring any extra effort beyond what is required by thecybersecurity degree. In the following, we go over CTaaS’s details. Cloud

Conference Session

Programming Education 2

Collection

2024 ASEE Annual Conference & Exposition

Authors

Timothy James, Purdue University; Alejandra J. Magana, Purdue University

Tagged Divisions

Computers in Education Division (COED)

, no. 4, pp. 789–809, Dec. 2020, doi: 10.1007/s10758-020-09441-x.[5] J. Goopio and C. Cheung, “The MOOC dropout phenomenon and retention strategies,” Journal of Teaching in Travel and Tourism, vol. 21, no. 2, pp. 177–197, 2021, doi: 10.1080/15313220.2020.1809050.[6] B. B. Morrison, L. E. Margulieux, and M. Guzdial, “Subgoals, context, and worked examples in learning computing problem solving,” in ICER 2015 - Proceedings of the 2015 ACM Conference on International Computing Education Research, Association for Computing Machinery, Inc, Jul. 2015, pp. 267–268. doi: 10.1145/2787622.2787733.[7] M. Yarmand, J. Solyst, S. Klemmer, and N. Weibel, “It feels like i am talking into a void: Understanding

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Sweni Shah; Hamid S Timorabadi P.Eng., University of Toronto; Sanjana Dasadia; Samreen Khatib Syed; Doaa Muhammad, University of Toronto

Tagged Divisions

Computers in Education Division (COED)

. In Figure 2,for instance, Mentee 4 is matched with Mentor 0, showcasing the algorithm's inclination towardsoptimizing for the best possible match based on overall compatibility. Figure 2: Test results for one-to-one test scenario. Check Appendix B for algorithm outputTo thoroughly assess the algorithm's performance in terms of speed and accuracy, a substantialdataset comprising 1000 mentees and 500 mentors was introduced. Each mentor's capacity wascapped at 2. The matching execution time, as outlined in Table 2, demonstrated efficiency at 4.08seconds which is lower than the anticipated value for the runtime. Given that many matchingalgorithms often exhibit a complexity of O(n2), where n is the number of users in the database,the algorithm's

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

David Reeping, University of Cincinnati; Aarohi Shah, University of Cincinnati

Tagged Divisions

Computers in Education Division (COED)

Literature Review of Empirical Research on ChatGPT in Education.” Rochester, NY, Sep. 06, 2023. doi: 10.2139/ssrn.4562771.[18] C. K. Lo, “What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,” Educ. Sci., vol. 13, no. 4, Art. no. 4, Apr. 2023, doi: 10.3390/educsci13040410.[19] C. M. L. Phillips, J. S. London, W. C. Lee, A. S. Van Epps, and B. A. Watford, “Reflections on the messiness of initiating a systematic literature review on broadening participation in engineering and computer science,” in 2017 IEEE Frontiers in Education Conference (FIE), Oct. 2017, pp. 1–8. doi: 10.1109/FIE.2017.8190482.[20] L. Krupp et al., “Unreflected Acceptance -- Investigating the Negative Consequences of ChatGPT

Conference Session

Programming Education 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

John Alexander Mendoza-Garcia, University of Florida

Tagged Divisions

Computers in Education Division (COED)

evidence. It is also possible to studystudents’ retention in a class before and after the change and track their persistence inengineering after several semesters. References[1] T. J. D’Zurilla, A. M. Nezu, and A. Maydeu-Olivares, “Social Problem Solving: Theory and Assessment.,” Social problem solving: Theory, research, and training., no. 1971, pp. 11–27, 2009, doi: 10.1037/10805-001.[2] K. Sorsdahl, D. J. Stein, and B. Myers, “Psychometric properties of the Social Problem Solving Inventory-Revised Short-Form in a South African population,” International Journal of Psychology, vol. 52, no. 2, pp. 154–162, 2017, doi: 10.1002/ijop.12192.[3] D. Kokotsaki, V. Menzies, and