ASEE PEER - Search Results

Displaying all 21 results

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: 0.016379982

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: 0.016197901

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

Relevance Score: 0.015682641

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: 0.015284801

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

Relevance Score: 0.014952811

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: 0.014722163

BOARD #109: The Journey of Korean Students in U.S. Colleges: Following Their Hearts to Become Computer Engineers and Scientists

Conference Session: Computing and Information Technology Division (CIT) Poster Session
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Hyeree Cho, Purdue Polytechnic Institute, Purdue University – West Lafayette; Woongbin Park, Purdue University at West Lafayette
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

without thedominance of societal biases.References[1] T. Camp, W. R. Adrion, B. Bizot, S. Davidson, M. Hall, S. Hambrusch, E. Walker and S. Zweben, “Generation CS: The growth of computer science,” ACM Inroads, vol. 8, no. 2, pp. 44–50, May 2017. [Online]. Available: https://doi:10.1145/3084362. [Accessed Jan 14, 2025].[2] T. G. Zimmerman, D. Johnson, C. Wambsgans and A. Fuentes, “Why latino high school students select computer science as a major,” ACM Trans. Comput. Educ., vol. 11, no. 2, pp. 1–17, Jul. 2011. [Online]. Available: https://doi:10.1145/1993069.1993074. [Accessed Jan 14, 2025].[3] S. R. Roy, “Educating Chinese, Japanese, and Korean international students: Recommendations to American professors

Relevance Score: 0.013509983

Assessment of Large Language Models for Wildcard Match Identification

Conference Session: Computing and Information Technology Division (CIT) Technical Session 4
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Claire Han; Abel Andres Reyes-Angulo, Michigan Technological University; Sidike Paheding, Fairfield University
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

flexible choice for applicationslike cookie classification and wildcard matching in cybersecurity.3.3.3 Flan-T5Flan-T5 is an enhanced version of the T5 model that incorporates instruction fine-tuning 15 . By training on a mixture of tasks phrasedas instructions, Flan-T5 improves its ability to follow task descriptions and generalize to new tasks. This makes Flan-T5 particularlyeffective in zero-shot and few-shot learning scenarios, where the model needs to perform well on tasks it has not explicitly beentrained on. In the context of identifying wildcard matches in cookies, Flan-T5’s improved understanding of instructions can lead tomore accurate and reliable classification results.4 Results4.1 Experimental SetupThe experimental

Relevance Score: 0.0128166955

Enhancing Academic Performance and Instructional Design Compliance through LLM-Integrated Virtual Learning Environments

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; Marco Aguilera; Martin Ignacio Gil; Maria Elena Truyol, Universidad Andres Bello, Santiago, Chile; Danilo Leal, Universidad Andres Bello; Claudia Bascur, Universidad Andres Bello, Santiago, Chile
Tagged Divisions: Computing and Information Technology Division (CIT)

in education andopens the door to new opportunities for personalization and adaptability in virtual environments.Integrating advanced technologies with robust pedagogical approaches is essential to transformteaching and learning in the digital age.References[1] S. Martín, E. López-Martín, A. Moreno-Pulido, R. Meier, and M. Castro, “A Comparative Analysis of Worldwide Trends in the Use of Information and Communications Technology in Engineering Education,” Ieee Access, 2019, doi: 10.1109/access.2019.2935019.[2] O. Kuzu, “Digital Transformation in Higher Education: A Case Study on Strategic Plans,” Vysshee Obrazovanie v Rossii = Higher Education in Russia, 2020, doi: 10.31992/0869-3617-2019-29-3- 9-23.[3] B. R. Aditya

Relevance Score: 0.012510419

A Framework for Understanding the Role of Generative AI in Engineering Education: A Literature Review

Conference Session: Computing and Information Technology Division (CIT) Technical Session 2
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Prarthona Paul, University of Toronto; Chirag Variawa, University of Toronto
Tagged Divisions: Computing and Information Technology Division (CIT)

education, ultimately preparing students for a rapidly evolvingtechnological landscape.References[1] M. R. Chavez, T. S. Butler, P. Rekawek, H. Heo and W. L. Kinzler, "Chat Generative Pre-trained Transformer: why we should embrace this technology," American Journal of Obstetrics and Gynecology, vol. 228, no. 6, pp. 706-711, 2023.[2] G. Debjania and J.-B. Souppeza R. G., "Generative AI In Engineering Education," in UK and Ireland Engineering Education Research Network Annual Symposium, Belfast, 2024.[3] A. Johri, A. S. Katz, J. Qadir and A. Hingle, "Generative artificial intelligence and engineering education," Journal of Engineering Education, vol. 112, no. 3, p. 572–577, 2023.[4] D. De Silva, O. Kaynak, M. El-Ayoubi, N. Mills, D

Relevance Score: 0.012158984

Developing an introductory machine learning course

Conference Session: Computing and Information Technology Division (CIT) Technical Session 5
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Caroline Crockett, University of Virginia
Tagged Divisions: Computing and Information Technology Division (CIT)

and curriculum developers select the correct learning goals and activities for theirspecific student population.References [1] S. Isaac Flores-Alonso, N. V. M. Diaz, J. Kapphahn, et al., “Introduction to AI in under- graduate engineering education,” in 2023 IEEE Frontiers in Education Conference (FIE), College Station, TX, USA: IEEE, Oct. 18, 2023, pp. 1–4, ISBN: 9798350336429. DOI: 10.1109/FIE58773.2023.10343187. [2] S. Khorbotly, “Machine learning: An undergraduate engineering course,” in 2022 ASEE Illinois-Indiana Section Conference Proceedings, Anderson, Indiana: ASEE Conferences, Apr. 2022, p. 42 132. DOI: 10.18260/1-2--42132. [3] R. DeMara, A. Gonzalez, A. Wu, et al., “A crcd experience: Integrating machine learning

Relevance Score: 0.012083696

Engaging and Effective Assessments in the Age of Generative AI: Student Perceptions and Recommendations

Conference Session: Computing and Information Technology Division (CIT) Technical Session 8
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Danushka Bandara, Fairfield University; Djedjiga Belfadel, Fairfield University
Tagged Divisions: Computing and Information Technology Division (CIT)

, limiting insights into how undergraduate students orthose in other disciplines might experience redesigned assessments. The short-term focus of the studyalso means that long-term impacts on learning and skill retention remain unexplored. Additionally,studies could examine the impact of redesigned assessments on instructor workload, studentengagement, and equity and accessibility, ensuring that innovative assessment practices benefit alllearners.ReferencesAnthropic. (2024). Claude [Large language model]. https://www.anthropic.com/Google. (2024). Gemini [Large language model]. https://gemini.google.com/Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial intelligence–enabledpersonalized recommendations on learners

Relevance Score: 0.012083696

Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course

Conference Session: Computing and Information Technology Division (CIT) Technical Session 5
Collection: 2025 ASEE Annual Conference & Exposition
Authors: James Weichert, Virginia Polytechnic Institute and State University; Hoda Eldardiry, Virginia Polytechnic Institute and State University
Tagged Divisions: Computing and Information Technology Division (CIT)

. For example, Scenario 3 on‘general-purpose’ AI is inspired by the EU AI Act’s requirement [28] that proprietors of ‘generalpurpose’ AI systems report details about model architecture and training processes to national AIauthorities. Each scenario, along with the proposed AI regulations that Congress can vote on, aredescribed in Appendix B. To start the game, only members of Evil Inc. are told internal companyinformation that motivates their lobbying efforts. For example: “Evil Inc.’s large language modelis only successful because its model architecture is kept a secret, so Evil Inc. should preventCongress from requiring the disclosure of any model architecture information.” Each round, EvilInc. members decide how to distribute a limited

Relevance Score: 0.011201886

BOARD #106: Investigating Factors Influencing Performance in an Introductory Programming Course

Conference Session: Computing and Information Technology Division (CIT) Poster Session
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Amanda Nicole Smith, University of Florida; Sage Bachus, University of Florida; Ashish Aggarwal, University of Florida
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

. Communications in Computer and Information Science, H. Florez and H. Astudillo, Eds. Springer, Cham, 2025, vol. 2237, accessed: 21-Oct-2024. [Online]. Available: https://doi.org/10.1007/978-3-031-75147-9 4 [3] K. Shah, P. Lee, D. Barretto, and S. N. Liao, “A qualitative study on how students interact with quizzes and estimate confidence on their answers,” in Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1, ser. ITiCSE ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 32–38. [Online]. Available: https://doi.org/10.1145/3430665.3456377 [4] S. N. Liao, “Early identification of at-risk students and understanding their behaviors,” Ph.D. dissertation, UC San

Relevance Score: 0.010723216

Earthquake Prediction: Irregular Time-series Forecasting with Deep Learning

Conference Session: Computing and Information Technology Division (CIT) Technical Session 5
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Neda Farahmandi, University of Massachusetts Dartmouth; Ashok Patel, University of Massachusetts Dartmouth
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

Relevance Score: 0.008273141

BOARD #111: WIP: Predictive Wear Balancing and Approximation for Efficient Non-Volatile Main Memory Management

Conference Session: Computing and Information Technology Division (CIT) Poster Session
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Marjan Asadinia, California State University, Northridge; Sherrene Bogle, California Polytechnic State University Humboldt ; Rowena Quinn
Tagged Divisions: Computing and Information Technology Division (CIT)

expressed in this paper are those of the authors anddo not necessarily reflect the views of the NSF.References [1] A. Ehrmann, T. Blachowicz, G. Ehrmann, and T. Grethe, “Recent developments in phase- change memory,” Applied Research, Jun. 2022, doi: https://doi.org/10.1002/appl.202200024. [2] R. Azevedo, J. D. Davis, K. Strauss, P. Gopalan, M. Manasse, and S. Yekhanin, “Zombie memory: Extending memory lifetime by reviving dead blocks,” in Proceedings of the Inter- national Symposium on Computer Architecture (ISCA), 2013. [3] H. Luo et al., “Write Energy Reduction for PCM via Pumping Efficiency Improvement,” ACM Transactions on Storage, vol. 14, no. 3, pp. 1–21, Aug. 2018. [4] J. Fan, S. Jiang, J. Shu, Y. Zhang, and W. Zhen

Relevance Score: 0.00810599

Active Learning and Specifications Grading for Undergraduate Algorithms and Data Structures courses

Conference Session: Computing and Information Technology Division (CIT) Technical Session 3
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Mahima Agumbe Suresh, San Jose State University
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

objectives mapped to eachquestion. Occassionaly, if the questions were phrased in a confusing manner, thresholds werereduced. Overall, the cutoffs were consistent with traditional grading letter grade cutoffs.Programming exams contained specifications similar to lab assignments (as described below),with tasks summing up to particular letter grades.Example specifications for a lab assignmentTask 1. Padovan Sequence:[LP] Part 1: In a file called Padovan.txt, write the pseudocode to recursively compute the nthPadovan number . A padovan number is an extension to the Fibonacci series that is defined by therelation: P(n) = P(n-2) + P(n-3). P(0)=P(1)=P(2)=1. Clearly state your base case(s).[LP] Part 2: Implement the pseudocode in a function called unsigned

Relevance Score: 0.0076424005

Academic Performance and Satisfaction with AI-supported Problem-solving Activities in a Course on Management Information Systems in an Industrial Engineering Program.

Conference Session: Computing and Information Technology Division (CIT) Technical Session 3
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Roberto Patricio Carú, Universidad Andres Bello; Juan Felipe Calderón, Universidad Andres Bello, Viña del Mar, Chile
Tagged Topics: Diversity
Tagged Divisions: Computing and Information Technology Division (CIT)

points, with Group 1's average being5.81 and Group 2's being 4.91. This indicates that, on average, Group 1 achieved higher gradesthan Group 2.Applying ANOVA to compare performance (final grade) between Group 1 and Group 2 yieldsan F-statistic value of 116.8963 and a p-value of approximately 7.411684e-13. The p-value isextremely low (much lower than any standard significance threshold, such as 0.05), indicating astatistically significant difference between the two groups' final grades. This suggests thatacademic performance (measured by final grade) significantly differs between Group 1 andGroup 2.Analysis of Students' Work ExperienceBoth groups belong to the evening session, mainly consisting of students working during the day.Additionally

Relevance Score: 0.007019993

A Follow-up Study of a Redesigned Cybersecurity Lab Course

Conference Session: Computing and Information Technology Division (CIT) Technical Session 2
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Peng Li, East Carolina University; Sohan Gyawali, East Carolina University
Tagged Divisions: Computing and Information Technology Division (CIT)

programmingassignments," Computer Science Education, vol. 15, no. 2, pp. 83–102, 2005. doi:10.1080/08993400500150747.[2] C. Douce, D. Livingstone, and J. Orwell, "Automatic test-based assessment of programming:A review," Journal on Educational Resources in Computing (JERIC), vol. 5, no. 3, pp. 4–es,2005. doi: 10.1145/1163405.1163409.[3] P. Li and L. Toderick, "An automatic grading and feedback system for e-learning ininformation technology education," in Proc. 2015 ASEE Annu. Conf. & Expo., Seattle, WA,USA, 2015, pp. 26.179.1–26.179.11. [Online]. Available: https://peer.asee.org/23518[4] C. L. Hull, "Simple trial and error learning: A study in psychological theory," PsychologicalReview, vol. 37, no. 3, pp. 241–256, 1930. doi: 10.1037/h0073614.[5] S. H

Relevance Score: 0.005403993

Bridging the Gap: Exploring Concurrent Enrollment in External Online Courses Among Computer Science Students

Conference Session: Computing and Information Technology Division (CIT) Technical Session 4
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Tyler Clark, SageFox Consulting Group ; Rebecca Zarch, SageFox Consulting Group; Mikaela Hurd
Tagged Divisions: Computing and Information Technology Division (CIT)

andprofessional trajectories of CS students.References[1] D. Shah. “By the numbers: MOOCs in 2021.” Class Central. Accessed: Feb. 20, 2025. [Online.] Available: https://www.classcentral.com/report/mooc-stats-2021/[2] L. J. Sax, M. A. Kanny, T. A. Riggers-Piehl, H. Whang, and L. N. Paulson. “‘But I’m not good at math’: The changing salience of mathematical self-concept in shaping women’s and men’s STEM aspirations,” Research in Higher Education, vol. 58, no.7, pp. 763–794, Feb. 2017, doi: 10.1007/s11162-017-9450-6.[3] National Center for Education Statistics, 2020, “Completions,” Integrated Postsecondary Education Data System (IPEDS). [Online]. Available: https://nces.ed.gov/ipeds/use-the-data[4] S. Mithun and X