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Erika A. Mosyjowski, University of Michigan; Shanna R. Daly, University of Michigan; Lisa R. Lattuca, University of Michigan

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feedback on how well an initial list of practices aligned with engineers’own academic and professional engineering experiences and asked for suggestions of additionalpractices that were reflective of their experiences. In addition, our team reviewed responses froman earlier study phase in which participants identified types of engineering skills they felt wereimportant in their work to capture those practices not on our original list [34]. Our team thensought feedback on the clarity of items through an informal focus group of undergraduate andgraduate engineering students in one of the authors’ labs. Finally, we conducted pilot interviewswith an additional seven engineering students to further check clarity and comprehensiveness ofthe list of

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Tammy Michelle McCoy, Georgia Institute of Technology; Comas Lamar Haynes, Georgia Tech Research Institute; C. Fred Higgs III, Rice University; Illya V. Hicks, Rice University; Clayton J. Clark II, Florida A&M University; Natalie Yolanda Arnett, FAMU-FSU College of Engineering; Sylvia L. Mendez, University of Colorado at Colorado Springs; Valerie Martin Conley, University of Colorado at Colorado Springs; Molly Stuhlsatz, BSCS Science Learning

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investment in this area is essential. Without this investmentthe academy’s stated commitment to broadening participation in engineering rings hollow.Funding AcknowledgementThis research is sponsored by the National Science Foundation (NSF) Alliances for GraduateEducation and the Professoriate (AGEP; award numbers: 1821298, 1821019, 1821052, and1821008). Any opinions, findings, conclusions, or recommendations are those of only theauthors and do not necessarily reflect the views of the NSF.ReferencesChakraverty, D. (2020). The imposter phenomenon among black doctoral and postdoctoral scholars in STEM. International Journal of Doctoral Studies, 15, 433-460. https://doi.org/10.28945/4613Griffin, K. A. (2019). Redoubling our efforts: How

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Jay Phillip Jefferson, Florida International University; Alexandra Coso Strong, Florida International University; Trina L. Fletcher, Florida International University; Jade R. Moten, Florida International University

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the author(s) and do not necessarily reflect theviews of the National Science Foundation.References[1] I. A. Toldson, I, “Why historically black colleges and universities are successful with graduating black baccalaureate students who subsequently earn doctorates in STEM (editor’s commentary),” J. Negro Educ., vol. 87, no. 2, pp. 95–98, 2018.[2] R. Winkle-Wagner and D. L. McCoy, “Feeling like an “Alien” or “Family”? Comparing students and faculty experiences of diversity in STEM disciplines at a PWI and an HBCU,” Race Ethn. Educ., vol. 21, no. 5, pp. 593-606, 2018.[3] R. T. Palmer, R. J. Davis, and T. Thompson, “Theory meets practice: HBCU initiatives that promote academic success among African Americans

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Mohsen Moghaddam, Northeastern University; Jacqueline A. Isaacs, Northeastern University; Sagar Kamarthi, Northeastern University; Martin Storksdieck, Oregon State University; Xiaoning Jin, Northeastern University

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Applications” innext phase of the project.AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.1935646. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation.References[1] G. Giffi, P. Wellener, B. Dollar, H. Ashton Manolian, L. Monck, and C. Moutray, “Deloitte and The Manufacturing Institute skills gap and future of work study,” 2018.[2] S. A. Ambrose, M. Lovett, M. W. Bridges, M. DiPietro, and M. K. Norman, How learning works : seven research-based principles for smart teaching. San Francisco: US: Jossey- Bass, 2010.[3] S. A. Ambrose and L

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Mark Wong, Contra Costa College; Sergio Alexander Alvarez, Contra Costa College; Jonathan Alexander Canel, Contra Costa College; Phiroze Jhalman Duggal; Yesenia Rodriguez Moreno ; Daven Ng ; Nathalie Zaldivar; Chao Liu, Contra Costa College; Jeff Kamalian, Contra Costa College; Seti Sidharta, Contra Costa College

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student body is 44% Hispanic/Latinx, 19% Asian, 17% African-American, 11%Caucasian, and 6% Two or more races. The college’s student body is 60% female and 39%male. 48% of students received Pell grants. In this project, we explore the effects of providinglow-income, underrepresented, and female students with hands-on research experience in STEM.In doing so, we hope to encourage them to continue their studies of science and technical fieldsand to give them practical context for applying what they learn in their classes. In this paper, weexamine the role of project-based learning on student retention in the technical fields. Theultimate goal is to have scientists and engineers with ethnic backgrounds better reflecting thepopulation in the

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Brian P. Self, California Polytechnic State University, San Luis Obispo; Milo Koretsky, Tufts University; Christopher Papadopoulos, University of Puerto Rico, Mayaguez Campus; James M. Widmann, California Polytechnic State University, San Luis Obispo

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workshops(e.g., NETI, ASEE section meetings, the ASEE National meeting, CW workshops), and haverecruited six participants in our Action Research Fellows program. By studying the context inwhich instructors adopt and utilize the CW, we will be able to provide recommendations forencouraging use of the CW and of other pedagogical innovations.AcknowledgmentsWe acknowledge the support from National Science Foundation (NSF) through grants DUE1821439, 1821445, 1821638, 1820888, and 1821603. Any opinions, findings, and conclusions orrecommendations expressed are those of the authors and do not necessarily reflect the views ofthe NSF.References[1] S. Freeman, S. L. Eddy, M. McDonough, M. K. Smith, N. Okoroafor, H. Jordt, and M. P. Wenderoth, “Active

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Lawrence Eric Meyer Jr., Miami Dade College; Elodie Billionniere, Miami Dade College

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which 21 werehigh school students, and 37 Solutions Architects including 10 high school students.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.1801024. 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.References[1] S. Fayer, A. Lacey and W. Watson, A. “BLS spotlight on statistics: STEM occupations-past, present, and future,” U.S. Department of Labor, Bureau of Labor Statistic, 2017. [Online]. Available: https://www.bls.gov/spotlight/2017/science-technology-engineering-and- mathematics-stem-occupations-past-present-and-future/pdf/science

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Karen Wosczyna-Birch, CT College of Technology

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and third goalsoutlined above. This relates to finding future success in the workforce and confidence indeploying 21st century workforce skills such as teamwork. The results suggest that students inYear 3 had more fully reached these goals than any previous year of CT CLICKs, reflecting astrengthened and continuously improving program. The improved results in Year 3 are attributedto a restructuring and redesign of faculty training materials which are presented in the trainingmanual delivered in fall 2019. In early fall 2019, workshops and associated resources wereredesigned to include a greater focus on student goals, the student and team building experience,and creating ways to develop workforce skills within the CT CLICKs classroom-based

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Candis S. Claiborn, Washington State University; Angela Minichiello P.E., Utah State University; Olusola Adesope, Washington State University; Ebenezer Rotimi Ewumi, Washington State University; Muhammad Asghar P.E., Utah State University

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recommendations for how to make HIEP more accessible to all E/CS students. 4. Recommendations will be provided on which HIEP are most effective, how much participation should be encouraged, and interventions for removing potential barriers to participation.Acknowledgments This material is based upon work supported by the National Science Foundation underGrant No. 1927218. Any opinion, findings, and conclusions or recommendations expressed inthis material are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.References[1] G.D. Kuh, “High-impact educational practices: What they are, who has access to them, and why they matter,” Washington, DC: Association of American

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Elizabeth A. Adams, Fresno City College; Carol Haden, Magnolia Consulting, LLC; Claire L. A. Dancz, Clemson University; Yushin Ahn, California State University at Fresno; Karen Willis, Fresno City College

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Average # of Semesters (excluding summer) Award Year # of Awards Mean Median 2012-13 1 12.0 12.0 2014-15 2 13.0 13.0 2015-16 2 4.5 4.5 2016-17 1 8.0 8.0 2018-19 1 10.0 10.0 2019-20 17 10.9 8.0 Overall 24 10.4 8.0Raw averages shown in the “Mean” column do not reflect the change that we are hoping to see;the overall average for the last eight

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Muhammad Dawood, New Mexico State University; Ehtesham Shareef, New Mexico State University; Rachel Boren, New Mexico State University; Germain Degardin, New Mexico State University; Melissa J. Guynn, New Mexico State University; Patti Wojahn, New Mexico State University

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team of researchers at a Southwest Hispanic-Serving Land-GrantUniversity embarked on an National Science Foundation-funded study to provide workshops forfirst year engineering students to introduce them to metacognitive awareness learning strategiesthat have the potential to help their study skills, and in turn, their academic performance. Toassess if these strategies were utilized and if they were helpful for students, we collected pre- andpost-intervention surveys and reflective writing journals. The survey items came from themetacognitive awareness inventory (MAI) [1] to measure pre- and post-knowledge andregulation of cognition. These surveys were administered to the introductory level engineeringclasses at the beginning and end of their

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Samantha Ruth Brunhaver, Arizona State University, Polytechnic School; Susan Sajadi, Arizona State University

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prompted to describe the circumstances, engineer's reaction, resourcesthe engineer used, and outcome for each incident. They are also asked to reflect on how theirorganization hires for and rewards adaptability.Interviews are being conducted with 15-20 engineering managers at four companies in thesouthwestern U.S. based on guidelines related to reaching code saturation in qualitative research[62] [63]. Engineering managers of different experience levels and demographic characteristicshave been recruited at each company with the help of an internal project liaison, most typically, asenior engineering manager themselves. Each company has different approaches to externalresearch, with some requiring the signing of non-disclosure agreements (NDAs

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Kenneth A. Connor, Rensselaer Polytechnic Institute; Kathy Ann Gullie, Gullie Consultant Services LLC; Barry J. Sullivan, Inclusive Engineering Consortium; Megan Bekolay ; Dean T. Spaulding ; Mandoye Ndoye, Tuskegee University; Otsebele E. Nare, Hampton University; Abdelnasser A. Eldek, Jackson State University

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learning experience, and research activities done at a distance. To gather thisinformation from REU/RET graduate mentors and undergraduate students, surveys weredeveloped and administered electronically. Items for the surveys were both Likert type items andopen ended to gather in depth information about how they moved from face to face to online/virtualclassrooms and how they addressed challenges along the way. The data included an analysis ofstudent reflections comparing perceptions from the spring 2020 semester of the COVID-19pandemic through to the present spring 2021 semester. Information focused on student perceptionsduring that time period. Qualitative and quantitative data were gathered and analyzed using theme-pattern analysis for both

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Allison Godwin, Purdue University at West Lafayette (COE); Brianna Benedict McIntyre, Purdue University; Jacqueline Ann Rohde, Purdue University at West Lafayette (COE); Herman Ronald Clements III, Purdue University at West Lafayette (COE); Heather Lee Perkins, Purdue University at West Lafayette (PPI); Joana Marques Melo, Purdue University at West Lafayette (COE); Andrea Lidia Castillo, University of California, Irvine

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the Fall of 2018. Eachinterview used journey maps to elicit students’ identity trajectories and probed further into theirshort and long-term goals and current educational environments, especially in response to theCOVID-19 global pandemic and its impact on engineering education. In this research, wespecifically use journey maps as a reflective tool for students to document their “high points” and“low points” within a particular semester (i.e., Summer 2019 to Fall 2019 or Winter 2019 to Spring2020). We also used journey maps as an artifact to guide the interviews and operate as an elementof procedural and communicative validation [11]. In alignment with the identity trajectory model,these journey maps allow us to differentiate between the most

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Amanda Gonczi, Michigan Technological University; Whitney Nicole McCoy, University of Virginia; Robert M. Handler; Jennifer L. Maeng, University of Virginia

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incorporate computers only forresearch (Wang et al., 2011). If integrated ETS instruction reflects these narrow views, studentswill not develop an understanding of the breadth of technologies and/or how they support scienceand engineering. Therefore, professional development and teacher preparation is needed toensure teachers have robust understandings and confidence to implement ETS instruction(Brophy et al., 2008; Dare Ellis, & Roehrig, 2014; Roehrig et al., 2012).The ill-defined nature of ETS instruction can also pose unique challenges for teachers. By nature,science instruction that incorporates engineering is student-focused, involves active learning, andemphasizes process rather than a single correct answer. This is a stark contrast with

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Alexander P. Parobek, Purdue University at West Lafayette (COS); Patrick M. Chaffin, Purdue University at West Lafayette (COS); Marcy H. Towns, Purdue University at West Lafayette (COS)

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𝑊 = ∫ 𝑝𝑑𝑉 𝑉𝑖 The interview protocol was broken up into three separate stages (Figure 2) thatprogressively investigated students’ interpretations of the first law and the provided interviewprompts. To start, participants were asked open-ended questions about the first law ofthermodynamics to better understand how they conceptualized the first law prior to any furtherprompting. Afterwards, students proceeded to separately address one in-discipline and one out-of-discipline interview prompt. Interview questions during the second stage were modeled afterthe dynamic transfer framework [22] by first asking questions that primed students to identifyrelevant target tools and to reflect on their

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Amber Simpson, State University of New York at Binghamton; Adam V. Maltese, Indiana University Bloomington; Jing Yang, Indiana University Bloomington ; Jungsun Kim, Indiana University Bloomington; Peter N. Knox, Binghamton University (State University of New York); Soo Hyeon Kim, Indiana University-Purdue University Indianapolis; Nikeetha Farfan D'Souza, Indiana University Bloomington

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material are those of theauthor(s) and do not necessarily reflect the views of the National Science Foundation.References[1] Museum of Science. (2021). Engineering is elementary. Available: http://www.eie.org/. [Accessed January 25, 2021].[2] Tufts University, About us: Center for engineering education and outreach. Available: http://ceeo.tufts.edu/about/. [Accessed February 15, 2021].[3] E. R. Banilower, P. S. Smith, K. A. Malzahn, C. L. Plumley, E. M. Gordon, and M. L. Hayes, Report of the 2018 NSSME+. Chapel Hill, NC: Horizon Research, Inc., 2018.[4] S. Brophy, S. Klein, M. Prtsmore, and C. Rogers, “Advancing engineering education in P-12 classrooms,” Journal of Engineering Education, vol. 97, no. 3, pp. 369-387, 2008.[5] M. W

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Melissa Dean, STEMWorks, LLC; James Van Haneghan, STEMWorks, LLC; Susan Pruet, STEMWorks, LLC; James Duke, STEMWorks, LLC.

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own words.This instrument was developed to measure indicators of impact on the SCCT constructs ofoutcome expectations and self-efficacy. Figure 2: Outcomes and Subscales of the Pre/Post Test. Note: * indicates significant differences favoring Academy Cadets.To supplement the pre/post assessment we collected qualitative data through interviews andstudent reflection journals. At the end of each day of the Academy, students were givenreflection prompts about the day’s activities. Students kept an electronic journal which captureda record of all their responses to each prompt. These journals were analyzed and comparedagainst the findings from the pre/post survey to better understand student attitudes towardSTEM, big ideas students took

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Jane L. Lehr, California Polytechnic State University, San Luis Obispo; Daniel Almeida, California Polytechnic State University, San Luis Obispo; Dominic J. Dal Bello, Allan Hancock College; Jeff Jones P.E., Cuesta College; Lizabeth L. Thompson, California Polytechnic State University, San Luis Obispo; Eva Schiorring, StemEval; Fred W. DePiero, California Polytechnic State University, San Luis Obispo; Christina Sholars Ortiz, Cuesta College

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with COVID by setting up a designated study area that is only mine.” • “Adaptability, one of my strengths, has allowed me to accept the situation and work with what has happened.” • “I have adapted and modified my life to use connectedness in new ways. I am able to talk and reach out to others by other means rather than face to face during these times by the use of technology such as zoom.”Ongoing Research: Strengths, Social Identity, and Social NetworksSurveys and interviews by the external evaluator have not explicitly asked students to reflect onthe “strengths from a social justice perspective in engineering and computer science as context”model of mentoring and advising – instead, the evaluator has focused, to date

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Samuel Enrique Blair, Texas A&M University; Henry David Banks, Georgia Institute of Technology; Julie S Linsey, Georgia Institute of Technology; Astrid Layton, Texas A&M University

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learned certain tools, or whichtools they had used prior to this semester and their college entry. Given the time between whentools were used and when students completed the surveys in question, their answers may nothave perfectly reflected their experiences. This difficulty in recalling the timeline of tool usageand when tools were learned is compounded particularly with simple tools and features of themakerspace, such as hand tools, whiteboards, or even a desk. Given that such tools and featureshave particularly interesting ramifications for makerspaces efficacy and their outcomes, theinformation lost from this could be considerable.While the information gathered does not perfectly capture how makerspaces are being used andthe motivations

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Authors

Zhen Zhao, Arizona State University, Polytechnic campus; Adam R. Carberry, Arizona State University; Jean S. Larson, Arizona State University; Michelle Jordan, Arizona State University; Wilhelmina C. Savenye, Arizona State University; Kristi L. Eustice, Arizona State University; Allison Godwin, Purdue University at West Lafayette; Gillian Roehrig, University of Minnesota - Twin Cities; Christopher Barr, Rice University; Kimberly Farnsworth, Arizona State University

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expertise of the team. Coordinated iterative cycles of reflection and action were usedfor instrument development [30-31]. The instrument currently has seven baseline categories thatcould be applied to all ERC population groups and will be used to conduct cross-ERCcomparisons. Table 1 documents the baseline categories (excluding demographics):understanding of the ERC, impact on skills, culture of inclusion, mentorship experience, futureplans, and program satisfaction. These six categories were extracted from the NSF ERC BestPractices Manual [3] and ERC program solicitation [32] as cross-cutting categories that NSFrecommends evaluating to monitor ERC progress and impact around workforce development andculture of inclusion initiatives. Comparisons

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Karinna M. Vernaza, Gannon University; Saeed Tiari, Gannon University; Scott Steinbrink, Gannon University; Lin Zhao, Gannon University; Varun K. Kasaraneni, Gannon University

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increasing behavior until the completion of the project.Additionally, the survey provided open-ended questions where students could identify the factorsthat affected the changes in their perceptions. This group was very satisfied with theirexperience and one stated that “…it got more exciting and we could see the project comingtogether.” This comment was shared by all 80% of this group. This is clearly reflected in theincreases in enthusiasm.Note that this student cohort was also included in the Alumni survey, as these students graduatedin the spring of 2019. Artifact Display Case, Project 1 7 Average Student Response

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John R. Reisel P.E., University of Wisconsin - Milwaukee; Wilkistar Otieno, University of Wisconsin - Milwaukee; Christine Beimborn, University of Wisconsin - Milwaukee

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Content Access, Virtual On line . 10.18260/1-2—3500310. Hartenstine, D., & Fizzano, P., & Brobst, J. A., & Litzler, E., & Barber DeGraaff, R. (2020, June), CS/M Scholars Program - an NSF S-STEM Project Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2— 3436011. Burckhard, S. R., & Kant, J. M., & Michna, G. J., & Abraham, R. P., & Reid, R. (2018, June), Reflections of S-STEM Faculty Mentors Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2—3092512. Reisel, J.R., & Cancado, L., &Walker, C.M., & Mitrayani, D. (2015, June), Defining a Successful Undergraduate

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Kent J. Crippen, University of Florida; Gayle Nelson Evans, University of Florida; Chelsey Simmons, University of Florida

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the goal of building teacher confidence. Finally, the SEP2 appears to be a powerful tool forunderstanding the experience and perceptions of participants in research experiences. AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.(EEC-1711543). Any opinions, findings, and 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] J. C. Brown, J. R. Bokor, K. J. Crippen, and M. J. Koroly, “Translating Current Science into Materials for High School via a Scientist–Teacher Partnership,” J

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Gail P. Baxter, Stevens Institute of Technology; Keith G. Sheppard, Stevens Institute of Technology; Susan Lowes, Teachers College, Columbia University

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universityafter more than 20 years in industry or other nonacademic positions. These faculty benefited from a moretargeted set of discussions focused on learning with understanding. Important here was attention to whatstudents bring to the learning environment (prior knowledge), organization of facts and ideas around aconceptual framework to facilitate its use in various contexts (connections within and across courses), andhelping students reflect on what they do or do not understand (metacognitive strategies) [6].Faculty and student data were collected over the five years of the project. Three sources of faculty datainclude interviews (subset each year beginning Spring 2016), reports/presentations (subset each yearbeginning Fall 2016) and teaching

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Authors

Sara A. Atwood, Elizabethtown College; Shannon Katherine Gilmartin, Stanford University; Anna M. Mostoller, Elizabethtown College; Helen L. Chen, Stanford University; Sheri Sheppard, Stanford University

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. Helen L. Chen, Stanford University Helen L. Chen is a research scientist in the Designing Education Lab in the Department of Mechanical Engineering at Stanford University. She has been involved in several major engineering education initia- tives including the NSF-funded Center for the Advancement of Engineering Education, National Center for Engineering Pathways to Innovation (Epicenter), as well as the Consortium to Promote Reflection in Engineering Education. Helen holds an undergraduate degree in communication from UCLA and a PhD in communication with a minor in psychology from Stanford University. Her current research and scholarship focus on engineering and entrepreneurship education; the pedagogy of portfolios

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Lorelie Imperial, University of Florida; Corey Payne; Kent J. Crippen, University of Florida; Maria Korolev, University of Florida; Philip J. Brucat, University of Florida; Chang-Yu Wu, University of Florida

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materialsdevelopment activities that seek to support the success of all students. AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.(DUE-1625378). Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of NSF. References[1] E. Cech, B. Rubineau, S. Silbey, and C. Seron, “Professional role confidence and gendered persistence in engineering,” Am. Sociol. Rev., vol. 76, no. 5, pp. 641–666, Oct. 2011, doi: 10.1177/0003122411420815.[2] K. A. Robinson, T. Perez, J. H. Carmel, and L. Linnenbrink-Garcia, “Science identity

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Authors

Shaylin Williams, Mississippi State University; Abigail M Clark, Ohio State University; Anastasia Nicole Doty, Ohio State University; Rachel Louis Kajfez, Ohio State University; Mahnas Jean Mohammadi-Aragh, Mississippi State University

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opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation.References[1] M. K. Orr, M. W. Ohland, R. A. Long, C. E. Brawner, S. M. Lord, and R. A. Layton, “Engineering matriculation paths: Outcomes of Direct Matriculation, First-Year Engineering, and Post-General Education Models,” Proc. Front. Educ. Conf. FIE Proc. - Front. Educ. Conf. FIE, 2012.[2] K. Reid, D. Reeping, and E. Spingola, “A Taxonomy for Introduction to Engineering Courses *,” Int. J. Eng. Educ., vol. 34, no. 1, pp. 2–19, 2018.[3] H. Matusovich, R. Streveler, and R. Miller, “Why Do Students Choose Engineering? A

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Saryn R. Goldberg, Hofstra University; Chris Venters, East Carolina University; Amy Masnick, Hofstra University

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. Researchers have used a rangeof approaches to categorize students’ questions, varying in complexity depending on the contextin which student questions were being solicited (e.g., [2], [3]). Marbach-Ad and Solokove [4]used a large sample of questions generated by biology students to develop a six-level, "semi-hierarchical” taxonomy based on question sophistication. Encouragingly, their work also showsthat students are able to pose more high-quality questions after being instructed in the taxonomyfor classifying the quality of their questions [5]. This approach has also been adapted forclassifying questions asked by physics students as part of a written reflection on their learning[6].Along with explanatory question taxonomies, question-asking can be

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Authors

Noemi V. Mendoza Diaz, Texas A&M University; Russ Meier, Milwaukee School of Engineering; Deborah Anne Trytten, University of Oklahoma; So Yoon Yoon, University of Cincinnati; Janie M. Moore, Texas A&M University; Andrea M. Ogilvie P.E., Texas A&M University; Mark Weichold, Texas A&M University

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. Whilecorrelation coefficients between items were all positive, there were only four eigenvalues greaterthan 1.0 on both ECTD beta A and B versions. This indicates there were four independent factorsmeasured by the instruments. Most items were loaded onto one factor and only one or two itemsloaded onto each of the other three factors. As the factor analysis results from the ECTD beta Aand B versions were not the desired model that can reflect the five computational thinkingfactors, there was a need for another round of revisions.Instead of designing two compatible versions A and B, the 30 items from the beta versions of theECTD were revisited for reanalyzes of content and face validity. The research team selected fourbest items to be indicators of each of