Conference Session

COED: All about That Math

Collection

2023 ASEE Annual Conference & Exposition

Authors

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Computers in Education Division (COED)

a better understanding of the subject and the ability to use and apply it [11].A Survey conducted by Poçan, S., Altay, B. & Yaşaroğlu, C [1] showed the effects of using appson the success and motivation of 73 students in a high school algebra class. The findingsrevealed that mobile technology applications positively impact the learning process. Fabian,Topping, and Barron [2] explored the effects of mobile technology on the attitudes andachievements of 52 elementary school students. They found that mobile technology results inpositive student responses, improving their performance. Yussop, Annamalai, and Salam [3]investigated to find out the effectiveness of a particular mobile application. They found that byusing the app, students

Conference Session

COED Programming Education 1: Students, Motivation, and Mastery

Collection

2023 ASEE Annual Conference & Exposition

Authors

Thomas Rossi, Penn State Behrend; Paul C. Lynch, Penn State Behrend

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Computers in Education Division (COED)

Learning Methods: Definitions, Comparisons, and Research Bases," Journal of College Teaching, vol. 36, no. 5, pp. 14-20, 2007.[5] T. Ruutman and H. Kipper, "Teaching Strategies for Direct and Indirect Instruciton in Teaching Engineering," in Proceedings of 14th International Conference on Interactive Collaborative Learning, Slovakia, 2011.[6] A. Poulsen, K. Lam, S. Cisneros and T. Treust, "ARCS Model of Mtivational Design," November 2008. [Online]. [Accessed December 2014].[7] S. Bjorklund, J. Parente and D. Sathianathan, "Effects of Faculty Interaction and Feedback on Gains in Student Skills," Journal of Engieering Education, vol. 93, no. 2, pp. 153-160, 2004.[8] P. Hsieh, J. R. Sullivan and N. S. Guerra, "A Closer

Conference Session

Simulations and Virtual Learning

Collection

2024 ASEE Annual Conference & Exposition

Authors

David M. Feinauer P.E., Virginia Military Institute; James C. Squire P.E., Virginia Military Institute; Gerald Sullivan, Virginia Military Institute

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Computers in Education Division (COED)

questions, the mean confidence in the response on a scale from 1 to5, with 1 indicating highest confidence, and the standard deviation of the confidence responses.The delta or difference between each performance metric computed by subtracting the discreteresults from the continuous results is perhaps the most illustrative lens for exploring the results.Table 2 - Table of objective performance results from users of the simulation tools. Question Percent Mean Median Std Dev Mean Std Dev Performance Correct [%] Time [s] Time [s] Time [s] Confidence Confidence Q1 Cont. 86.67 66.05 42.26 60.58 1.63 0.93 Q1 Discrete 71.88 81.35

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Alexander Hicks, Virginia Polytechnic Institute and State University; Cliff Shaffer, Virginia Polytechnic Institute and State University

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Computers in Education Division (COED)

from additional scaffolding in office hours.References [1] M. Ball, J. Hsia, H. Pon-Barry, A. DeOrio, and A. Blank, “Teaching TAs To Teach: Strategies for TA Training,” in Proceedings of the 51st ACM Technical Symposium on Computer Science Education, (Portland OR USA), pp. 477–478, ACM, Feb. 2020. [2] E. McDonald, G. Arevalo, S. Ahmed, I. Akhmetov, and C. Demmans Epp, “Managing TAs at Scale: Investigating the Experiences of Teaching Assistants in Introductory Computer Science,” in Proceedings of the Tenth ACM Conference on Learning @ Scale, (Copenhagen Denmark), pp. 120–131, ACM, July 2023. [3] S.-H. Ko and K. Stephens-Martinez, “What Drives Students to Office Hours: Individual Differences and Similarities,” in Proceedings

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

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

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Computers in Education Division (COED)

to 0.5 Hz. (f) Repeat steps a-e, overlaying each plot on top of the others, as long as the random amplitude is less than 9. (Once it is 9 or larger, stop) (g) Create a title that displays the number of sine waves that were generated in your plot. (Note: This number can change with each running of your script file. Therefore, create a variable to keep track of this count.) (h) Label the x and y axes with “Time (s)” and “Amplitude (m)”, respectively. (i) Add a legend that shows the frequency of each plotted sine wave. Each label in your legend should include the appropriate variable, number, and units. For example: f = 0.56192 Hz.The exam was designed to test different levels of critical thinking and coding skills

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Linda Davis Ahlstrom, Utah State University; Oenardi Lawanto, Utah State University; Cassandra J McCall, Utah State University; Michaela Harper, Utah State University; Wade H Goodridge, Utah State University; Daniel Kane, Utah State University

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Computers in Education Division (COED)

usedAI technologies we can see that education in engineering is changing. How we anticipate theserapid changes will be of upmost importance in engineering programs at every university.AcknowledgementsThis material is based upon work supported by the National Science Foundation through AwardNo. 2346881. Any opinions, findings, conclusions, or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.References[1] H. Al-Juboori and G. Noonan, “Leveraging the Power of Digital Immersive Technologies to Enhance Engineering Education and Learning,” 2024 IEEE Glob. Eng. Educ. Conf. EDUCON Glob. Eng. Educ. Conf. EDUCON 2024 IEEE, pp. 1–5, May 2024, doi

Conference Session

COED: Grading Systems

Collection

2023 ASEE Annual Conference & Exposition

Authors

Chenyan Zhao, University of Illinois, Urbana-Champaign; Matthew West, University of Illinois, Urbana-Champaign; Mariana Silva, University of Illinois, Urbana-Champaign

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Diversity

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Computers in Education Division (COED)

: https://doi.org/10.1111/j.1540-4609.2012.00366.x [3] C. A. Shaffer and S. H. Edwards, “Scheduling and student performance,” in Proceedings of the 16th Annual Joint Conference on Innovation and Technology in Computer Science Education, ser. ITiCSE ’11. New York, NY, USA: Association for Computing Machinery, 2011, p. 331. [Online]. Available: https://doi.org/10.1145/1999747.1999842 [4] S. Willman, R. Lindén, E. Kaila, T. Rajala, M.-J. Laakso, and T. Salakoski, “On study habits on an introductory course on programming,” Computer Science Education, vol. 25, no. 3, pp. 276–291, 2015. [5] G. Schraw, T. Wadkins, and L. Olafson, “Doing the things we do: A grounded theory of academic procrastination,” US, pp. 12–25, 2007. [Online

Conference Session

The Best of Computers in Education

Collection

2023 ASEE Annual Conference & Exposition

Authors

Jayma Koval, Georgia Institute of Technology; Diley Hernandez, Georgia Institute of Technology; Tom McKlin; Douglas Edwards, Georgia Institute of Technology; Rafael A. Arce-Nazario; Joseph Carroll-Miranda; Isaris Rebeca Quinones Perez, University of Puerto Rico, Rio Piedras; Lilliana Marrero-Solis; Jason Freeman, Georgia Institute of Technology; Taneisha Lee Brown; Pascua Padro; Stephen Garrett; Analia E. Rao; Marion Usselman, Georgia Institute of Technology

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Diversity

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Computers in Education Division (COED)

theCollective Self-Esteem Scale [35] and included three of the original MIBI-T seven subscales(centrality, private regard, and public regard). We used this scale with the purpose of exploringstudents’ ethnic identity identification [36]. Because Latinx ethnic identity can be complex andvaried, we developed an initial question to allow the students to self-identify ethnically(Latin/Hispanic, Puerto Rican/Boricua, etc.), they then answered follow-up questions related tothat identity such as “I have a strong sense of belonging to other _____ people,” and “Mostpeople think that ______(s) are as smart as people of other groups.”Sense of Belongingness in Computer Science: Items were selected from the Sense of Social andAcademic Fit (in STEM) instrument [37

Conference Session

Computers in Education Division (COED) Track 6.D

Collection

2025 ASEE Annual Conference & Exposition

Authors

Raymond Deji Olamijulo, Morgan State University; Kingsley Matthew, Morgan State University; Oluwafemi Samuel Ifesanmi, Morgan State University; Abiola Olayinka Ajala, Morgan State University; Pelumi Olaitan Abiodun, Morgan State University; Oludare Adegbola Owolabi P.E., Morgan State University

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Computers in Education Division (COED)

. Sage publications.[21] Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methodsapproaches (4th ed.). Thousand Oaks, CA: Sage.[22] Garris, R., Ahlers, R., & Driskell, J. E. (2002). Games, motivation, and learning: A researchand development agenda. Simulation & Gaming, 33(4), 441-461.[23] Deterding, S., Holmes, B., Dedes, M., & Zimmerman, L. (2011). From game design elementsto gamefulness: Defining gameful design. In Proceedings of the 2011 chi conference on humanfactors in computing systems (pp. 3025-3032).[24] Sedighian, K., & Sedighian, S. (2014). The effectiveness of game-based learning: A reviewof the literature. Computers & Education, 75, 1-15.[25] Shute, V. J., Wang, L., Greiff, S., Zhao, W

Conference Session

Computers in Education Division (COED) Track 4.C

Collection

2025 ASEE Annual Conference & Exposition

Authors

Hossein Jamali, University of Nevada, Reno; Andrew L DeBolt; Henry Dalton, University of Nevada, Reno; Jenavieve K Layosa, University of Nevada, Reno; Ilana Rose Macy; Ponkoj Chandra Shill, University of Nevada, Reno; David Feil-Seifer, University of Nevada, Reno; Frederick C Harris, University of Nevada, Reno; Sergiu Dascalu, University of Nevada, Reno; Rui Wu, East Carolina University

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Computers in Education Division (COED)

rapidly evolving field. Asthe platform continues to evolve, it is expected to play an increasingly important role inpreparing the next generation of robotics professionals.AcknowledgmentsThis work was supported by the National Science Foundation (NSF) under Federal Award#DUE-2142360. The authors would like to thank the students and instructors who par-ticipated in the development and testing of the FORE platform, as well as the Universityof Nevada, Reno, for providing the necessary resources and support.References [1] Shill, P. C., Wu, R., Jamali, H., Hutchins, B., Dascalu, S., Harris, F. C., & Feil- Seifer, D. (2023). WIP: Development of a student-centered personalized learning framework to advance undergraduate robotics education

Conference Session

COED: Spotlight on Diverse Learners

Collection

2023 ASEE Annual Conference & Exposition

Authors

Vijesh J. Bhute, Imperial College London; Ellen Player; Deesha Chadha, Imperial College London

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Diversity

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Computers in Education Division (COED)

virtual learning environment in a university class,” Comput. Educ., vol. 56, no. 2, pp. 495–504, 2011, doi: 10.1016/j.compedu.2010.09.012.[2] J. Barker and P. Gossman, “The learning impact of a virtual learning environment : students’ views,” Teach. Educ., vol. 5, no. 2, pp. 19–38, 2013.[3] H. Waheed, S. U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani, and R. Nawaz, “Predicting academic performance of students from VLE big data using deep learning models,” Comput. Human Behav., vol. 104, no. November 2018, p. 106189, 2020, doi: 10.1016/j.chb.2019.106189.[4] J. Kuzilek, J. Vaclavek, Z. Zdrahal, and V. Fuglik, “Analysing Student VLE Behaviour Intensity and Performance.,” in Transforming Learning with

Conference Session

COED: Cybersecurity Education

Collection

2023 ASEE Annual Conference & Exposition

Authors

Sai Sushmitha Sudha; SaiSuma Sudha; Ahmad Y. Javaid, The University of Toledo; Xiaoli Yang

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Computers in Education Division (COED)

comprehension of the subjects in cybersecurity. Following the lecturesessions, the students appeared to have improved their knowledge and understanding of thesubject, as evidenced by the rise in the average value of each question in the post-surveycompared to the pre-survey. This may be ascribed to the thorough lecture materials and practicallab exercises that were made available to the students, which helped to reinforce the ideas andprovide them with hands-on experience using the knowledge acquired. The findings support theidea that providing cybersecurity education to undergrad students can significantly affect theircomprehension of and readiness for the sector. Table 2 Questions of Quiz Survey S. No. Questions of

Conference Session

COED: Spotlight on Diverse Learners

Collection

2023 ASEE Annual Conference & Exposition

Authors

Heidi Huang, University of Michigan, Ann Arbor; Kevin Yan, University of Michigan, Ann Arbor; Andrew Deorio, University of Michigan, Ann Arbor

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Diversity

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Computers in Education Division (COED)

results support the generalizability of Doebling et al.’s findings. We too observed that womenstudents attended office hours more frequently than men. Similarly, we observed that URM statusdid not have a statistically significant association with office hours usage.5.2 LimitationsOne limitation of our study is that we cannot disentangle remote learning from the pandemic. Asa result, the pandemic could have caused interesting student behaviors without affecting usagepatterns. Because remote learning was immediately adopted following the onset of the pandemic,we cannot separate how remote learning and pandemic onset interacted in our final results.An example of possible interference would be if pandemic onset caused widespread mental

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

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Computers in Education Division (COED)

require careful calibration of new algorithms created withinTranscriptto and other AI APIs that would allow us to perform that complex functionality.References[1] F. Martin, A. Ritzhaupt, S. Kumar, and K. Budhrani, “Award-winning faculty online teaching practices: Course design, assessment and evaluation, and facilitation,” Internet High. Educ., vol. 42, pp. 34–43, Jul. 2019, doi: 10.1016/j.iheduc.2019.04.001.[2] M. Kebritchi, A. Lipschuetz, and L. Santiague, “Issues and Challenges for Teaching Successful Online Courses in Higher Education: A Literature Review,” J. Educ. Technol. Syst., vol. 46, no. 1, pp. 4–29, Sep. 2017, doi: 10.1177/0047239516661713.[3] C.-S. Li and B. Irby, “An Overview of Online Education: Attractiveness

Conference Session

Simulations and Virtual Learning

Collection

2024 ASEE Annual Conference & Exposition

Authors

J Cecil, Oklahoma State University

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Computers in Education Division (COED)

Laboratories,Los Alamos National Laboratory and the Mozilla Foundation.REFERENCES [1] Forcael, E., Glagola, C., and González, V. (2012). ”Incorporation of Computer Simulations into Teaching Linear Scheduling Techniques.” J. Prof. Issues Eng. Educ. Pract., 138(1), 21–30 [2] Adams, W.K., Reid, S., LeMaster, R., McKagan, S.B., Perkins, K.K., Dubson, M., and Wieman. C.E. (2008a). A study of educational simulations part I—Engagement and learning. Journal of Interactive Learning Research, 19(3), 397-419.[3] Adams, W.K., Reid, S., LeMaster, R., McKagan, S.B., Perkins, K.K., Dubson, M., and Wieman, C.E. (2008b). A study of educational simulations part II—Interface design. Journal of Interactive Learning

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)

pressure of an exam situation. This also readiedstudents to be able to interact and carefully evaluate responses by the AI. For some students,ChatGPT-3.5’s initial response did not satisfy the requirements of the test question. This actuallyproduced a very high level of engagement. By this stage, students had developed expertise of theproblem, and had to work toward nudging the AI to get a correct response. Because of theirprevious knowledge of the problem, students were better able to identify differences andsimilarities with their code. While engaged in this careful comparison, several students gainednew insights, or even new methods. The process of nudging the AI toward the correct answer isreminiscent of improving one’s learning by teaching or

Conference Session

Computers in Education Division (COED) Track 5.C

Collection

2025 ASEE Annual Conference & Exposition

Authors

Christopher J. Lowrance, United States Military Academy; Edward Sobiesk, United States Military Academy; Jean R.S. Blair, United States Military Academy; Christa M Chewar, United States Military Academy

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Computers in Education Division (COED)

. Statement Cliff’s Delta Effect Size Interpretation Value of Course 0.563 Large Evaluate Emerging Tech 0.759 Large Analyze Cyberspace 0.786 Large Apply Critical Mindset 0.625 Large Explore & Integrate 0.821 Large Table 2: Cliff’s Delta Effect Sizes for Pre- and Post-Course Survey ResponsesTo further assess student perceptions, we conducted module-specific surveys at the end of eachmajor topic. These surveys asked students to rate their level of agreement with statements suchas: “The assignment(s) increased my

Conference Session

Computers in Education Division (COED) Track 6.D

Collection

2025 ASEE Annual Conference & Exposition

Authors

Mitchell Gerhardt, Virginia Polytechnic Institute and State University; Andrew Katz, Virginia Polytechnic Institute and State University

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Computers in Education Division (COED)

Online Textbook – Intro to Software Design CS 2114 – Software Design & Data OpenDSA CS 2114 Online Textbook – Structures Software Design & Data Structures CS 2104 – Intro to Problem-Solving for CS Whimbey, A., Lochhead, J., & Narode, R. (2013). Problem solving and comprehension (7. ed). Routledge CS 2505 – Intro to Computer Organization I Patt, Y. N., & Patel, S. J. (2004). Introduction to computing systems: From bits and gates to C

Conference Session

Computers in Education Division (COED) Track 3.E

Collection

2025 ASEE Annual Conference & Exposition

Authors

Oyku Eren Ozsoy, Embry-Riddle Aeronautical University - Prescott; Luis Felipe Zapata-Rivera, Embry-Riddle Aeronautical University

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Computers in Education Division (COED)

component.The required sample size for the study was determined using the standard sampling calculationsdescribed below with a confidence level of 95% and a margin of error of 5%:1) For an infinite population, the sample size S can be calculated by using the equation: S = Z 2 ∗ P ∗ (1 − P )/M 2 , (1)where Z is the z-score based on the desired confidence level, P is the estimated populationproportion (often assumed as 0.5), and M is the desired margin of error. For this survey, thesample size S is calculated as: S = 1.962 ∗ 0.5 ∗ (1 − 0.5)/0.052 = 384.16, (2)2) The value of S should be adjusted for a finite population by using the

Conference Session

Computers in Education Division (COED) Track 4.C

Collection

2025 ASEE Annual Conference & Exposition

Authors

Safia Malallah, Kansas State University; Ejiro U Osiobe; Lior Shamir, Kansas State University; Joshua Levi Weese, Kansas State University; Nathan H. Bean, Kansas State University; Russell Feldhausen, Kansas State University

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Computers in Education Division (COED)

, 2025 Developing a Virtual Worlds Framework for Early Childhood Safia A. Malallah, Kansas State University, safia@ksu.edu Ejiro Osiobe, dr.o@aneosiobe.org Lior Shamir, Kansas State University, lshamir@ksu.edu Allen S. David, Kansas State University, dallen@ksu.edu Joshua L. Weese, Kansas State University, weeser@ksu.edu Bean H Nathan, Kansas State University, nhbean@ksu.edu Feldhausen Russel, Kansas State University, russfeld@ksu.edu Abstract – In the past decade, there has been a significant shift from simply restricting children’s access to technology toward actively monitoring and managing their

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Hannah Oluwatosin Abedoh, Morgan State University; Blessing Isoyiza ADEIKA, Morgan State University; Pelumi Olaitan Abiodun, Morgan State University; Oludare Adegbola Owolabi P.E., Morgan State University; Abiola Olayinka Ajala, Morgan State University; OLUWATOYOSI OYEWANDE, Morgan State University

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Diversity

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Computers in Education Division (COED)

Conference Session

COED Programming Education 1: Students, Motivation, and Mastery

Collection

2023 ASEE Annual Conference & Exposition

Authors

Ryan Parsons, Western Washington University; Qiang Hao, Western Washington University; Lu Ding, University of South Alabama

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Computers in Education Division (COED)

. First things first: Providing metacognitive scaffolding for interpreting problem prompts. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education, SIGCSE ’19, page 531–537, New York, NY, USA, 2019. Association for Computing Machinery. [7] G. Polya. How to solve it: A new aspect of mathematical method, volume 85. Princeton university press, 2004. [8] D. J. Barnes, S. Fincher, and S. Thompson. Introductory problem solving in computer science. In 5th Annual Conference on the Teaching of Computing, pages 36–39, 1997. [9] D. McCall and M. K¨olling. Meaningful categorisation of novice programmer errors. In 2014 IEEE Frontiers in Education Conference (FIE) Proceedings, pages 1–8. IEEE, 2014.[10] D. McCall

Conference Session

The Best of Computers in Education Division (COED)

Collection

2024 ASEE Annual Conference & Exposition

Authors

Gerald Tembrevilla, Mount Saint Vincent University; Mohosina Jabin Toma, University of British Columbia, Vancouver; Marina Milner-Bolotin, University of British Columbia, Vancouver

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Computers in Education Division (COED)

.2018.00092.[2] CLAS. "CLAS: Collaborative Learning Annotation System." UBC Arts ISIT. https://clas.ubc.ca (accessed June 1, 2023).[3] C. Mulryan-Kyne, "Supporting reflection and reflective practice in an initial teacher education programme: an exploratory study," European journal of teacher education, vol. 44, no. 4, pp. 502-519, 2021, doi: 10.1080/02619768.2020.1793946.[4] S. Ledger and J. Fischetti, "Micro-teaching 2.0: Technology as the classroom," Australasian journal of educational technology, vol. 36, no. 1, p. 37, 2020, doi: 10.14742/ajet.4561.[5] H. Crichton, F. Valdera Gil, and C. Hadfield, "Reflections on peer micro-teaching: raising questions about theory informed practice," Reflective

Conference Session

Simulations and Virtual Learning

Collection

2024 ASEE Annual Conference & Exposition

Authors

David J Gagnon, Field Day Lab @ UW-Madison; John M. Pfotenhauer, University of Wisconsin, Madison; Arganthael Berson, University of Wisconsin, Madison; Luke Swanson, University of Wisconsin, Madison

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Computers in Education Division (COED)

system. ● Probability of Transit (p(T)): This parameter measures the probability the student learns the skill after attempting a problem related to that skill. ● Probability of Guess (p(G)): This parameter accounts for the likelihood that the student guesses the answer correctly without actually knowing the skill. It helps distinguish between true knowledge and lucky guesses. ● Probability of Slip (p(S)): The slip parameter is the probability that the student, despite knowing the skill, incorrectly answers a problem. This could be due to mistakes, misunderstandings, or other factors unrelated to their actual knowledge level.Each of these parameters must be initially estimated for each student model variable. ForThermoVR

Conference Session

Computers in Education Division (COED) Track 3.C

Collection

2025 ASEE Annual Conference & Exposition

Authors

Brian Michael Fiegel, The University of Iowa; Geb W. Thomas, The University of Iowa; Matthew Yazvec

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Computers in Education Division (COED)

effectiveness of feedback in SQL-Tutor," in Proceedings International Workshop on Advanced Learning Technologies. IWALT 2000. Advanced Learning Technology: Design and Development Issues, 2000: IEEE, pp. 143-144.[11] A. Mitrovic, S. Ohlsson, and D. K. Barrow, "The effect of positive feedback in a constraint-based intelligent tutoring system," Computers & Education, vol. 60, no. 1, pp. 264-272, 2013.[12] M. Mayo and A. Mitrovic, "Optimising ITS Behaviour with Bayesian Networks and Decision Theory," International Journal of Artificial Intelligence in Education, vol. 12, pp. 124-153, 2001.[13] A. Mitrovic, B. Martin, and M. Mayo, "Using Evaluation to Shape ITS design: Results and Experiences with SQL-Tutor," User

Conference Session

Computers in Education Division (COED) Track 3.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ming Li, The University of Tulsa; Kevin Garcia-Estala, The University of Tulsa; Issai Gutierrez, The University of Tulsa

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Computers in Education Division (COED)

constant data in the MEM stage. The data memory is separated from the instructionmemory, with a pair of read and write ports only accessible to the MEM stage. To supportvariable data widths requested by LB/SB, LH/SH and LW/SW, the data memory is split into fourone-byte wide sub-memory components. A store pre-processing component examines the type ofthe store instruction and determines which one(s) of the sub-memory components should beactive during the store operation. A load post-processing module examines the type of the loadinstruction and determines which byte(s) should be connected to the load data bus. One issue isreported in [6] about the difficulty to merge synthesized memory into their pipeline as IntelQuartus Prime can only create two

Conference Session

Computers in Education Division (COED) Track 6.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kevin Huang, Troy High School; Ivan Zimmerman; Doina Bein, California State University, Fullerton

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Computers in Education Division (COED)

. (2019). Using learning analytics to develop early- warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1), 1–20. 4. Shafiq, D. A., Marjani, M., Habeeb, R. A. A., & Asirvatham, D. (2022). Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review. IEEE Access 5. Seidel, E., & Kutieleh, S. (2017). Using predictive analytics to target and improve first year student attrition. The Australian Journal of Education, 61(2), 200–218 6. Yu, C.-C., & Wu, Y. (Leon). (2021). Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks. Sustainability, 13(22

Conference Session

COED: All about That Math

Collection

2023 ASEE Annual Conference & Exposition

Authors

Yael Gertner, University of Illinois, Urbana-Champaign; Juan Alvarez, University of Illinois, Urbana-Champaign; Benjamin Cosman; Jennifer R. Amos, University of Illinois, Urbana-Champaign

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Computers in Education Division (COED)

thesefindings related to how students actually prepared for exams and studied for the course. Wefound no significant correlation between sense of belonging and final grades. In future workwe plan to explore different ways of getting at sense of belonging questions beyond the oneswe used here.References[1] E.L. Deci and R.M. Ryan. 2012. 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., 416–436.[2] C.S. Dweck. 2006. Mindset: The new psychology of success. New York: Random House.[3] Catherine Good, Aneeta Rattan, and Carol S Dweck. 2012. Why do women opt out? Sense of belonging and women’s representation in mathematics. J. Pers. Soc. Psychol

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2023 ASEE Annual Conference & Exposition

Authors

Valerie A. Carr, San Jose State University; Jennifer Avena, San Jose State University; David Schuster, San Jose State University; Wendy Lee, San Jose State University; Belle Wei, San Jose State University

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Computers in Education Division (COED)

education and technology. Cambridge, MA: Harvard University Press, 2010.[4] A. Bandura, Self-efficacy: The exercise of control. New York, NY: W.H. Freeman and Company, 1997.[5] R. W. Lent, S. Brown, and G. Hackett, “Contextual supports and barriers to career choice: A social cognitive analysis,” Journal of Counseling Psychology, vol. 47, no. 1, pp. 36–49, Jan. 2000.[6] R. W. Lent, S. Brown, and G. Hackett, “Toward a unifying social cognitive theory of career and academic interest, choice, and performance,” Journal of Vocational Behavior, vol. 45, no. 1, pp. 79–122, Aug. 1994.[7] R. W. Lent, F. G. Lopez, H. Sheu, H., and A. M. Lopez, “Social cognitive predictors of the interests and choices of

Conference Session

COED Modulus Topics

Collection

2023 ASEE Annual Conference & Exposition

Authors

Martha Cervantes, Johns Hopkins University ; Raphael Norman-Tenazas; Adam Goertz; Erik C. Johnson, University of Illinois, Urbana-Champaign; William Roberts Gray-Roncal

Tagged Divisions

Computers in Education Division (COED)

, Alexandra Hatfield et al. "Adaptable platform for interactive swarm robotics (apis): a human-swarm interaction research testbed." In 2019 19th International Conference on Advanced Robotics (ICAR), pp. 720-726. IEEE, 2019.12. Farnham, T., Jones, S., Aijaz, A., Jin, Y., Mavromatis, I., Raza, U., ... & Sooriyabandara, M. (2021, January). Umbrella collaborative robotics testbed and iot platform. In 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) (pp. 1-7). IEEE.13. Ospina, Nestor I., Eduardo Mojica-Nava, Luis G. Jaimes, and Juan M. Calderón. "Argrohbots: An affordable and replicable ground homogeneous robot swarm testbed." IFAC-PapersOnLine 54, no. 13 (2021): 256-261.14. Rubenstein, Michael