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Progress Report on BE-TEC: An NSF S-STEM Project

Conference Session: Computing and Information Technology Division (CIT) Technical Session 7
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Afsaneh Minaie, Utah Valley University; Reza Sanati-Mehrizy, Utah Valley University
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

Paper ID #44219Progress Report on BE-TEC: An NSF S-STEM ProjectDr. Afsaneh Minaie, Utah Valley University Afsaneh Minaie is a Professor of Electrical and Computer Engineering at Utah Valley University. She received her B.S., M.S., and Ph.D. all in Electrical Engineering from the University of Oklahoma. Her research interests include gender issues in the academic sciences, embedded system, internet of things, wireless sensor network, and robotics.Dr. Reza Sanati-Mehrizy, Utah Valley University Reza Sanati-Mehrizy is a professor of Computer Science Department at Utah Valley University, Orem, Utah. He received his M.S. and

Relevance Score: 3.1319284

Shifts in Perceptions of Career Pathways: The Impact of an S-STEM Program on Lower-Income Computing Students

Conference Session: Computing and Information Technology Division (CIT) Technical Session 6
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Nivedita Kumar, Florida International University; Stephen Secules, Florida International University; Maimuna Begum Kali, Florida International University; Tiana Solis, Florida International University; Atota Bedane Halkiyo, Arizona State University; Mark Allen Weiss, Florida International University; Michael Georgiopoulos, University of Central Florida; Jacqueline Faith Sullivan, University of Central Florida; Ken Christensen P.E., University of South Florida; Angela Estacion
Tagged Divisions: Computing and Information Technology Division (CIT)

Paper ID #41948Shifts in Perceptions of Career Pathways: The Impact of an S-STEM Programon Lower-Income Computing StudentsMs. Nivedita Kumar, Florida International University Nivedita is pursuing her Ph.D. in Engineering & Computing Education at Florida International University. She has a background in computer science engineering. For her dissertation, Nivedita aims to uncover the caste-based inequities within engineering & computing education.Dr. Stephen Secules, Florida International University Stephen is an Assistant Professor Engineering and Computing Education at Florida International University. He has a

Relevance Score: 2.0879524

WIP: Skip the Lecture: A Decoding First Approach to Introductory Computing Education

Conference Session: Computing and Information Technology Division (CIT) Technical Session 5
Collection: 2023 ASEE Annual Conference & Exposition
Authors: David Zabner, Tufts University; Trevion S Henderson, Tufts University
Tagged Divisions: Computing and Information Technology Division (CIT)

Paper ID #39391WIP: Skip the Lecture: A Decoding First Approach to IntroductoryComputing EducationDavid Zabner, Tufts UniversityTrevion S Henderson, Tufts University Trevion Henderson is Assistant Professor of Mechanical Engineering at Tufts University. He earned his Ph.D. in Higher Education at the University of Michigan. ©American Society for Engineering Education, 2023 (WIP) Skip the Lecture: A Decoding First Approach to Introductory Computing Education David Zabner Trevion Henderson david.zabner@tufts.edu

Relevance Score: 0.63268316

Algorithmic Thinking: Why Learning Cannot Be Measured By Code-Correctness in a CS Classroom

Conference Session: Computing and Information Technology Division (CIT) Technical Session 4
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Alejandra Noemi Vasquez, Tufts University; Trevion S Henderson, Tufts University; David Zabner, Tufts University
Tagged Divisions: Computing and Information Technology Division (CIT)

Paper ID #42748Algorithmic Thinking: Why Learning Cannot Be Measured By Code-Correctnessin a CS ClassroomMs. Alejandra Noemi Vasquez, Tufts UniversityTrevion S Henderson, Tufts University Trevion Henderson is Assistant Professor of Mechanical Engineering and STEM Education at Tufts University. He earned his Ph.D. in Higher Education at the University of Michigan.Mr. David Zabner, Tufts University ©American Society for Engineering Education, 2024 Algorithmic Thinking: Why Learning Cannot Be Measured By Code-Correctness in a CS Classroom

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Paving Digital Infrastructure: Innovation Through an Educational Video Game Database

Conference Session: Computing and Information Technology Division (CIT) Technical Session 5
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Anthony Daniel Jones, Texas A&M University; Michael S Rugh, Texas A&M University; Meet Mahesh Gamdha, Texas A&M University; Tristen James Norman, Texas A&M University; Rose Myers, Texas A&M University; Kailee Meek, Texas A&M University; Amir Hossein Khazaei, Texas A&M University; Sherry Minh Nguyen, Texas A&M University; Ethan Thai Nguyen, Texas A&M University
Tagged Divisions: Computing and Information Technology Division (CIT)

such as SERA (2023), Sigma Nursing Conference (2023), Frontiers of Education (2023).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 Graduate Merit Fellowship from the Association of Former Students and was the College of Education and Human Development Distinguished Honor Graduate for Fall 2021. He has had multiple years of K–16 teaching in mathematics and science. As a research scientist, he leads research teams to study the effects of products created by the LIVE Lab which include educational video games, apps, simulations, and

Relevance Score: 0.25307328

Introducing Internet-of-Things (IoT) – A Remote Approach

Conference Session: Computing and Information Technology Division (CIT) Technical Session 4
Collection: 2023 ASEE Annual Conference & Exposition
Authors
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

Blocks(i) We will utilize the code blocks editor for listening to an Arduino input pin. Next, the analogvalue or digital state will be print out in the SerialM onitor window. To open the code panel, astudent needs to click the “Code” button.(ii) Next, s/he is asked to click on the SerialM onitor which is located at the bottom of the codepanel.(iii) To run the Arduino code, s/he should click “Start Simulation”, and observe the numbers inthe Serial Monitor during the interaction with the potentiometer. As the potentiometer input valuechanges by moving the pointer on the dial, the serial output value will change accordingly. Sincethe circuit includes two independent Arduinos, students can click back and forth between the twoArduinos while the

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Unleashing Video Benefits: Student Perceptions in a Flipped Programming Course

Conference Session: Computing and Information Technology Division (CIT) Technical Session 3
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Kwansun Cho, University of Florida; Umer Farooq, Texas A&M University; Minje Bang, Texas A&M University; Saira Anwar, Texas A&M University
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

MeasuresIn this study, we explored students’ perceptions of the two different kinds of videos they watchedbefore coming to the class for hands-on experience with the programming topic. The data wascollected for eight programming topics (7 weeks), where videos were available to students eachweek through the course management system. The eight topics are Arrays (A), Structures andClasses (SC), Constructors and other tools (C), Operator overloading (OO), Strings (S), Pointers(P), Inheritance (I), and Polymorphism and Virtual functions (PV). Students were asked tocomplete a small survey describing their perception of each video. Each participant had about aweek to answer the survey questions after watching the videos (concept and coding videos) thatwere

Relevance Score: 0.020994045

Apples or Oranges: A Step Back in Time to Understand Which Programming Language is for Novice Programmers

Conference Session: Computing and Information Technology Division (CIT) Technical Session 1
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Kwansun Cho, University of Florida; Umer Farooq, Texas A&M University; Saira Anwar, Texas A&M University
Tagged Divisions: Computing and Information Technology Division (CIT)

is for novice programmersAbstractIn this work-in-progress paper, the emphasis is to understand the perceptions about whichlanguage should be the first programming language. Computer programming is a fundamentalskill for novice engineers. However, over time, multiple programming languages have emergedand are being used as the first language for students. While in modern times, many schoolsaround the globe, particularly in the USA, consider Python’s syntax simplicity and versatility asa way to go, other places and traditional computer scientists consider C++’s efficiency as theirchoice. Similarly, many engineering schools introduce MATLAB as the first programminglanguage. While these decisions are made at the

Relevance Score: 0.020778716

Personalized Education with Generative AI and Digital Twins: VR, RAG and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development

Conference Session: Computing and Information Technology Division (CIT) Technical Session 7
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Yu-Zheng Lin, The University of Arizona; Karan Patel, The University of Arizona; Ahmed H Alhamadah, The University of Arizona; Sujan Ghimire, The University of Arizona; Jesus Pacheco; Banafsheh Saber Latibari, The University of Arizona; Soheil Salehi, The University of Arizona; Pratik Satam, University of Arizona
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

Inspection Training Group 1 (w/o Adaptive Mechanism) Group 2 (w/ Adaptive Mechanism) Participants 22 6 Samples 82 72 Statistic Time Taken (s) Hits Percentage Time Taken (s) Hits Percentage x) Mean (¯ 68.93 78 % 48.94 83 % Standard Deviation (σ) 34.93 17 % 23.89 14 % Minimum Value (xmin ) 27.00 25 % 10.00 50 % Maximum Value (xmax ) 208.00 100

Relevance Score: 0.019297374

Task, Knowledge, Skill, and Ability: Equipping the Small-Medium Businesses Cybersecurity Workforce

Conference Session: Computing and Information Technology Division (CIT) Technical Session 6
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Aadithyan Vijaya Raghavan, Cleveland State University; Chansu Yu, Cleveland State University
Tagged Divisions: Computing and Information Technology Division (CIT)

4 shows the snippet ofkeywords extracted from a document [21], along with their score (S). Score (S) is based onkeyword features (term casing, term position, term frequency normalization, term relatedness tocontext, term different sentence) and is computed by the YAKE! Algorithm [31]. Lower the valueof S, the more significant the keyword [31].Third. To eliminate similar keywords, we employed a de-duplication process based on similarityalgorithms such as Levenshtein similarity [35], Jaro-Winkler [36], and Hamming Distance[37, 38, 39]. We used Levenshtein similarity because it works on the principle of the minimumnumber of single-character edits required to change one word into the other [38].For example, take a group of similar keywords like

Relevance Score: 0.018181376

Teaching IoT in Both Physical and Virtual Environments

Conference Session: Computing and Information Technology Division (CIT) Technical Session 5
Collection: 2023 ASEE Annual Conference & Exposition
Authors: James R. Mallory, Rochester Institute of Technology (COE); Edmund Lucas, National Technical Institute for the Deaf; William Arnold
Tagged Divisions: Computing and Information Technology Division (CIT)

Paper ID #36938Teaching IoT in Both Physical and Virtual EnvironmentsProf. James R. Mallory, Rochester Institute of Technology (COE)Edmund Lucas, National Technical Institute for the DeafWilliam Arnold ©American Society for Engineering Education, 2023Teaching IoT in Both Physical and Virtual EnvironmentsAuthors: Arnold, W., Fontaine, J., Griggs, S., Huff, G., Johnson, D., Linares, C., Patel, S.,Reader, J., Roman, J., Sawaqed, Y., Yadav, R., Lucas, E. & Mallory, J. National TechnicalInstitute for the Deaf / Rochester Institute of TechnologyPrimary Division: Computing and Information Technology DivisionSecondary Division: Education

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Auto-graded Scaffolding Exercises For Theoretical Computer Science

Conference Session: Computing and Information Technology Division (CIT) Technical Session 3
Collection: 2023 ASEE Annual Conference & Exposition
Authors: Jeff Erickson, University of Illinois Urbana-Champaign; Jason Xia, University of Illinois at Urbana - Champaign; Eliot Wong Robson; Tue Do; Aidan Tzur Glickman; Zhuofan Jia; Eric Jin; Jiwon Lee; Patrick Lin; Steven Pan; Samuel Ruggerio; Tomoko Sakurayama, University of Illinois, Urbana-Champaign ; Andrew Yin; Yael Gertner, University of Illinois, Urbana-Champaign; Brad Solomon, University of Illinois, Urbana-Champaign
Tagged Divisions: Computing and Information Technology Division (CIT)

Notes in Computer Science, no. 12225. Springer, 2020, pp. 3–14. [4] L. D’Antoni, D. Kini, R. Alur, S. Gulwani, M. Viswanathan, and B. Hartmann, “How can automatic feedback help students construct automata?” ACM Trans. Comput.-Hum. Interact., vol. 22, no. 2, pp. 9:1–9:24, 2015. [5] E. L. Deci, H. Eghrari, B. C. Patrick, and D. R. Leone, “Facilitating internalization: The self determination theory perspective,” Journal of Personality, vol. 62, pp. 119–142, 1994. [6] E. L. Deci and R. Ryan, “Self-determination theory,” in Handbook of Theories of Social Psychology, P. A. M. van Lange, A. W. Kruglanski, and E. T. Higgins, Eds. Sage Publications Ltd., 2012, vol. 1, ch. 20, pp. 416–436. [7] Y. Du, A. Luxton-Reilly, and P. Denny, “A

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Developing the ITL framework and committing to inquiry as a method for reducing equity gaps in high-impact, computer science and engineering courses

Conference Session: Computing and Information Technology Division (CIT) Technical Session 7
Collection: 2023 ASEE Annual Conference & Exposition
Authors: Sagnik Nath, University of California, Santa Cruz; Jennifer Quynn, University of California, Santa Cruz; Jose Renau
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

responsive teaching," Journal of teacher education, vol. 53, no. 2,pp. 106- 116, 2002.[02]R. T. White and R. F. Gunstone, "Metalearning and conceptual change," International Journal ofScience Education, vol. 11, no. 5, pp. 577-586, 1989.[03]D. Kuhn and S. Pearsall, "Developmental origins of scientific thinking," Journal of cognition andDevelopment, vol. 1, no. 1, pp. 113-129, 2000.[04]T. Litzinger, L. R. Lattuca, R. Hadgraft and W. Newstetter, "Engineering education and thedevelopment of expertise," Journal of Engineering Education, vol. 100, no. 1, pp. 123-150, 2011.[05]J. R. Frederiksen, M. Sipusic, M. Sherin and E. W. Wolfe, " Video portfolio assessment: Creating aframework for viewing the functions of teaching.," Educational Assessment, vol. 5

Relevance Score: 0.01742348

Designing Course Level-Appropriate Mentoring for Computing Students

Conference Session: Computing and Information Technology Division (CIT) Technical Session 1
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Shamima Mithun, Indiana University-Purdue University, Indianapolis; Xiao Luo, Oklahoma State University
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

% 60% 40% 18% 15% 20% 0% CIT 12000 CIT 21400 Agree Neither agree or disagree Table 7: Perceptions of Mentees towards Mentoring CIT 12000 CIT 21400 The mentors gave me the sense that s/he and I The mentors modeled how to overcome challenges shared similarities in the background. and reach personal goals. The mentors helped me explore resources to The mentors showed me how to treat failed succeed academically. attempts as a

Relevance Score: 0.017071456

Factors Affecting the Future Career Pathway Decisions of Lower-income Computing Students

Conference Session: Computing and Information Technology Division (CIT) Technical Session 7
Collection: 2023 ASEE Annual Conference & Exposition
Authors: Nivedita Kumar, Florida International University; Bailey Bond-Trittipo, Florida International University; Maimuna Begum Kali, Florida International University; Stephen Secules, Florida International University; Angela Estacion, WestEd; Mark Allen Weiss, Florida International University; Michael Georgiopoulos, University of Central Florida; Ken Christensen P.E., University of South Florida; Tiana Solis, Florida International University; Jacqueline Faith Sullivan, University of Central Florida
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

Affecting the Future Career Pathway Decisions of Lower-income Computing Students1. IntroductionWithin research on broadening participation in computing, the experience and perspectives ofundergraduate students have been important elements of exploration. As undergraduate studentsare experts of their own experience, conducting research that focuses on understanding theirperspective can help those who organize programmatic efforts to respond to student needs andconcerns. This paper emerges from the context of a specific National Science Foundation (NSF)-funded Scholarships in Science, Technology, Engineering, and Mathematics (S-STEM) program.As with all S-STEM programs, Florida Information Technology Graduation

Relevance Score: 0.016590472

BOARD #110: WIP: A Reconfigurable Testbed for Assessing Cognitive Workload in N-back and Multi-Object Tracking Tasks

Conference Session: Computing and Information Technology Division (CIT) Poster Session
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Yug Patel, Missouri University of Science and Technology; Sanjana Shangle, University of Texas at Dallas; Asir Abrar, Missouri University of Science and Technology; Venkata Sriram Siddhardh Nadendla, Missouri University of Science and Technology; K Krishnamurthy, Missouri University of Science and Technology
Tagged Divisions: Computing and Information Technology Division (CIT)

consolidate all data into a database for comprehensiveanalysis.CASE STUDY: GENERATED PILOT EXPERIMENTS AND DATA COLLECTION PLANThe testbed has been validated by generating three pilot experiments. These pilot experimentsare designed using parameters depicted in figures 4 and 5 for both N -back tasks and MOT taskrespectively. Each of these pilot experiments comprises of R runs, where each run contains T trialsand each trial has S sub-trials. The participant performs any given task once in every sub-trial. Inthe case of both N -back pilot experiments, we chose R = 3, T = 9 and S = 20. Furthermore,the value of N is also varied randomly between 1 and 3 (i.e. N = 1 for low workload, N = 2 formedium workload, and N = 3 for high workload) across trials to

Relevance Score: 0.016261917

BOARD #108: DARE-AI: Discovery, Analysis, Research and Exploration Based Experiential Learning Platform for Teaching Integrated AI and Cybersecurity

Conference Session: Computing and Information Technology Division (CIT) Poster Session
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Utsab Khakurel, Howard University; Danda B Rawat, Howard University
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

(Figure 27) represents a male passenger who survived despite gender being a negativefactor in the model’s prediction. His high Pclass and fare ($35.5) played a crucial role inincreasing his survival probability. The cumulative SHAP value graphs (Figs. 28, 29) furtherhighlight these trends by selecting the few vital causes from the trivial many, showing thatinstance 3’s survival was driven by gender, wealth, and class, aligning with historical data where75% of women survived compared to only 19% of men. On the other hand, instance 55 survivedsolely due to his high social class, with gender contributing the least to his survival.3 Evaluation and FindingsIn this section, we evaluate the effectiveness of the DARE-AI labs through surveys conducted

Relevance Score: 0.016081145

Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory CS Courses

Conference Session: Computing and Information Technology Division (CIT) Technical Session 7
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Ismael Villegas Molina, University of California, San Diego; Audria Nikitza Montalvo, University of California, San Diego; Benjamin Ochoa, University of California, San Diego; Paul Denny, University of Auckland; Leonard Porter, University of California, San Diego
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

resource for NNES studentsin computer science education, providing tailored support that enhances their learning experience and overcomes languagebarriers. R EFERENCES [1] V. Agarwal, Y. Chuengsatiansup, E. Kim, Y. LYu, and A. G. Soosai Raj, “An analysis of stress and sense of belonging among native and non-native english speakers learning computer science,” in Proceedings of the 53rd ACM Technical Symposium on Computer Science Education-Volume 1, 2022, pp. 376–382. [2] S. Alaofi and S. Russell, “A validated computer terminology test for predicting non-native english-speaking cs1 students’ academic performance,” in Australasian Computing Education Conference, 2022, pp. 133–142. [3

Relevance Score: 0.015584037

The Impact of Extended Reality-based Digital Approaches to Support STEM Learning for Autistic Students

Conference Session: Computing and Information Technology Division (CIT) Technical Session 5
Collection: 2024 ASEE Annual Conference & Exposition
Authors: J Cecil, Oklahoma State University; Avinash Gupta, University of Illinois at Urbana-Champaign
Tagged Divisions: Computing and Information Technology Division (CIT)

an opportunity to create computer based art. Assessment of learning was through pre- and post-testquestions . A total of eight students were involved in this study; four from middle school (grades 6, 7 and 8)and four from high school (grades 9 to 12).Assembly VLEsTen students interacted with the Assembly VLEs (5 middle school s, 5 high school). Among the middle schoolparticipants, 3 students were able to understand the target assembly concepts in the first round of learninginteractions; two of the students needed an additional round of learning interactions for learning the sameconcepts. Among the high school students, four students were able to demonstrate an understanding of allconcepts after one round of interactive sessions with the VLEs

Relevance Score: 0.0155696

Enhancing Object-Oriented Programming Education through Virtual Learning and Adaptive AI Technologies

Conference Session: Computing and Information Technology Division (CIT) Technical Session 8
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Juan Felipe Calderón, Universidad Andres Bello, Viña del Mar, Chile; Matias Isaac Vargas
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

flexibility engine within the LMSand adjustments to the underlying framework to facilitate adaptability in the dynamic assignmentof materials, tasks, and evaluations, utilizing a more extensive cluster model encompassing abroader spectrum of student characteristics.References[1] S. Park, “Analysis of Time-on-Task, Behavior Experiences, and Performance in Two Online Courses With Different Authentic Learning Tasks,” The International Review of Research in Open and Distributed Learning, 2017, doi: 10.19173/irrodl.v18i2.2433.[2] Z. Zen, Reflianto, Syamsuar, and F. Ariani, “Academic Achievement: The Effect of Project-Based Online Learning Method and Student Engagement,” Heliyon, 2022, doi: 10.1016/j.heliyon.2022.e11509.[3] S. B

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BOARD #107: An Efficient Approach for Muscle Segmentation and 3D Modeling Using Keypoint Tracking in MRI Scans

Conference Session: Computing and Information Technology Division (CIT) Poster Session
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Mengyuan Liu, Northeastern University; Jeongkyu Lee, Northeastern University
Tagged Divisions: Computing and Information Technology Division (CIT)

will enable students to visually exploreand interact with muscle segmentation processes, including keypoint selection, boundarytracking, and 3D reconstruction. This hands-on approach aims to foster a deeper, more intuitiveunderstanding of the algorithm’s functionality and its practical application in real-world medicalimaging scenarios.AcknowledgmentThis project was funded in part by the Northeastern TIER 1 seed grant.References [1] J. Zhu, B. Bolsterlee, B. V. Chow, C. Cai, R. D. Herbert, Y. Song, and E. Meijering, “Deep learning methods for automatic segmentation of lower leg muscles and bones from mri scans of children with and without cerebral palsy,” NMR in Biomedicine, vol. 34, no. 12, p. e4609, 2021. [2] R. Ni, C. H. Meyer, S. S

Relevance Score: 0.015174627

Integrating Microlearning Instructional Approach into an Introductory Data-management Course

Conference Session: Computing and Information Technology Division (CIT) Technical Session 9
Collection: 2023 ASEE Annual Conference & Exposition
Authors: Shamima Mithun, Indiana University - Purdue University Indianapolis; Rajagopal Sankaranarayanan, University of Texas at Austin
Tagged Divisions: Computing and Information Technology Division (CIT)

CIT21400 course. In this study, we integrated the microlearning instructional approach into CIT 21400to help engage students and retain the knowledge gained through the introduction to datamanagement course. CIT 21400 is a required class for all CIT students and a prerequisite for allother courses in the data-management concentration. Figure 1 shows the current plan of study forthe CIT data-management concentration; we draw particular attention to CIT 21400’s position asa prerequisite course for all data-management courses. Approximately 140 students who enroll inCIT 21400 will directly benefit per academic year. We anticipate seeing learning and performancegains over time as students continue in their programs as an outcome of our research

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Teaching SOLID Software Design Principles Using Peer Instruction—A Pilot Study

Conference Session: Computing and Information Technology Division (CIT) Technical Session 3
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Bhuvaneswari Gopal, University of Nebraska, Lincoln
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

, has gained attention from the computingeducation community over the last few years [1]. The focus in PI is active student engagementthrough discussion, involving students in the answering and discussion of multiple-choicequestions. This is typically accomplished by obtaining real-time student feedback through theuse of student response systems in class as the students learn the topic.SOLID is an acronym that denotes five basic principles widely used in designing software builton the .NET platform. S stands for SRP (Single Responsibility Principle), O for OCP (OpenClosed Principle) L for LSP (Liskov Substitution Principle), I for ISP (Interface SegregationPrinciple) D for DI (Dependency Inversion Principle). The main purpose of these

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Barkplug 2.0 and Beyond - a Chatbot for Assisting Students in High DFW Courses

Conference Session: Computing and Information Technology Division (CIT) Technical Session 4
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Jason M. Keith, Iowa State University of Science and Technology; Amin Amirlatifi, Mississippi State University; Sudip Mittal, Mississippi State University; Subash Neupane, Mississippi State University; HIMANSHU TRIPATHI, Mississippi State University
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

cybersecurity and digital forensics). Further iterations of the chatbot will focus onimproving its ability to facilitate collaborative learning, assist with project-based assessments,and provide actionable feedback to students and instructors.References[1] Maderer, J. “Artificial Intelligence Course Creates AI TeachingAssistant,”https://news.gatech.edu/news/2016/05/09/artificial-intelligence-course-creates-ai-teaching-assistant, May 2016, accessed January 2025.[2] Chopra, S., Gianforte, R., and Sholar, J. “Meet Percy: The CS 221 Teaching AssistantChatbot,” ACM Transactions on Graphics, Vol. 1 (1), December 2016.[3] Lluna, A. P. “Creation and Development of an AI Teaching Assistant,” Master’s Thesis,Universitat Politecnica de Catalunya, 2017/2018.[4

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Exploring Collaborative Success of Undergraduate Students within a Cyberlearning Environment Using Gamification

Conference Session: Computing and Information Technology Division (CIT) Technical Session 6
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Gabriel Eduardo Prieto, Florida International University; Neila Bennamane, Florida International University; Agoritsa Polyzou, Florida International University; Peter J Clarke, Florida International University
Tagged Divisions: Computing and Information Technology Division (CIT)

described in Section 3.1. The extra credit points from SEP-CyLE were computed as apercentage ratio of the student(s) with the highest number of virtual points. The student(s) withthe most virtual points received 3% extra credit course points.The grades for each course project deliverable consist of four components: presentation (21%),demonstration (12.6%), documentation (50.4%), and peer evaluation (16%). The peer evaluationconsists of the members of a team grading each other using a peer evaluation rubric provided bythe instructor. The rubric includes four criteria: Helping - assistance provided by a team memberto other team members, Participating - contribution and attendance by a team member at teammeetings, Questioning - the level at which the

Relevance Score: 0.014616046

Exploring the Potential of Deep Learning for Personalized Learning Environments

Conference Session: Computing and Information Technology Division (CIT) Technical Session 4
Collection: 2023 ASEE Annual Conference & Exposition
Authors: Fadhla Binti Junus, Purdue University at West Lafayette (PPI); Sean P. Brophy, Purdue University at West Lafayette (COE)
Tagged Divisions: Computing and Information Technology Division (CIT)

indrawing our conclusion. Nevertheless, this work has an added value as a basis for us toconduct more extensive research in the future. Additionally, academics will have a wideropportunity to explore deep learning to produce more novel educational solutions since ourstudy discovered that only a small number of studies had investigated the application of thisAI technology.References[1] M. King, R. Cave, M. Foden, and M. Stent, “Personalised education From curriculum to career with cognitive systems,” 2016.[2] T. J. Sejnowski, The deep learning revolution. Cambridge: The MIT Press, 2018.[3] J. S. Groff, “Personalized learning: The state of the field & future directions,” 2017. [E-book]. Available: https://dam

Relevance Score: 0.014226213

Identifying Factors That Influence Engineering Students' Outcome Expectancy and Learning Self-Efficacy in a Flipped CS1 Course

Conference Session: Computing and Information Technology Division (CIT) Technical Session 1
Collection: 2023 ASEE Annual Conference & Exposition
Authors: Ashish Aggarwal, University of Florida; Griffin Pitts, University of Florida; Sage Bachus, University of Florida; Sarah Rajkumari Jayasekaran, University of Florida; Saira Anwar, Texas A&M University
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

253 600Students were asked to self-report their GPA. GPA was based on a scale of 4, with an “A” being a4.00, a “B” being a 3.00, a “C” being a 2.00, a “D” being a 1.00, and an “S” being a 0.00. Someclasses also used a “+” or “–” system. A “+” adds 0.33 to the base grade, while a “-” subtracts0.33. For example, a “B+” would quantitatively be a 3.33 (3.00 + 0.33), while a “B-” would be a2.77 (3.00 - 0.33).Data was gathered on students’ expected majors. Out of a total of 600 students, 311 (51.8%) weremechanical and/or aerospace engineering students, 114 (19.0%) were civil and/or environmentalengineering students, 102 (17.0%) were biomedical engineering students and 73 (12.2%) studentshad other majors. This data can be seen in Figure 2

Relevance Score: 0.014083234

A network analysis of the Twitter-Rxiv ecosystem for purveyors of science misinformation in preprints on the COVID-19 pandemic

Conference Session: Computing and Information Technology Division (CIT) Technical Session 8
Collection: 2023 ASEE Annual Conference & Exposition
Authors: David C. Brown, University of North Carolina at Charlotte; Erfan Al-Hossami, University of North Carolina at Charlotte; Zhuo Cheng; Alyssa Lasmarias Alameda; Tia Nicole Johnson; Mesbah Uddin, University of North Carolina at Charlotte; Daniel Andrew Janies
Tagged Divisions: Computing and Information Technology Division (CIT)

Review of Centrality Measures in Social Networks,” Business & Information Systems Engineering, vol. 2, no. 6, pp. 371–385, Dec. 2010, doi: 10.1007/s12599-010-0127-3.[10] K. Das, S. Samanta, and M. Pal, “Study on centrality measures in social networks: a survey,” Social Network Analysis and Mining, vol. 8, no. 1, p. 13, Feb. 2018, doi: 10.1007/s13278-018-0493-2.[11] R. J. Abdill and R. Blekhman, “Tracking the popularity and outcomes of all bioRxiv preprints,” Elife, vol. 8, Apr. 2019, doi: 10.7554/eLife.45133.[12] B.-C. Björk and D. Solomon, “The publishing delay in scholarly peer-reviewed journals,” J. Informetr., vol. 7, no. 4, pp. 914–923, Oct. 2013, doi: 10.1016/j.joi.2013.09.001.[13] R. S. Mehta and N. A. Rosenberg

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On Time-based Exploration of Student Performance Prediction

Conference Session: Computing and Information Technology Division (CIT) Technical Session 1
Collection: 2023 ASEE Annual Conference & Exposition
Authors: Abdulmalek Al-Gahmi, Weber State University; Kyle D. Feuz, Weber State University; Yong Zhang, Weber State University
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

. Zhang. "On Time-based Exploration of LMS Data andPrediction of Student Performance", 2022 ASEE Annual Conference & Exposition,Minneapolis, MN, 2022, August. ASEE Conferences, 2022.[2] R. Conijn, C. Snijders, A. Kleingeld and U. Matzat, "Predicting Student Performance fromLMS Data: A Comparison of 17 Blended Courses Using Moodle LMS," IEEE Transactions onLearning Technologies, vol. 10, no. 01, pp. 17-29, 2017.[3] D. Gašević, S. Dawson, T. Rogers and D. Gasevic, "Learning analytics should not promoteone size fits all: The effects of instructional conditions in predicting academic success," TheInternet and Higher Education, vol. 28, pp. 68-84, 2016.[4] M. Riestra-Gonza ́lez, M. d. P. Paule-Ruíz and F. Ortin, "Massive LMS log data analysis