Conference Session

Computers in Education 8 - Video Technology

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Walter W. Schilling Jr., Milwaukee School of Engineering

Tagged Divisions

Computers in Education

Paper ID #33303Assessing the Effectiveness of Individual Reflections on Video FeedbackDr. Walter W. Schilling Jr., Milwaukee School of Engineering Walter Schilling is a Professor in the Software Engineering program at the Milwaukee School of Engi- neering in Milwaukee, Wisconsin. He received his B.S.E.E. from Ohio Northern University and M.S. and Ph.D. from the University of Toledo. He worked for Ford Motor Company and Visteon as an Embedded Software Engineer for several years prior to returning for doctoral work. He has spent time at NASA Glenn Research Center in Cleveland, Ohio, and consulted for multiple embedded

Conference Session

Computers in Education 10 - Technology 2

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Irini Spyridakis, University of Washington

Tagged Divisions

Computers in Education

Report—in depth and discuss its success. The assignment takesplace in a three-week UI course module in a required junior level, communication andengineering design course in the Department of Human Centered Design & Engineering in theCollege of Engineering at the University of Washington. Outcomes were qualitatively assessedby examining samples of students’ reports versus requirements and students’ reflections on themodule and report assignment. Outcomes have been positive and reveal that students gain anunderstanding of effective UI design and the important role of UI designers, and the impact of UIdesign on society. Educators from a multitude of disciplines that intersect with human computerinteraction can use this assignment in helping

Conference Session

Computers in Education 1 - Programming 1

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Ben Tribelhorn, University of Portland; Heather Dillon, University of Washington Tacoma; Andrew M. Nuxoll, University of Portland; Nicole C. Ralston, University of Portland

Tagged Divisions

Computers in Education

. Exam scores were improved when measuring studentsability to create use cases, especially clarity and completeness. Student performance was greatlyimproved when writing use cases, especially clarity and completeness which was reflected inimproved projects. Quantitatively, the same mindset objectives were assessed in other coursemodules as part a larger curriculum wide effort in Engineering. The numerical results indicatethat the modules in this course outperformed other modules in the curriculum for most of themindset objectives. Ultimately, the results indicate these types of modules may play an importantrole in entrepreneurial mindset development for computer science students.IntroductionThis paper describes a set of modules designed to

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

educational technology tools in STEM classrooms in the pastfew decades. Previous studies have discussed the impact of design, development, and use ofeducational technology tools on creating an interactive learning environment for students.However, in the realm of user experience, limited studies explored the context of technology andstudents’ experiences while interacting with educational technology tools, such as students’perceived ease of use. Accordingly, this work in progress study explores reflections of students’experience while interacting with the most commonly used education technology tools inpostsecondary classrooms. For this study, we recruited thirty undergraduate STEM students fromtwo midwestern educational institutes. Our primary

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

volume of researchon games and learning in the past 15 years has grown along with related theoretical frameworks,methods, and areas of study 6 7 8 . In engineering education, there are a variety of game-basedapproaches for teaching and learning with generally positive results 9 , although there is a need formore transparency in design and more rigorous methodological techniques 10 .This growth in gaming research is also reflected at the American Society for EngineeringEducation (ASEE) annual conference proceedings, expanding from 12 papers during the2001-2005 conferences to 73 papers during 2016-2020, a six fold increase over 20 years. Byexamining the evolution of gaming trends over time, the results can be used to inform the ASEEcommunity of

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

. PurdueUniversity has the highest number of connections (degree = 442), reflecting the high number ofco-authorship collaborations of Purdue-affiliated authors with other institutions. FollowingPurdue University, Virginia Tech had a degree equal to 255. The nodes with the highest degree inour collaboration network are mostly the institutions that are classified as research-orientedinstitutions according to the Carnegie classification. These nodes are often referred to as hubs,and calculating degree is the quickest way of identifying hubs. Betweenness centrality is another network measure useful to capture broker nodes thatstand between groups and give the network connectivity and cohesion [18]. Purdue Universityalso had the highest betweenness

Conference Session

Computers in Education 5 - Online and Distributed Learning 2

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Tiffanie R. Smith, Lincoln University; Susan Ellen Safford, Lincoln University; Chidera Iguwe; Mofoluwasho Akinlade

Tagged Topics

Diversity

Tagged Divisions

Computers in Education

through the app andMain Menu were easy, and the same percentage were positive about the fit of the image on theirdevices and the app logo. All of the students felt that the process to create an account did not runsmoothly and the frustration with the initial encounter of the app was reflected in individualstudent comments. Almost 50% of respondents had at least one experience with the app crashing.Over 70% of the respondents described specific issues they encountered using the app and/ormade suggestions on ways to improve it. Some specific issues included some of the questions notloading and occasional navigational redirection.Learning Modes and ContentWith regards to the learning modes, 48% of the students thought that the Tutorial feature

Conference Session

Computers in Education 7 - Modulus 2

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Rahman Adekunle; John Kofi Eshirow Jr., University of Virginia; Jacob Lam Herring, University of Virginia; Sin Lin, University of Virginia; Rider W. Foley, University of Virginia

Tagged Topics

Diversity

Tagged Divisions

Computers in Education

and rural-urban differentiation. The aim is to critically reflect upon the extent to which the CS4ALL:RPPis reaching children that lack educational opportunities within the field of computer scienceeducation. In the following section, prior work published within the Computers and Educationdirectorate, as well as other pertinent scholarship, is briefly summarized and connections to thisresearch are made clear. The methods of data collection, organization, and analysis are detailedin the next section. The results offer an initial cataloging and review of the projects and programsfunded by the Research-Practitioner Partnerships, which is funded by the NSF as part of theCS4ALL program. The discussion focuses on the opportunities for

Conference Session

Computers in Education 10 - Technology 2

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Tristan M. Ericson, York College of Pennsylvania

Tagged Divisions

Computers in Education

frequencies) and eigenvectors (mode shapes), so this task is performed easily in the activity. In prior lecture sections I show how the magnification factor curve is – strangely and conveniently – the FFT of the impulse response. We apply this principle in the MDOF module by creating a Simulink model for the 2DOF system and determining the time domain impulse response from a free vibration simulation with an initial velocity at one degree of freedom. The FFT of the results gives the two magnification factors, and the mode shape results are clearly reflected in the frequency domain. The calculated mode shapes are 𝒖𝟏 = [2 1] and 𝒖𝟐 = [1 −1]. Figure 8 shows the simulation results in the time and frequency domains. The relative

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

discussion wasutilized, leading to a 100% agreement at the end. Researchers shared the same intention oflooking for figurative language and other instructors’ stylistics. First, researchers read a randomsample of 10 video transcripts and developed initial categories that were used to code the rest ofthe videos. The first round of codebook analysis revealed three initial categories: figurativelanguage, technical figurative language, and teaching style. In the second round, researcherswent through all the excerpts coded as figurative language and developed further categories ofcodes reflecting the figurative language type. The codes created in the first and second roundsare shown in table 1. The following metaphors and figurative language were

Conference Session

Computers in Education 3 - Modulus I

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Arinjoy Basak, Virginia Polytechnic Institute and State University; Todd Patrick Shuba, Virginia Polytechnic Institute and State University; Jianqiang Zhang, Virginia Polytechnic Institute and State University; Sneha Patel Davison, Virginia Polytechnic Institute and State University; David A. Dillard, Virginia Polytechnic Institute and State University; Jacob R. Grohs, Virginia Polytechnic Institute and State University; Nicole P. Pitterson, Virginia Polytechnic Institute and State University; Clifford A. Shaffer, Virginia Polytechnic Institute and State University

Tagged Divisions

Computers in Education

construction, as this distinguishes ex-perts and novices. According to cognitive load theory (CLT), for learning to occur, workingmemory needs to accommodate the additive needs of intrinsic, extraneous, and germane cogni-tive loads [9]. From this perspective, interactive exercises empower the user to optimize theirown learning through the ability to decrease intrinsic cognitive load of the problem, allowingidentification of what they know and what they don’t, as well as provide opportunity for meta-cognitive reflection – all of which has been shown to increase development of more complexknowledge [10]. When done properly, educational technologies and e-learning environments cangreatly optimize the elements of CLT for effective learning [11

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

" ) OR LIMIT-TO ( EXACTSRCTITLE , "ASEE Annual Conference Proceedings" ) )Next, we perform co-occurrence analysis [5]–[7] to classify and map co-occurred words andphrases among the collected papers related to PBL and VR to describe research trends. Figure 3presents an illustrative example of co-occurrence analysis with three hypothetical documents(Doc 1-3) and the resulting map/network of keywords/phrases (denoted by A, B, C, E, R, W, X). (a) The three documents and their keywords used in the example of co-occurrence analysis. The size of nodes and length of links reflect the number of co-occurrences

Conference Session

Computers in Education 7 - Modulus 2

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Anu Aggarwal, University of Illinois Urbana Champaign

Tagged Divisions

Computers in Education

website,black board and lab access over vpn were used for course delivery. This caused some confusionamong students and instructors. Therefore, in Fall 2020 semester (still affected by covid), it wasdecided to deliver the course over a single platform, viz, blackboard. As such, zoom links forlectures, office hours and exams were posted on blackboard, all lecture notes, recordings, HWs,labs, project and exams were delivered through blackboard. Labs were moved to a platform thatwas free (PSPICE) to students and could be installed on their laptops. So, problems encountereddue to remote log in were not there. This was reflected in better student performance and betterinstructor evaluations in the Fall 2020 semester than in the Spring 2020 semester

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

same technology to solve these two questions.  26% of the participants correlated to solve Q1 and Q2 by using the same technology, calculator.  33% of the participants correlated to solve Q2 and Q3 by using a calculator.  35% of the research participants selected different technologies for all three questions.Figure 16 below reflects a summary of the correlation analysis. Correlation Analysis of the Three Research Questions  Different Tech  35%  Q2&3  33%  Q1&3  52%  Q 1&2

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

) literature, the richness of a medium is defined as its capacity to change understanding,and it is commonly asserted that media with more information are richer [3]. For instance, Zoom andGoogle Meet are richer than text messages [3]. CMC researchers have shown richer media to facilitatemore fluent conversation, interpersonal awareness, interpersonal bonding, oxytocin release, andperception of understanding [4][5][6][7]. Video is used to quickly communicate nonverbal cues forturn-taking, understanding, and attention [5][8][9].State of the art videoconferencing reflects the findings of the literature on richness; Zoom and BbCollaborate feature simultaneous videoconferencing, emoji reactions, text chat, screen sharing, andbreakout rooms. However, they

Conference Session

Computers in Education 6: Best of CoED

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Luwen Huang, Massachusetts Institute of Technology; Kayla M. Bicol; Karen E. Willcox, University of Texas at Austin

Tagged Divisions

Computers in Education

.NBT.1 Recognize that in a multi-digit whole num- 4 ber, a digit in one place represents ten times what it represents in the place to its right. Highest out-degree 1.0A.6 Add and subtract within 20; 2.0A.2 Fluently 1; 2; 3; 3; add and subtract within 20; 3.OA.7 Fluently multi- High School ply & divide within 100; 3.OA.9 Identify arithmetic patterns; G-CO.4 Develop definitions of rotations, reflections, and translations Highest incoming rank 9 (17 vertices) Highest outgoing rank 9 (6 vertices)nine; Figure 4 visualizes this path. Note that in our visualization, arrows point

Conference Session

Computers in Education 9 - Technology 1

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Efthymia Kazakou, zyBooks, A Wiley Brand; Alex Daniel Edgcomb, zyBooks, A Wiley Brand; Yamuna Rajasekhar, zyBooks, A Wiley Brand; Roman Lysecky, University of Arizona; zyBooks, A Wiley Brand; Frank Vahid, University of California, Riverside

Tagged Divisions

Computers in Education

concepts in an incremental manner to helpstudents progress, while still enabling a less-prepared student many opportunities to practice. Anexplanation provides the student feedback, guides the student through the level, and adapts to thegiven question and answer provided.As more classes become virtual and instructors need to cope with larger groups of learners moreefficiently, auto-grading and self-assessment as a result, have become very important.Self-assessment promotes students’ skills of reflective practice and self-monitoring, andincreases students’ motivation and confidence. This homework activity style encouragesself-assessment especially with the use of activities where students receive immediate feedbackon each question. Even when a

Conference Session

Computers in Education 3 - Modulus I

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Roxanne Moore, Georgia Institute of Technology; Sunni Haag Newton, Georgia Institute of Technology; Meltem Alemdar, Georgia Institute of Technology; Sabrina Grossman, Georgia Institute of Technology; Jason Freeman, Georgia Institute of Technology; Jason Brent Smith, Georgia Institute of Technology; Tom Berry, Amazon Future Engineer

Tagged Topics

Diversity

Tagged Divisions

Computers in Education

worked on the project only at home.Less than 1/3 of students had made music using a computer prior to the competition, and fewer(16.4%) had used the EarSketch platform prior to the competition. In terms of their coursework,nearly all students (94.5%) reported being currently enrolled in a computer science or technologyrelated course, and a large portion of students (89.0%) reported that they had previously taken acomputer science or technology related course.Students’ Feedback on the Competition: Students were asked to reflect on various aspects of thecompetition. On eight of these ten items, average student responses fell between the “Agree” and“Strongly Agree” scale points, indicating generally positive feedback about most aspects of

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

wasa feeling of being overwhelmed with “no chance of getting a good grade.” On the positive side,these students remarked about an improved way to learn and the utilization of many outsideresources as necessities in this modality. Online Course (OLC): The comments replicated most often reflected a feeling ofdetachment and isolation, as well as a feeling of being on their own and learning by themselves.Similar to responses in the face-to face modality, these learners also provided a majority ofnegative comments stating that the courses were more rigorous and required additional time forstudying. Additionally, respondents believed they “learned a lot less” and faculty were viewed asrestrictive with limited access and delayed feedback. The

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

of the real-life system. Students will alsobuild alternatives to the current system to improve the system's key performance measures. Inaddition, more data will be collected in the future course. This data will help in analyzing theeffectiveness of the CLICK approach across several courses in the IE curriculum. The usabilityof the learning modules will also be revised based on the students’ feedback.AcknowledgmentThe authors would like to thank the National Science Foundation for funding this work underGrant # 1834465. Any opinions, findings, or conclusions found in this paper are those of theauthors and do not necessarily reflect the views of the sponsors. The authors would also like tothank Xing Chen for his help in animating the 3D

Conference Session

Computers in Education 6: Best of CoED

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Molly Rebecca Domino, Virginia Polytechnic Institute and State University; Margaret O'Neil Ellis; Dennis Kafura

Tagged Topics

Diversity

Tagged Divisions

Computers in Education

visualization embedded in the textbook. These visualizationswere integrated in the e-textbook and offered students the chance to see aspects of iterationdemonstrated immediately after the relevent paragraph.The design of the visualization reflected the appearance of the block-based language the studentswere using on their first encounter with iteration. The horizontal green segmented rectangle is thelist which moves from right to left on each iteration so that a single list item becomes the value ofthe iteration variable (”price” in this example). Figure 1: Example of a Textbook VisualizationTo interact with these visualizations, students clicked on the four arrow icons seen at the top ofthe figure. Clicking the ‘¿’ button

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

last decades of the past half century suggest that while manyfactors are contributing to the actualization of “thinking machines”, paradigms about AI are acritical in translating AI research into effective, reliable and trustworthy real-world applicationsfor learning, health, automation and other domains.References1 Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journalof Artificial Intelligence in Education, 26(2), p.582-599.2 Schön, D. A. (c1983.). The reflective practitioner: How professionals think in action /. Basic Books,.3 Osoba, O. A., & Welser IV, W. (2017). An intelligence in our image: The risks of bias and errors in artificialintelligence. Rand Corporation