Skip to main content

Advertisement

SpringerLink
Log in

Development and testing of the Draw-a-Programmer test (DAPT) to explore elementary preservice teachers’ conceptions of computational thinking

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Recent US science standards conceptualize science as a set of shared multidisciplinary ideas and practices in common with engineering and computer science (CS). At the core, this portrayal requires an understanding of CS as a viable career path and set of discrete knowledge and skills, including those related to computer programming. However, research repeatedly shows inservice and preservice teachers to be unfamiliar or uncomfortable with reform-based instruction and CS-related careers. This exploratory study uses a Draw-a-Programmer Test (DAPT) instrument (adapted from the Draw-a-Scientist [DAST], Engineering [DAET], and Computer Scientist [DACST]) to investigate how preservice teachers understand and visualize computer programming. Here, we detail the development and testing of this tool across two preservice elementary science and technology courses. Participants in this study included 52 preservice teachers in the last semester of their teacher preparation program enrolled in these courses. Data were collected using the DAPT instrument and were analyzed using open coding of respondents’ depictions and written descriptions of computer programming. Findings revealed that participants held somewhat stereotypical, yet distinct conceptions of CS and computer programming (i.e., separate from science and engineering) which may provide concrete entry points into fostering computational thinking skills. Implications are discussed as they relate to elementary teacher education and research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Alves, N. D. C., Von Wangenheim, C. G., & Hauck, J. C. (2019). Approaches to assess computational thinking competences based on code analysis in K-12 education: A systematic mapping study. Informatics in Education, 18(1), 17.

    Article  Google Scholar 

  • Anastasiadou, S. D., & Karakos, A. S. (2011). The beliefs of electrical and computer engineering students’ regarding computer programming. The International Journal of Technology, Knowledge and Society, 7(1), 37–51.

    Article  Google Scholar 

  • Antink-Meyer, A., & Meyer, D. Z. (2016). Science teachers’ misconceptions in science and engineering distinctions: Reflections on modern research examples. Journal of Science Teacher Education, 27(6), 625–647.

    Article  Google Scholar 

  • Banilower, E. R., Smith, P. S., Malzahn, K. A., Plumley, C. L., Gordon, E. M., & Hayes, M. L. (2018). Report of the 2018 NSSME+. Horizon Research, Inc.

    Google Scholar 

  • Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54.

    Article  Google Scholar 

  • Bean, N., Weese, J., Feldhausen, R., & Bell, R. S. (2015, October). Starting from scratch: Developing a pre-service teacher training program in computational thinking. In 2015 IEEE Frontiers in Education Conference (FIE) (pp. 1–8).

  • Bers, M. U. (2018). Coding as a playground. Taylor & Francis.

    Google Scholar 

  • Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). Developing computational thinking in compulsory education-Implications for policy and practice (No. JRC104188). Joint Research Centre (Seville site). Retrieved from https://ideas.repec.org/p/ipt/iptwpa/jrc104188.html

  • Bower, M., Wood, L. N., Lai, J. W., Howe, C., Lister, R., Mason, R., Highfield, K., & Veal, J. (2017). Improving the computational thinking pedagogical capabilities of school teachers. Australian Journal of Teacher Education, 42(3). https://doi.org/10.14221/ajte.2017v42n3.4

  • Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In: Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada (Vol. 1, p. 25).

  • Bryan, L., & Guzey, S. S. (2020). K-12 STEM education: An overview of perspectives and considerations. Hellenic Journal of STEM Education, 1(1), 5–15.

    Article  Google Scholar 

  • Burger, C. (2014). Gender equity in science, engineering, and technology. In S. S. Klein, B. Richardson, D. A. Grayson, L. H. Fox, C. Kramarae, D. S. Pollard, & C. A. Dwyer (Eds.), Handbook for achieving gender equity through education. Routledge.

    Google Scholar 

  • Çetin, M., & Demircan, H. Ö. (2020). Empowering technology and engineering for STEM education through programming robots: A systematic literature review. Early Child Development and Care, 190(9), 1323–1335. https://doi.org/10.1080/03004430.2018.1534844

    Article  Google Scholar 

  • Chambers, D. W. (1983). Stereotypic images of the scientist: The draw-a-scientist test. Science Education, 67(2), 255–265. https://doi.org/10.1002/sce.3730670213

    Article  Google Scholar 

  • Chang, Y. H., & Peterson, L. (2018). Pre-service teachers’ perceptions of computational thinking. Journal of Technology and Teacher Education, 26(3), 353–374.

    Google Scholar 

  • Chatley, R., Donaldson, A., & Mycroft, A. (2019). The next 7000 programming languages. In T. B. Steffen & G. Woeginger (Eds.), Computing and software science: State of the art and perspectives (1st ed., pp. 250–282). Springer. https://doi.org/10.1007/978-3-319-91908-915

    Chapter  MATH  Google Scholar 

  • Chen, B., Bastedo, K., & Howard, W. (2018). Exploring design elements for online STEM courses: Active learning, engagement & assessment design. Online Learning, 22(2), 59–75.

    Article  Google Scholar 

  • Cheryan, S., Plaut, V. C., Handron, C., & Hudson, L. (2013). The stereotypical computer scientist: Gendered media representations as a barrier to inclusion for women. Sex Roles, 69(1–2), 58–71.

    Article  Google Scholar 

  • Computer Science Teachers Association. (2013). Bugs in the system: Computer science teacher certification in the U.S.; https://c.ymcdn.com/sites/www.csteachers.org/resource/resmgr/CSTA_BugsInTheSystem.pdf

  • Cortesi, S. C., Hasse, A., Lombana, A., Kim, S., & Gasser, U. (2020). Youth and digital citizenship+ (plus): Understanding skills for a digital world. SSRN Electronic Journal, 7641. https://doi.org/10.2139/ssrn.3557518

  • Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.

    MATH  Google Scholar 

  • Doleck, T., Bazelais, P., Lemay, D. J., Saxena, A., & Basnet, R. B. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: Exploring the relationship between computational thinking skills and academic performance. Journal of Computers in Education, 4(4), 355–369.

    Article  Google Scholar 

  • Duncan, R. G., & Cavera, V. L. (2015). DCIs, SEPs, and CCs, oh my! Understanding the three dimensions of the NGSS. Science Scope, 39(2), 50.

    Google Scholar 

  • Ehsan, H., Rehmat, A. P., & Cardella, M. E. (2020). Computational thinking embedded in engineering design: Capturing computational thinking of children in an informal engineering design activity. International Journal of Technology and Design Education, 1–24.

  • Finson, K. D. (2002). Drawing a scientist: What we do and do not know after fifty years of drawings. School Science and Mathematics, 102, 335–345. https://doi.org/10.1111/j.1949-8594.2002.tb18217.x

    Article  Google Scholar 

  • Ford, M. J. (2015). Educational implications of choosing “practice” to describe science in the next generation science standards. Science Education, 99(6), 1041–1048.

    Article  Google Scholar 

  • Fralick, B., Kearn, J., Thompson, S., & Lyons, J. (2009). How middle schoolers draw engineers and scientists. Journal of Science Education and Technology, 18(1), 60–73.

    Article  Google Scholar 

  • Garcia, A., & Lee, C. H. (2020). Equity-centered approaches to educational technology. In M. J. Bishop, E. Boling, J. Elen, & V. Svihla (Eds.), Handbook of research in educational communications and technology: Learning design (5th ed., pp. 247–261). Springer Nature.

    Chapter  Google Scholar 

  • Gorski, P. C. (2002). How computers STILL cheat girls. The Education Digest, 68(3), 23.

    Google Scholar 

  • Gorski, P. (2005). Education equity and the digital divide. AACE Journal, 13(1), 3–45.

    Google Scholar 

  • Gorski, P. (2016). Rethinking the role of “culture” in educational equity: From cultural competence to equity literacy. Multicultural Perspectives, 18(4), 221–226.

    Article  Google Scholar 

  • Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43.

    Article  Google Scholar 

  • Grover, S., & Pea, R. (2018). Computational thinking: A competency whose time has come. Computer science education: Perspectives on teaching and learning in school, 19.

  • Günbatar, M. S., & Bakırcı, H. (2019). STEM teaching intention and computational thinking skills of pre-service teachers. Education and Information Technologies, 24(2), 1615–1629.

    Article  Google Scholar 

  • Hall, J., Roman, C., Jovel-Arias, C., & Young, C. (2020). Pre-service teachers examine digital equity amidst schools' COVID-19 responses. Journal of Technology and Teacher Education, 28(2), 435–442.

  • Hansen, A. K., Dwyer, H. A., Iveland, A., Talesfore, M., Wright, L., Harlow, D. B., & Franklin, D. (2017, March). Assessing children's understanding of the work of computer scientists: The draw-a-computer-scientist test. In: Proceedings of the 2017 ACM SIGCSE technical symposium on computer science education (pp. 279–284).

  • Hillman, S. J., Bloodsworth, K. H., Tilburg, C. E., Zeeman, S. I., & List, H. E. (2014). K-12 Students' perceptions of scientists: Finding a valid measurement and exploring whether exposure to scientists makes an impact. International Journal of Science Education, 36(15), 2580–2595.

    Article  Google Scholar 

  • Hsu, M. C., Purzer, S., & Cardella, M. E. (2011). Elementary teachers’ views about teaching design, engineering, and technology. Journal of Pre-College Engineering Education Research (J-PEER), 1(2), 5.

    Google Scholar 

  • Hunsaker, E. (2018). Computational thinking. In A. Ottenbreit-Leftwich & R. Kimmons (Eds.), The K-12 educational technology handbook. EdTech Books Retrieved from https://edtechbooks.org/k12handbook/computational_thinking

    Google Scholar 

  • International Society for Technology in Education. (2017). ISTE standards for educators. International Society for Technology in Education. https://www.iste.org/standards/for-educatorsInternational

  • K-12 Computer Science Framework. (2016). Retrieved from http://www.k12cs.org.

  • Kale, U., & Yuan, J. (2020). Still a new kid on the block? Computational thinking as problem solving in code. Org. Journal of Educational Computing Research, 59(4), 620–644. https://doi.org/10.1177/0735633120972050

    Article  Google Scholar 

  • Ketelhut, D. J., Mills, K., Hestness, E., Cabrera, L., Plane, J., & McGinnis, J. R. (2020). Teacher change following a professional development experience in integrating computational thinking into elementary science. Journal of Science Education and Technology, 29(1), 174–188.

    Article  Google Scholar 

  • Kim, C., Yuan, J., Vasconcelos, L., Shin, M., & Hill, R. B. (2018). Debugging during block-based programming. Instructional Science, 46(5), 767–787.

    Article  Google Scholar 

  • Knight, M., and Cunningham, C. (2004). Draw an engineer test (DAET): development of a tool to investigate students’ ideas about engineers and engineering. Paper presented at the Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition, Salt Lake City, UT.

  • Knuth, D. E. (1997). The art of computer programming (Vol. 3). Pearson Education.

    MATH  Google Scholar 

  • Kopcha, T. J., Neumann, K. L., Ottenbreit-Leftwich, A., & Pitman, E. (2020). Process over product: The next evolution of our quest for technology integration. Educational Technology Research and Development, 68(2), 729–749. https://doi.org/10.1007/s11423-020-09735-y

    Article  Google Scholar 

  • Krajcik, J. (2015). Three-dimensional instruction. The Science Teacher, 82(8), 50.

    Article  Google Scholar 

  • Lee, O., & Buxton, C. A. (2010). Diversity and equity in science education: Research, policy, and practice. Multicultural education series. Teachers College Press.

    Google Scholar 

  • Lee, I., & Malyn-Smith, J. (2020). Computational thinking integration patterns along the framework defining computational thinking from a disciplinary perspective. Journal of Science Education and Technology, 29(1), 9–18.

    Article  Google Scholar 

  • Lee, I., Grover, S., Martin, F., Pillai, S., & Malyn-Smith, J. (2020). Computational thinking from a disciplinary perspective: Integrating computational thinking in K-12 science, technology, engineering, and mathematics education. Journal of Science Education and Technology, 29(1), 1–8.

    Article  Google Scholar 

  • Li, C., Chan, E., Denny, P., Luxton-Reilly, A., & Tempero, E. (2019, January). Towards a framework for teaching debugging. In Proceedings of the Twenty-First Australasian Computing Education Conference (pp. 79–86).

  • Li, P. L., Ko, A. J., & Begel, A. (2020). What distinguishes great software engineers? Empirical Software Engineering, 25(1), 322–352.

    Article  Google Scholar 

  • Losh, S. C., Wilke, R., & Pop, M. (2008). Some methodological issues with “draw a scientist tests” among young children. International Journal of Science Education, 30(6), 773–792.

    Article  Google Scholar 

  • Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41(1), 51–61.

    Article  Google Scholar 

  • Marginson, S., Tytler, R., Freeman, B., & Roberts, K. (2013). STEM: country comparisons: international comparisons of science, technology, engineering and mathematics (STEM) education. Final report.

  • Martin, C. D. (2004). Draw a computer scientist. ACM SIGCSE Bulletin, 36(4), 11–12.

    Article  Google Scholar 

  • McComas, W. F., & Nouri, N. (2016). The nature of science and the next generation science standards: Analysis and critique. Journal of Science Teacher Education, 27(5), 555–576.

    Article  Google Scholar 

  • McGuffee, J. W. (2000). Defining computer science. ACM SIGCSE Bulletin, 32(2), 74–76.

    Article  Google Scholar 

  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: A sourcebook of new methods 2nd edition. Sage Publishing.

    Google Scholar 

  • Milford, T. M., & Tippett, C. D. (2013). Preservice teachers’ images of scientists: Do prior science experiences make a difference? Journal of Science Teacher Education, 24(4), 745–762.

    Article  Google Scholar 

  • Miller, D. I., Nolla, K. M., Eagly, A. H., & Uttal, D. H. (2018). The development of children's gender-science stereotypes: A meta-analysis of 5 decades of US draw-a-scientist studies. Child Development, 89(6), 1943–1955.

    Article  Google Scholar 

  • Moore, P. (2003). Career directions computer repair technician. Tech Directions, 62(6), 30.

    Google Scholar 

  • Mouza, C., Yang, H., Pan, Y. C., Ozden, S. Y., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3).

  • N.G.S.S. Lead States. (2013). Next generation science standards. National Academies Press.

    Google Scholar 

  • National Science Teaching Association (NSTA). (2020). STEM education teaching and learning. NSTA Position Statement. https://www.nsta.org/about/positions/stem.aspx

  • New York State Education Department. (2017). New York State P-12 science learning standards. http://www.nysed.gov/common/nysed/files/programs/curriculum-instruction/p-12-science-learning-standards.pdf

  • Office of the Chief Scientist. (2014). Science, technology, engineering and mathematics: Australia’s future. Australian Government, Canberra. Retrieved from https://www.chiefscientist.gov.au/sites/default/files/STEM_AustraliasFuture_Sept2014_Web.pdf

  • Ozel, M. (2012). Children's images of scientists: Does grade level make a difference? Educational Sciences: Theory and Practice, 12(4), 3187–3198.

    Google Scholar 

  • Papert, S., & Harel, I. (1991). Situating constructionism. Constructionism, 1–11. https://doi.org/10.1111/1467-9752.00269

  • Park, W., Wu, J. Y., & Erduran, S. (2020). The nature of STEM disciplines in the science education standards documents from the USA, Korea and Taiwan. Science & Education, 29(4), 899–927.

    Article  Google Scholar 

  • Peters-Burton, E. E., Cleary, T. J., & Kitsantas, A. (2018). Computational thinking in the context of science and engineering practices: A self-regulated learning approach. In D. Sampson, D. Ifenthaler, J. M. Spector, & P. Isaías (Eds.), Digital technologies: Sustainable innovations for improving teaching and learning (1st ed., pp. 223–240). https://doi.org/10.1007/978-3-319-73417-0_13

    Chapter  Google Scholar 

  • Radloff, J., & Capobianco, B. M. (2019). Investigating elementary teachers’ tensions and mitigating strategies related to integrating engineering design-based science instruction. Research in Science Education, 1–20.

  • Reinisch, B., Krell, M., Hergert, S., Gogolin, S., & Krüger, D. (2017). Methodical challenges concerning the draw-a-scientist test: A critical view about the assessment and evaluation of learners’ conceptions of scientists. International Journal of Science Education, 39(14), 1952–1975.

    Article  Google Scholar 

  • Resnick, M. (2018). Lifelong kindergarten: Cultivating creativity through projects, passion, peers, and play. MIT Press.

    Google Scholar 

  • Rich, P. J., & Hodges, C. B. (2017). In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking. Springer. https://doi.org/10.1007/978-3-319-52691-1

    Chapter  Google Scholar 

  • Rich, P. J., Browning, S. F., Perkins, M. K., Shoop, T., Yoshikawa, E., & Belikov, O. M. (2019). Coding in K-8: International trends in teaching elementary/primary computing. TechTrends, 63(3), 311–329. https://doi.org/10.1007/s11528-018-0295-4

    Article  Google Scholar 

  • Rijke, W. J., Bollen, L., Eysink, T. H., & Tolboom, J. L. (2018). Computational thinking in primary school: An examination of abstraction and decomposition in different age groups. Informatics in Education, 17(1), 77–92.

    Article  Google Scholar 

  • Sadik, O., Ottenbreit-Leftwich, A., & Nadiruzzaman, H. (2017). Computational thinking conceptions and misconceptions: Progression of preservice teacher thinking during computer science lesson planning. In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 221–238). Springer.

    Chapter  Google Scholar 

  • Saldaña, J. (2015). The coding manual for qualitative researchers. Sage.

    Google Scholar 

  • Selby, C. C. (2015, November). Relationships: Computational thinking, pedagogy of programming, and Bloom's Taxonomy. In: Proceedings of the workshop in primary and secondary computing education (pp. 80–87).

  • Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158.

    Article  Google Scholar 

  • So, H. J., Jong, M. S. Y., & Liu, C. C. (2020). Computational thinking education in the Asian Pacific region. The Asia-Pacific Education Researcher, 29(1), 1–8. https://doi.org/10.1007/s40299-019-00494-w

    Article  Google Scholar 

  • Sommerville, I. (1998). Systems engineering for software engineers. Annals of Software Engineering, 6(1–4), 111–129.

    Article  Google Scholar 

  • Sullivan, A. A. (2019). Breaking the STEM stereotype: Reaching girls in early childhood. Rowman & Littlefield Publishers.

    Google Scholar 

  • Sullivan, F. R., Kapur, M., Madden, S., & Shipe, S. (2015). Exploring the role of ‘Gendered' discourse styles in online science discussions. International Journal of Science Education, 37(3), 484–504.

    Article  Google Scholar 

  • Takahashi, B., & Tandoc Jr., E. C. (2016). Media sources, credibility, and perceptions of science: Learning about how people learn about science. Public Understanding of Science, 25(6), 674–690.

    Article  Google Scholar 

  • Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798.

    Article  Google Scholar 

  • Technology and Engineering Educators Association (ITEEA). (2017). Technological literacy standards: Technology education vs educational technology. Retrieved from https://www.iteea.org/Activities/2142/Technological_Literacy_Standards/45979/51801.aspx

  • Thomas, J., Hawley, L. R., & DeVore-Wedding, B. (2020). Expanded understanding of student conceptions of engineers: Validation of the modified draw-an-engineer test (mDAET) scoring rubric. School Science and Mathematics, 120(7), 391–401.

    Article  Google Scholar 

  • Van-Roy, P., & Haridi, S. (2004). Concepts, techniques, and models of computer programming. MIT Press.

    Google Scholar 

  • Vee, A. (2013). Understanding computer programming as a literacy. Literacy in Composition Studies, 1(2), 42–64.

    Article  Google Scholar 

  • Waterman, K. P., Goldsmith, L., & Pasquale, M. (2020). Integrating computational thinking into elementary science curriculum: An examination of activities that support students’ computational thinking in the service of disciplinary learning. Journal of Science Education and Technology, 29(1), 53–64.

    Article  Google Scholar 

  • Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147.

    Article  Google Scholar 

  • Windschitl, M. A., & Stroupe, D. (2017). The three-story challenge: Implications of the next generation science standards for teacher preparation. Journal of Teacher Education, 68(3), 251–261.

    Article  Google Scholar 

  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

    Article  Google Scholar 

  • Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725.

    Article  MathSciNet  MATH  Google Scholar 

  • Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), 1–16.

    Article  Google Scholar 

  • Yadav, A., Gretter, S., Good, J., & McLean, T. (2017). Computational thinking in teacher education. In: Emerging research, practice, and policy on computational thinking (pp. 205–220). Springer.

  • Yadav, A., Sands, P., Good, J., & Lishinki, A. (2018). Computer science and computational thinking in the curriculum: Research and practice. In J. Voogt, G. Knezek, R. Christensen, & K. W. Lai (Eds.), Second handbook of information Technology in Primary and Secondary Education. Springer. https://doi.org/10.1007/978-3-319-71054-9_6

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Childhood/Early Childhood Education Department, SUNY Cortland, Cortland, NY, USA

    Jeffrey Radloff & Jacob A. Hall

Authors
  1. Jeffrey Radloff
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacob A. Hall
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeffrey Radloff.

Ethics declarations

The authors did not receive support from any organization for the submitted work. The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest associated with this submitted work.

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the SUNY Cortland university Institutional Review Board (#192046).

Consent statement

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

The Draw-a-Programmer Test instrument.

figure a

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Radloff, J., Hall, J.A. Development and testing of the Draw-a-Programmer test (DAPT) to explore elementary preservice teachers’ conceptions of computational thinking. Educ Inf Technol 27, 4301–4320 (2022). https://doi.org/10.1007/s10639-021-10787-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-021-10787-7

Keywords

Search

Navigation