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Coding is the New Coal: A History of Integrating Computer Science Across Wyoming’s K-12 Curriculum

Conference Session: Computers in Education 1 - Programming 1
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Astrid K. Northrup P.E., Northwest College; Raymond Edward Floyd, Northwest College; S. Renee Dechert, Northwest College; Andrea Carneal Burrows Borowczak, University of Wyoming
Tagged Divisions: Computers in Education

Lecturer at Northwest College in Powell, WY. He has published over 200 papers on a variety of topics. He most recently co-authored a text, Perspectives on Engineering (2011), an IEEE eBook, Shaping an Engineering Career: Book 2: Dual Career Ladders (2013), and another text, So You Want to be an Engineer? (2015).Dr. S. Renee Dechert, Northwest College Renee Dechert is a professor of English at Northwest College in Powell, Wyoming, where she teaches courses in technical writing, business communication, composition, and social media. Her current re- search focus is on the rhetoric of social media, She also blogs about the Colorado Rockies.Dr. Andrea Carneal Burrows, University of Wyoming Dr. Andrea C. Burrows is a

Relevance Score: 0.4412825

IoT to Enable Remote Collaboration in Robotics Class of Mechanical Engineering Technology

Conference Session: Computers in Education 5 - Online and Distributed Learning 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Zhou Zhang, New York City College of Technology; Yizhe Chang, California State Polytechnic University, Pomona; Sven K. Esche, Stevens Institute of Technology; Andy S. Zhang, New York City College of Technology
Tagged Topics: Diversity
Tagged Divisions: Computers in Education

, USA, he received the Best Paper Award for his article ’A Virtual Laboratory on Fluid Mechanics’.Dr. Andy S. Zhang, New York City College of Technology Dr. Andy S. Zhang received his Ph.D. from the City University of New York in 1995. He is currently the program director of a mechatronics project in the New York City College of Technology/CUNY. For the past 15 years, Dr. Zhang has been working on bringing mechatronics technology to the undergraduate en- gineering technology curricula and on helping high school students to learn mechatronics through FIRST Robotic Competition events. American c Society for Engineering Education, 2021

Relevance Score: 0.37824216

Engage AI: Leveraging Video Analytics for Instructor-class Awareness in Virtual Classroom Settings

Conference Session: Computers in Education 6: Best of CoED
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Jeremy Stairs, University of Toronto; Raman Mangla, University of Toronto; Manik Chaudhery, University of Toronto; Janpreet Singh Chandhok, University of Toronto; Hamid S. Timorabadi, University of Toronto
Tagged Divisions: Computers in Education

at the University Of Toronto with a focus on Artificial Intelligence and Business. Manik will be graduating in May 2021.Mr. Janpreet Singh Chandhok, University of Toronto Janpreet Singh Chandhok is an undergraduate student in computer engineering and artificial intelligence at the University of Toronto (graduating May 2021)Dr. Hamid S. Timorabadi, University of Toronto Hamid Timorabadi received his B.Sc, M.A.Sc, and Ph.D. degrees in Electrical Engineering from the University of Toronto. He has worked as a project, design, and test engineer as well as a consultant to industry. His research interests include the application of digital signal processing in power systems. American

Relevance Score: 0.37824216

Artificial Intelligence Paradigms and the Future of Learning: What a Partial Review of Half a Century of AI Conceptualization Suggests

Conference Session: Computers in Education 7 - Modulus 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Joseph Maloba Makokha, Stanford University
Tagged Divisions: Computers in Education

shiftsbetween the 1970’s and 2010’s.using paradigms to understand AI’s evolutionPractitioners in diverse fields define the term “paradigm” in different ways depending on theirdomains, with slight variations corresponding to norms in their respective fields. We takeKuhn’s[12] view which holds that a paradigm provides an open-ended resource that presents aframework of concepts, results and procedures within which subsequent work is structured. Acharacteristic of paradigms is that they can “shift” with new knowledge or evidence. An exampleusing human flight experience can be represented as shown in Table 2 below. The inspirationmay have originated from nature, through birds’ ability to swiftly move in air. Legends andmythology from early Greek times

Relevance Score: 0.020344185

Work in Progress: STEM Students’ Experiences with Educational Technology Tools

Conference Session: Computers in Education 7 - Modulus 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Ahmed Ashraf Butt, Purdue University at West Lafayette; Saira Anwar, University of Florida; Muhsin Menekse, Purdue University at West Lafayette
Tagged Topics: Diversity
Tagged Divisions: Computers in Education

asked to reflect on their experiences using the followingquestion:How often have you been in courses where some educational technology tools, especiallymobile applications, have been used? Tell us something about your experience. a. Please state the name of the application(s) or other technology tools (e.g., Clicker, CATME, Socrative, Any quiz software, etc.). b. What you liked about that application(s) and why? c. What you didn’t like and why? d. Were those applications academically relevant? If yes, why, if no, why not?Data AnalysisThe study focuses on exploring the students’ perceptions of using educational technology toolsin postsecondary STEM classrooms. To inform our study, we employed

Relevance Score: 0.019757524

Figurative Language in Computer Education: Evidence from YouTube Instructional Videos

Conference Session: Computers in Education 4 - Online and Distributed Learning 1
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Sherif Abdelhamid, Virginia Military Institute; Andrew Katz, Virginia Polytechnic Institute and State University
Tagged Divisions: Computers in Education

Metaphor F-M Perceptual metaphor F-M-P Figurative Language F Lexicalized metaphor F-M-L Personification F-P Simile F-S Synecdoche F-Y Metonymy F-M Analogy F-A Question S-Q Illustrative S-E Teaching Style S Example Imagination S-I Repetition

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A Study on the Effectiveness of the CLICK Approach in an Operations Research Course

Conference Session: Computers in Education 10 - Technology 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Christian E. Lopez, Lafayette College; Omar Ashour, Pennsylvania State University; James Devin Cunningham, Carnegie Mellon University; Conrad Tucker, Carnegie Mellon University
Tagged Divisions: Computers in Education

suspension of disbelief on the part of the student. In Deshpande etal.’s [23] review of simulation games in engineering education, they found many advantages ofteaching engineering concepts through simulated environments over traditional classroominstruction, including but not limited to connecting theory to practice, customizability ofdifficulty to match students comprehension level, reduction of resistance to accepting innovativeideas and concepts, and greater retention of concepts over time. Another advantage ofsimulation-based learning is its compatibility with online learning, which continues to be agrowing trend, especially since the COVID-19 pandemic [11], [18], [31], [32].Using virtual systems to augment education is not only limited to

Relevance Score: 0.017711068

A Pilot Study Investigating STEM Learners’ Ability to Decipher AI-generated Video

Conference Session: Computers in Education 8 - Video Technology
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Dule Shu, Carnegie Mellon University; Christopher Doss, RAND Corporation; Jared Mondschein, RAND Corporation; Denise Kopecky, Challenger Center; Valerie A. Fitton-Kane, Challenger Center; Lance Bush, Challenger Center; Conrad Tucker, Carnegie Mellon University
Tagged Divisions: Computers in Education

illustrations still improve students’ learning from text,” Educational psychology review, vol. 14, no. 1, pp. 5–26, 2002.[11] W. R. Tan, C. S. Chan, H. E. Aguirre, and K. Tanaka, “ArtGAN: Artwork synthesis with conditional categorical GANs,” in 2017 IEEE International Conference on Image Processing (ICIP), 2017, pp. 3760–3764.[12] D. Shu et al., “3D Design Using Generative Adversarial Networks and Physics-based Validation,” 2019.[13] C. E. Lopez, J. Cunningham, O. Ashour, and C. S. Tucker, “Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments,” Journal of Computing and Information Science in Engineering, pp. 1–33, 2020.[14] A. Jordan, “On discriminative vs. generative classifiers: A comparison

Relevance Score: 0.016802194

Game Research Trends at the Annual ASEE Conference: A 15-year Content Analysis

Conference Session: Computers in Education 3 - Modulus I
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Larysa Nadolny, Iowa State University of Science and Technology; Md Imtiajul Alam, Iowa State University of Science and Technology; Michael Geoffrey Brown, Iowa State University of Science and Technology; Monica H. Lamm, Iowa State University of Science and Technology
Tagged Divisions: Computers in Education

. In contrast to the concept map view, the topics provides a rich environment toqualitatively examine related data.Data CollectionGaming research papers in engineering education were infrequent in the early 2000’s 10 9 , andprior to 2006, there were few abstracts published in the ASEE annual conference proceedings.Therefore, the data search included all ASEE annual conference papers from 2006-2020 using theASEE Conference Proceedings Search.The search terms included game, gaming, gamer, gamify,and gamification in the title of the paper. Relevant paper abstracts and metadata were included inthe sample. We reviewed each abstract and excluded papers on topics not related to games andlearning, such as game theory or sports, and papers without

Relevance Score: 0.016328797

Combining Immersive Technologies and Problem-based Learning in Engineering Education: Bibliometric Analysis and Literature Review

Conference Session: Computers in Education 9 - Technology 1
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Mahgol Nowparvar, Pennsylvania State University ; Xing Chen, Pennsylvania State University ; Omar Ashour, Pennsylvania State University ; Sabahattin Gokhan Ozden, Pennsylvania State University Abington; Ashkan Negahban, Pennsylvania State University
Tagged Divisions: Computers in Education

Science Foundation under Grant No.2000599. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the authors and do not necessarily reflect the views of the National ScienceFoundation. The preliminary stages of this work are supported by funds from the Office of theExecutive Vice President and Provost at The Pennsylvania State University as part theuniversity’s strategic plan related to transforming education.References[1] D. R. Brodeur, P. W. Young, and K. B. Blair, “Problem-based learning in aerospace engineering education,” ASEE Annu. Conf. Proc.,2002, doi: 10.18260/1-2-10974.[2] J. T. Bell and H. S. Fogler, “Implementing virtual reality laboratory accidents using the half-life game

Relevance Score: 0.016328797

Factors Associated with Collaboration Networks in ASEE Conference Papers

Conference Session: Computers in Education 6: Best of CoED
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Sherif Abdelhamid, Virginia Military Institute; Yousef Jalali, Virginia Polytechnic Institute and State University; Andrew Katz, Virginia Polytechnic Institute and State University
Tagged Divisions: Computers in Education

is evident in the results presented in this paper. Ourgoal here is not to make a conclusive argument about the connection between research topics andcollaborations across universities but rather highlight that the changes in major research areas,for example, in response to funding opportunities, may play a role in connecting researchers withdifferent degrees of expertise across institutions; future research may examine suchmulti-variable relationships.References 1. Bozeman, B., Fay, D., and Slade, C.P. (2013). Research collaboration in universities and academic entrepreneurship: the-state-of-the-art, Journal of Technology Transfer, 38, 1, 1-67. 2. Jones, B.F., Wuchty, S., Uzzi, B. (2008). Multi-university research teams

Relevance Score: 0.013861103

Coding Trails: Concise Representations of Student Behavior on Programming Tasks

Conference Session: Computers in Education 1 - Programming 1
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Frank Vahid, zyBooks; University of California, Riverside; Roman Lysecky, University of Arizona; zyBooks; Bailey Alan Miller, University of California, Riverside; Lyssa Vanderbeek, zyBooks
Tagged Divisions: Computers in Education

like scores and statistical data, occupying another column of agradebook. And, a textual representation can be parsed by scripts for additional analysis. The main tradeoff is losing the graphical benefit of showing time simply as a bar with width representing time spent.3.1 Basic develop and submit runsAs an initial attempt, we tried a compact version of the log file for a given student: dev dev dev dev sub(0) dev dev dev sub(2) sub(5) dev dev sub(10)While enabling a quicker view than a detailed log, we realized the short words weren’t needed. Instead,we could use single letters, and eliminate spaces. We used d for each develop run, and s for each submitrun followed by the score on that run. The student below did 4 develop runs, then

Relevance Score: 0.013068372

Computational Thinking: A Pedagogical Approach Developed to Prepare Students for the Era of Artificial Intelligence

Conference Session: Computers in Education 7 - Modulus 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Gulustan Dogan, University of North Carolina Wilmington; Yang Song, University of North Carolina Wilmington; Damla Surek, Yildiz Technical University
Tagged Topics: Diversity
Tagged Divisions: Computers in Education

believethat our modules had a greater impact on those students who were newer to computationalthinking, over those who had prior experience and were enrolled in upper-level computationalcourses.1 IntroductionAccording to Wing, Computational Thinking (CT) is the thought processes involved informulating a problem and expressing its solution(s) in such a way that an information processor– human or machine – can effectively carry out that solution [1]. The educational philosophybehind Computational Thinking is that problems in every discipline can be solved by the tools ofcomputation such as algorithmic thinking, decomposition, abstraction, pattern recognition. Forinstance, one of the pillars of computational thinking is algorithmic thinking

Relevance Score: 0.0129343355

Work in Progress: Remote Instruction of Circuitry in a Multidisciplinary Introduction to Engineering First-year Course

Conference Session: Computers in Education 5 - Online and Distributed Learning 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: James E. Lewis, University of Louisville; Nicholas Hawkins, University of Louisville; Brian Scott Robinson, University of Louisville
Tagged Divisions: Computers in Education

first-year students programmingcurriculum. The Arduino Uno was the chosen microcontroller since the platform is excellent forteaching basic circuitry and programming, such as having easily accessible digital and analoginput/output ports.ENGR 111 uses a scaffolded set of lessons to introduce circuitry, programming an Arduino, andinterfacing between an Arduino and circuits. These lessons start with basic circuits usingbreadboards, basic components, and wires. Programming the Arduino is the next set of lessons,and these lessons focus on basic programming concepts and how to interact with the Arduino.Finally, there is a series of lessons that help the students create circuit(s) and program(s) thatinteract with each other.Although the ENGR 111

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Work in Progress: Homework in the Digital Age: The Implementation, Effects, and Perception of Randomly Generated Custom Digital Assignments

Conference Session: Computers in Education 9 - Technology 1
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: David Beevers, Pennsylvania State University; Qi Dunsworth, Pennsylvania State University
Tagged Divisions: Computers in Education

1 1 Pipe Diameter Flow Rate 5 2-3 2–4 Pipe Diameter C. Problem generation.With these parameters identified, the problem generation algorithms can proceed. The problemgeneration process begins by selecting the fluid, entrance type, and pipe material. The entrancelocation is considered to be the reference location for the system elevation, and the materiallimits the pipe sizes that can reasonably be considered.Next, the pipe diameter(s) are randomly selected. The diameters are generated such that

Relevance Score: 0.01056084

Work-in-Progress: Incorporating Computational Thinking Instruction into K-12 Using 3D Weather

Conference Session: Computers in Education 1 - Programming 1
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Pat Ko, Mississippi State University; Mahnas Jean Mohammadi-Aragh, Mississippi State University; Jonathan G. Harris, Northern Gulf Institute; Jamie Lee Dyer, Mississippi State University; Yan Sun, Mississippi State University
Tagged Divisions: Computers in Education

in the meteorology community. Initially released in 2002, it isdeveloped by the Unidata Program Center (UPC)(Unidata | IDV FAQs, n.d.), which is a group ofinstitutions that develops and shares tools and data with the Earth Science research and educationcommunity. Unidata is primarily funded by the National Science Foundation and is part of the UniversityCorporation for Atmospheric Research (UCAR) (Unidata Tour, 2021). Figure 2 is a typical IDVvisualization included with the curriculum. Figure 2. An IDV visualization showing a constant pressure (a.k.a., isobaric) surface colored by windspeed along with surfaces of constant wind (a.k.a., isotach) at a value of 50 m/s. Note the orientationdirected from southeast to northwest across the Earth

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Solar-powered, Digital Classroom-in-Box: A Digital System to Mitigate the Digital Divide of Post-pandemic Education

Conference Session: Computers in Education 5 - Online and Distributed Learning 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Shamsul Arefeen, Texas Tech University; Tim Dallas P.E., Texas Tech University; Heather Greenhalgh-Spencer, Texas Tech University
Tagged Topics: Diversity
Tagged Divisions: Computers in Education

systems.acknowledgementThis work has been funded by the Global Laboratory for Energy Asset Management andManufacturing (GLEAMM) and Texas Instruments.references[1] A. Ramsetty and C. Adams, "Impact of the digital divide in the age of COVID-19," Journal of the American Medical Informatics Association, vol. 27, no. 7, pp. 1147-1148, 2020.[2] H. Greenhalgh-Spencer and M. Jerbi, "Technography and design–actuality gap-analysis of internet computer technologies-assisted education: Western expectations and global education," Policy Futures in Education, vol. 15, no. 3, pp. 275-294, 2017.[3] A. Banerjee, P. Glewwe, S. Powers, and M. Wasserman, Expanding access and increasing student learning in post-primary education in

Relevance Score: 0.010331456

Investigation of Technology-based Student Interaction for Social Learning in Online Courses

Conference Session: Computers in Education 5 - Online and Distributed Learning 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Sunay Palsole, Texas A&M University; Jaskirat Singh Batra, Texas A&M University; Xi Zhao, Texas A&M University
Tagged Divisions: Computers in Education

discussionboards at the same time.From Figure 4, there is an indication that students were somewhat satisfied with thetechnologies/platforms and methods used by instructors/TAs during the online session. Filteringthe data by type of methods and satisfaction indicates that students showed the greatestsatisfaction when live lecturing methods were supplemented by students being able to askquestions from the instructor during the session, at the end of the session, during office hours, orby email after the session. Method(s) used for interaction during the online session N/A 5 Participant Response

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VolumeVisual: Design and Evaluation of an Educational Software Tool for Teaching and Learning Volume Visualization

Conference Session: Computers in Education 2 - Programming 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Xueyi Bao, Notre Dame University; Jun Han, University of Notre Dame; Chaoli Wang, University of Notre Dame
Tagged Divisions: Computers in Education

that need to be cast through, the texture coordinate, and the depth of the entry point. Theray direction is given by the vector from the entry and exit points in the texture space. Each sample’s position alongthe ray direction is computed via linear interpolation. In terms of how many samples we should take, we set the stepsize as half of a voxel. Users can adjust the Sampling Rate parameter to change the number of samples taken alongeach ray. Note that the assigned opacity also depends on the sampling rate. For example, when using fewer slices, theopacity has to be scaled up, so that the overall intensity of the rendering results remains the same. We use Equation 1to correct the opacity whenever users change the sampling rate s from the

Relevance Score: 0.009900788

Assessing the Influence of an Online Video Tutorial Library on Undergraduate Mechanical Engineering Students

Conference Session: Computers in Education 4 - Online and Distributed Learning 1
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Juliana Lynn Fuqua, California State Polytechnic University, Pomona; Faye Linda Wachs, California State Polytechnic University, Pomona; Paul Morrow Nissenson, California State Polytechnic University, Pomona; Deanna Miranda Barrios; Cecilia Nguyen, California State Polytechnic University, Pomona
Tagged Topics: Diversity
Tagged Divisions: Computers in Education

Accessibility Initiative, “Making Audio and Video Media Accessible,” Accessed November 2020.Available at [11] Bureau of Internet Accessibility, “Checklist for Creating Accessible Videos,” Accessed November 2020.Available at [12] G. Morin, J. Rubin, and R. Leisinger, “508 Accessible Videos – Why (and How) to Make Them,” Available at[13] Directory of Coursera University Partners. Accessed November 2020. Available at[14] Directory of edX University Partners. Accessed November 2020. Available at[15] Cal Poly Pomona Mechanical Engineering Department YouTube account. Available at[16] Cal Poly Pomona Mechanical Engineering Department video content website, ME Online. Available at[17] S. Tosun, The Effects of Blended Learning on EFL Students’ Vocabulary

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Measuring Awareness of Computational Thinking in Kuwaiti Educational Institutions

Conference Session: Computers in Education 2 - Programming 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Safia Malallah, Kansas State University; Khaled Nasser Alsalmi, The Public Authority for Applied Education and Training; Joshua Levi Weese, Kansas State University
Tagged Topics: Diversity
Tagged Divisions: Computers in Education

[1] D. Barr, J. Harrison, and L. Conery, "Computational thinking: A digital age skill for everyone," Learning & Leading with Technology, vol. 38, no. 6, pp. 20-23, 2011.[2] C. ISTE. "Computational Thinking Leadership Toolkit - ISTE." https://cdn.iste.org/www-root/ct-documents/ct-leadershipt-toolkit.pdf?sfvrsn=4 (accessed 2021).[3] I. Corradini, M. Lodi, and E. Nardelli, "Computational Thinking in Italian Schools: Quantitative Data and Teachers' Sentiment Analysis after Two Years of" Programma il Futuro"," in Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education, 2017, pp. 224-229.[4] S. F. Sidek, C. S. Said, and M. H. M. Yatim, "Characterizing

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A Comparative Analysis of Student Performance and Face-to-Face Engineering Courses

Conference Session: Computers in Education 4 - Online and Distributed Learning 1
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Sunay Palsole, Texas A&M University; Jeff Chernosky, Texas A&M University; Randy McDonald, Texas A&M University Engineering
Tagged Topics: Diversity
Tagged Divisions: Computers in Education

] M. Simmons, G. Parchoma, M. Jacobsen, D. Nelson, and S. Bhola, “Designing for studentengagement in an online doctoral research methods course. Proceedings of the IDEAS:Designing for Innovation. pp. 81–91, 2016.[3] P. Nuangchalerm, T. Polyiem, and P. Wongchantra, “Learning achievement, science processskills, and moral reasoning of ninth grade students learned by 7E learning cycle andsocioscientific issue-based learning. Australian Journal of Basic and Applied Sciences, vol. 5,no. 10, pp. 257-564, 2011.[4] S.J. Baldwin and J.H. Trespalacios, “Evaluation instruments and good practices in onlineeducation,” Online Learning, vol. 21, no. 2. pp. 1-18. doi:10.24059/olj.v21i2.913, 2017.[5] P. K. Gibson and T. Kinsey, “Need we train online

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Common Logic Errors for Programming Learners: A Three-decade Literature Survey

Conference Session: Computers in Education 2 - Programming 2
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Nabeel Alzahrani, University of California, Riverside; Frank Vahid, University of California, Riverside
Tagged Divisions: Computers in Education

errors.Generic errors Specific errors• Conceptual errors • Action definition • Incorrect transfer of • Mixed up of constructs[Hall12 (58%)] wrong [Winikoff14] knowledge [Pillay06] (if and while)• Misunderstanding / • Action(s) of rule • Inefficient problem [Grandell05]misinterpretation wrong (but legal) solving approach • Natural-language[Spohrer86, Robins10] [Winikoff14] [Pillay06] problem [Robins10]• Problem solving • Additional (wrong) • Interpretation problem • Not supported[Bryce10, Pillay06] rule [Winikoff14] [Robins10] [Spohrer86] • Cognitive load • Lack of • Not

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Technology Decisions of Engineering Students for Solving Calculus Questions

Conference Session: Computers in Education 9 - Technology 1
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Emre Tokgoz, Quinnipiac University; Samantha Eddi Scarpinella, Quinnipiac University; Michael Giannone, Quinnipiac University
Tagged Divisions: Computers in Education

participants’ responses to solve series/error termsThe remaining part of this section is dedicated to analyzing the responses of the research participants.Participant 10 below chooses to solve the question using excel. This is an chosen by only 4% of thestudents. This student chooses to use excel because it was easy to use. Figure 12. Participant 10 response to solving the numerical value calculations.Participant 13 chooses to use MATLAB as the primary language and declares Mathematica as thesecond choice. The student has experience with MATLAB and can perform tasks quickly. MATLAB ischosen by 13% of the research participating students. Figure 13. Participant 13’s response based on determining solution quickly for the questionBelow

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The Rise of Program Auto-grading in Introductory CS Courses: A Case Study of zyLabs

Conference Session: Computers in Education 9 - Technology 1
Collection: 2021 ASEE Virtual Annual Conference Content Access
Authors: Chelsea L. Gordon, zyBooks, A Wiley Brand; Roman Lysecky, University of Arizona; zyBooks, A Wiley Brand; Frank Vahid, University of California, Riverside
Tagged Divisions: Computers in Education

encourage early starts or to decrease cheating, newexperiences using auto-graders' built-in similarity checkers to reduce cheating [12],and much more.References[1] M. Sherman, S. Bassil, D. Lipman, N. Tuck, and F. Martin, “Impact of auto- grading on an introductory computing course,” Journal of Computing Sciences in Colleges, vol. 28, no. 6, pp. 69-75, Jun 2013.[2] R. Pettit, J. Homer, R. Gee, S. Mengel, and A. Starbuck. “An Empirical Study of Iterative Improvement in Programming Assignments.” in Proceedings of the 46th ACM Technical Symposium on Computer Science Education, SIGCSE, pp. 410-415, Feb 24 2015.[3] G. Haldeman, A. Tjang, M. Babeş-Vroman, S. Bartos, J. Shah, D. Yucht, and T.D. Nguyen, “Providing meaningful feedback for