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
interested in software programming and User Experience designs. He is proficient with C, C++ and Python and familiar with JavaScript, PSQL, Intel FPGA Verilog and ARM Assembly(ARMv7-A). Personal Website: https://junhao.caDr. Hamid S. Timorabadi, 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, 2023 WIP - A Face Recognition Application to Improve In-Person LearningAbstractA face recognition application that enables instructors
, University of Toronto Sowrov Talukder is a Computer Engineering student at the University of Toronto helping to improve programming labs in education.Mr. Parth Sindhu, University of TorontoDr. Hamid S. Timorabadi, 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, 2023 WIP: Lab Container: An environment to manage a student’s time to complete programming labs while providing effective
. 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
students.Limitations and Future WorkThe frameworks must be validated through qualitative research, and the work should beexpanded to include integration pathways.AcknowledgementThis work was funded by the National Science Foundation (NSF) with Grant No DRLGEGI008182. However, the authors alone are responsible for the opinions expressed in thiswork and do not reflect the views of the NSF.References[1] B. Vittrup, S. Snider, K. K. Rose, and J. Rippy, "Parental perceptions of the role of media and technology in their young children’s lives," Journal of Early Childhood Research, vol. 14, no. 1, pp. 43-54, 2016.[2] A. Sullivan, M. Bers, and A. Pugnali, "The impact of user interface on young children’s computational thinking," Journal of Information
design their class.Among the multiple ways to reveal collaborative problem-solving processes, temporal submissionpatterns is one that is more scalable and generalizable in Computer Science education. In thispaper, we provide a temporal analysis of a large dataset of students’ submissions to collaborativelearning assignments in an upper-level database course offered at a large public university. Thelog data was collected from an online assessment and learning system, containing the timestampsof each student’s submissions to a problem on the collaborative assignment. Each submission waslabeled as quick (Q), medium (M), or slow (S) based on its duration and whether it was shorter orlonger than the 25th and 75th percentile. Sequential compacting and
) creating examples and projectsis one delivery mechanism but there could be a steep learning curve student will encounter [27], 6) currentdemands from larger employers who may not all use these techniques, and lastly [28]; 7) Creating newtracks is possible but requires new resources and faculty to teach them. Given these benefits and challenges,many engineering students are still often pushed to take computer science course(s) to compensate for theirlack of in-department offerings. This research looks to help overcome several aspects of these barriers inthe discipline specific domains of architectural engineering (AE) and material science and engineering(MATSE). Both fields were selected given their renewed emphasis and need for more data skills as
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
classified below as subtopics: 1. Established identity in CS with themselves and others. 2. Personal experiences and challenges in CS that are gender and/or race related. 3. Psycho-social characteristics experienced. 4. Personal feedback/recommendations for promoting equity, inclusion, and representation of black women in CS.Each subtopic and corresponding findings are discussed below.4.1 Established Identity in CS with Themselves & OthersFindings for this classification were based on five key questions that were asked during the focusgroup sessions: Q1: Do the participant(s) exhibit an identity towards computer science? Q2: Do the participant(s) consider themselves as computer scientists? Q3: Are they proud to be
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
noted as one persistent attribute that students exhibit during theseexperiences. For instance, one aspect of Behroozi et al.’s work [7] compared anxiety levels thattheir participants exhibited while conducting mock technical interviews either in a public settingor in a private setting. It was determined that participants who conducted technical interviews ina public setting exhibited higher levels of anxiety than their counterparts who were in a privatesetting. Similarly, Hall and Gosha [23] conducted a study that measured the correlation ofanxiety and preparation in a technical interview that targeted junior and senior CS majors at aSoutheastern Historically Black College/University (HBCU) in the United States. Keyinformation collected during
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
engineering educators to research more holisticstudent networks than previously studied. Results of these future studies may yield moregeneralizable and accurate conclusions about which social practices help students succeed.Acknowledgements This material is based upon work supported by the second author's National ScienceFoundation Graduate Research Fellowship under Grant No. DGE1745048. Any opinions,findings, and conclusions or recommendations expressed in this material are those of theauthor(s) and do not necessarily reflect the views of the National Science Foundation. References[1] A. Kozulin, Vygotsky’s Psychology: A biography of ideas. Cambridge, MA: Harvard University Press, 1990.[2
institution.” Journal of Hispanic Higher Education, vol. 20, no. 3, pp. 297-312, 2021.[4] M. F. Rogers-Chapman. "Accessing STEM-focused education." Education and Urban Society, vol. 46, no. 6, pp. 716-737, 2014.[5] J. L. Petersen and J. S. Hyde. "Trajectories of self-perceived math ability, utility value and interest across middle school.” Ed. Psych., vol. 37, no. 4, pp. 438-456, 2017.[6] D. L. and Z. Lavicza, “Dissecting a Cube as a Teaching Strategy for Enhancing Students’ Spatial Reasoning,” Proceedings of Bridges 2019, pp. 319–326,[7] u/diegolieban, “GeoGebra and 3D printing: Mathematics as a creative practice,” GeoGebra, Feb. 03, 2020. www.geogebra.org/m/pkfzccjw (accessed Jan. 16, 2021).[8] Y. Gao, S. Liu, M. M. Atia, and A
.[6] K. Mite-Baidal, C. Delgado-Vera, E. Solís-Avilés, A. H. Espinoza, J. Ortiz-Zambrano, and E. Varela-Tapia, “Sentiment analysis in education Domain: A systematic literature review,” in Technologies and Innovation, R. Valencia-García, G. Alcaraz-Mármol, J. Del Cioppo- Morstadt, N. Vera-Lucio, and M. Bucaram-Leverone, Eds., Cham: Springer International Publishing, 2018, pp. 285–297.[7] Y. Sun, Z. Ming, Z. Ball, S. Peng, J. K. Allen, and F. Mistree, “Assessment of Student Learning Through Reflection on Doing Using the Latent Dirichlet Algorithm,” J. Mech. Des., vol. 144, no. 12, Sep. 2022, doi: 10.1115/1.4055376.[8] U. Naseem, I. Razzak, K. Musial, and M. Imran, “Transformer based Deep Intelligent
students inthe college of engineering and college of arts and sciences. Student responses to open-endedquestions were scored manually by two trained raters in accordance with Grohs et al.’s publishedscoring guide [15]. Scores for each response were assigned and rationales recorded. An initialsample of 20% of the responses were scored individually by each rater. These scores were thencompared across raters to develop a consensus for interpreting student-generated text [16] andscoring guidelines normalized across raters. The remaining 80% of responses were split evenlybetween the two raters. This process required 50 human hours of work.Facilitated ScoringUsing the RStudio and the R Shiny package we import a spreadsheet of the raw text
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
Practices and Processes,” Hollylynne S. Lee etel. developed a framework using the work of statistics educators and researchers to investigatehow data science practices can inform work in K–12 education. Their framework buildsfundamental practices and processes from data science [19]. The math field has contributed to data science research via the Common Core StateStandards Initiative (CCSSI), which is a joint project to develop common K–12 reading andmath standards designed to prepare students for college and careers. The CCSSI includes a datascience section for elementary students that focuses on data collection, data type, function,analysis type, and sample [20]. Similarly, the Launch Years Data Science Course Frameworkprovides broad
offering online sections of courses to students that want the flexibilitythat they facilitate, if their primary concern is student performance. We found no statistically sig-nificant difference in the overall performance of students that elect to take a course online relativeto those that elect to take it in person. Taking courses online may, however, have a substantialnegative impact on a student’s sense of belonging. This effect is particularly pronounced for un-derrepresented minority students and first generation students, but not present in women.References [1] B. Bizot and S. Zweben, “Generation cs, three years later,” On the Internet at https://cra. org/generation-cs- three-years-later/(visited August 2019), 2019. [2] T. Camp, W. R
institutions are beginning toimplement technical interview practices into the classroom as assignments, group projects,warm-ups, class exercises, and dedicating a class to the topic. For instance, literature shows thatexposing students to technical interview exercises in their Data Structure course(s) is one of themost effective methods. One reason being that students are exposed to the process early on but itbecomes natural for them to think as interviewees based on the construct of these particularcourses. Likewise, literature suggests that introducing the technical interview process early in astudent’s computational development could better gauge the overall effectiveness of thisemployed initiative. Yet, the number of studies that reflect such
forearly childhood. As a future work, the models and framework developed could be branched intoseveral qualitative research studies for validation. Additionally, AI inclusion for early childhoodlearning could be studied.AcknowledgementsThis work was funded by the National Science Foundation (NSF) with Grant No DRLGEGI008182. However, the authors alone are responsible for the opinions expressed in thiswork and do not reflect the views of the NSF.References[1] A. Strawhacker and M. U. Bers, "Promoting positive technological development in a Kindergarten makerspace: A qualitative case study," European Journal of STEM Education, vol. 3, no. 3, p. 9, 2018.[2] B. Vittrup, S. Snider, K. K. Rose, and J. Rippy, "Parental perceptions of the
of applications that were introduced in the workshop.Upon completion of the workshop, the participants were given an eight-question exit post-trainingsurvey shown in Figure 2. There were six quantitative questions using a five point or a three-pointLikert scale as well as two qualitative questions. The two qualitative questions were also used aspedagogical tools based on experiential learning best practices. Question 7’s goal was to elicit apositive self-reflection while Question 8 reinforced learning through internalization andsummarization. 1. Exiting this workshop, I learned something new about AI concepts, applications, and ethics (1 - strongly disagree to 5 - strongly agree). 2. I have a better understanding of AI and how to
University, India. He extensively traveled within and abroad for technical lectures viz., USA, Germany, Belarus, China, Hong Kong, Thailand, Malaysia, Singapore.Dr. Shanmuganeethi Velu, P.E., Dr. V.Shanmuganeethi, Professor, Department of Computer Science and Engineering. He has been work- ing in the domain of Education Learning Analytics, web technologies, programming Paradigm, Instruc- tional technologies and Teaching aˆ C” Learning PraDr. P. MalligaDr. Dinesh Kumar K.S.A. Dr. K S A Dineshkumar, Assistant Professor, Department of Civil Engineering. He has been working in the domain of Structural Engineering, Geographical Information System, Sustainable development, Smart City, Instructional technologies and Teaching