Skip to main content

Advertisement

Log in

Elementary Students Learning Computer Programming: an investigation of their knowledge Retention, Motivation, and perceptions

  • Research Article
  • Published:
Educational technology research and development Aims and scope Submit manuscript

Abstract

Students need to learn and practice computational thinking and skills throughout PreK-12 to be better prepared for entering college and future careers. We designed a math-infused computer science course for third to fifth graders to learn programming. This study aims to investigate the impact of the course on students’ knowledge acquisition of mathematical and computational concepts, motivation, and perceptions of the computing activities. Fifty-one students at a Boys and Girls Club participated in the study. Data collection procedures include pre- and post-tests, pre- and post-surveys, in-class observations, and one-on-one interviews. Results indicate that students have improved significantly on mathematical and computational concepts. They also tended to believe computer programming is fun, comprehensible, enjoyable, and were able to perceive the value of learning it. Implications and recommendations for future research are also discussed.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • State of Computer Science Education (2021). Retrieved from https://advocacy.code.org/

  • Akinola, S. O. (2015). Computer programming skill and gender difference: An empirical study. American Journal of Scientific and Industrial Research, 7(1), 1–9. https://doi.org/10.5251/ajsir.2016.7.1.1.9

    Google Scholar 

  • Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47–57

    Google Scholar 

  • Armoni, M. (2012). Teaching CS in kindergarten: How early can the pipeline begin? ACM Inroads, 3(4), 18–19. https://doi.org/10.1145/2381083.2381091

    Article  Google Scholar 

  • Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670. https://doi.org/10.1016/j.robot.2015.10.008

    Article  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, 48–54. https://doi.org/10.1145/1929887.1929905

    Article  Google Scholar 

  • Basawapatna, A. R., Koh, K. H., & Repenning, A. (2010, June). Using scalable game design to teach computer science from middle school to graduate school. In Proceedings of the fifteenth annual conference on Innovation and technology in computer science education (pp. 224–228). ACM. https://doi.org/10.1145/1822090.1822154

  • Belanger, C., Christenson, H., & Lopac, K. (2018). Confidence and common challenges: The effects of teaching computational thinking to students ages 10–16 [Master’s thesis, St. Catherine University]. SOPHIA Repository. https://sophia.stkate.edu/maed/267

  • Benton, L., Hoyles, C., Kalas, I., & Noss, R. (2017). Bridging primary programming and mathematics: Some findings of design research in England. Digital Experiences in Mathematics Education, 3(2), 115–138. https://doi.org/10.1007/s40751-017-0028-x

    Article  Google Scholar 

  • Berland, M., & Wilensky, U. (2015). Comparing virtual and physical robotics environments for supporting complex systems and computational thinking. Journal of Science Education and Technology, 24(5), 628–647. https://doi.org/10.1007/s10956-015-9552-x

    Article  Google Scholar 

  • Bers, M., & Horn, M. (2010). Tangible programming in early childhood: Revisiting developmental assumptions through new technologies. In I. Berson, & M. Berson (Eds.), High-tech tots: Childhood in a digital world (pp. 49–70). Information Age Publishing

  • Bubnó, K., & Takács, V. L. (2019). Cognitive aspects of mathematics-aided computer science teaching. Acta Polytechnica Hungarica, 16(6), 73–93. http://acta.uni-obuda.hu/Bubno_Takacs_93.pdf

    Google Scholar 

  • Burke, Q. (2016). Mind the metaphor: Charting the rhetoric about introductory programming in K-12 schools. On the Horizon, 24(3), 210–220. https://doi.org/10.1108/OTH-03-2016-0010

    Article  Google Scholar 

  • Burnard, P. (1991). A method of analysing interview transcripts in qualitative research. Nurse Education Today, 11(6), 461–466. https://doi.org/10.1016/0260-6917(91)90009-Y

    Article  Google Scholar 

  • Caglar, F., Shekhar, S., Gokhale, A., Basu, S., Rafi, T., Kinnebrew, J., & Biswas, G. (2018). Simulation modelling practice and theory cloudhosted simulation-as-a-service for high school STEM education. Simulation Modelling Practice and Theory, 58(2015), 255–273. https://doi.org/10.1016/j.simpat.2015.06.006

  • Calder, N. (2010). Using Scratch: An integrated problem-solving approach to mathematical thinking. Australian Primary Mathematics Classroom, 15(4), 9–14. https://doi.org/10.1007/s10857-012-9226-z

    Article  Google Scholar 

  • Clements, D. H. (2002). Computers in early childhood mathematics. Contemporary Issues in Early Childhood, 3(2), 160–181

    Article  Google Scholar 

  • Clements, D. H., Battista, M. T., & Sarama, J. (2001). Logo and geometry. National Council of Teachers of Mathematics. https://doi.org/10.2307/749924

  • Coşar, M., & Özdemir, S. (2020). The effects of computer programming on elementary school students’ academic achievement and attitudes towards computer. Elementary Education Online, 19(3), 1509–1522. https://doi.org/10.17051/ilkonline.2020.732794

    Article  Google Scholar 

  • Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.

  • Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33–39. https://doi.org/10.1145/2998438

    Article  Google Scholar 

  • Felleisen, M., & Krishnamurthi, S. (2009). Viewpoint: Why computer science doesn’t matter. Communication of the ACM, 52(7), 37–40. https://doi.org/10.1145/1538788.1538803

    Article  Google Scholar 

  • Fisler, K., Schanzer, E., Weimar, S., Fetter, A., Renninger, K. A., Krishnamurthi, S. … Koerner, C. (2021, March). Evolving a K-12 curriculum for integrating computer science into mathematics. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 59–65). Association for Computing Machinery. https://doi.org/10.1145/3408877.3432546

  • Flannery, L. P., Silverman, B., Kazakoff, E. R., Bers, M. U., Bontá, P., & Resnick, M. (2013). Designing ScratchJr: Support for early childhood learning through computer programming. In Proceedings of the 12th International Conference on Interaction Design and Children (pp. 1–10). ACM. https://doi.org/10.1145/2485760.2485785

  • Fluck, A., Webb, M., Cox, M., Angeli, C., Malyn-Smith, J., Voogt, J., & Zagami, J. (2016). Arguing for computer science in the school curriculum. Educational Technology and Society, 19(3), 38–46

    Google Scholar 

  • Garneli, V., & Giannakos, M. N. (2015). Computing education in K-12 schools: A review of the literature. In Proceedings of 2015 IEEE Global Engineering Education Conference (EDUCON), p. 543–551. https://doi.org/10.1109/EDUCON.2015.7096023

  • Gim, N. G. (2021). Development of life skills program for primary school students: Focus on entry programming. Computers, 10(5), 1–17. https://doi.org/10.3390/computers10050056

    Article  Google Scholar 

  • Google Inc. & Gallup Inc (2016). Trends in the state of computer science in U.S. K-12 schools.http://goo.gl/j291E0

  • Grover, S., & Pea, R. (2013). Using a discourse-intensive pedagogy and android’s app inventor for introducing computational concepts to middle school students. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education (pp. 723–728). ACM. https://doi.org/10.1145/2445196.2445404

  • Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199–237. https://doi.org/10.1080/08993408.2015.1033142

    Article  Google Scholar 

  • Gutierrez, F. J., Simmonds, J., Hitschfeld, N., Casanova, C., Sotomayor, C., & Peña-Araya, V. (2018). Assessing software development skills among K-6 learners in a project-based workshop with Scratch. Proceedings of the 40th International Conference on Software Engineering: Software Engineering Education and Training (pp. 98–107). IEEE Xplore

  • Harel, I., & Papert, S. (1990). Software design as a learning environment. Interactive Learning Environments, 1(1), 1–32. https://doi.org/10.1080/1049482900010102

    Article  Google Scholar 

  • Hickmott, D., Prieto-Rodriguez, E., & Holmes, K. (2018). A scoping review of studies on computational thinking in K–12 mathematics classrooms. Digital Experiences in Mathematics Education, 4(1), 48–69. https://doi.org/10.1007/s40751-017-0038-8

    Article  Google Scholar 

  • Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310. https://doi.org/10.1016/j.compedu.2018.07.004

    Article  Google Scholar 

  • Hughes, J., Gadanidis, G., & Yiu, C. (2017). Digital making in elementary mathematics education. Digital Experiences in Mathematics Education, 3(2), 139–153. https://doi.org/10.1007/s40751-016-0020-x

    Article  Google Scholar 

  • Jenkins, C. (2015). A work in progress paper: Evaluating a microworlds-based learning approach for developing literacy and computational thinking in cross-curricular contexts. Proceedings of the Workshop in Primary and Secondary Computing Education (pp. 61–64).ACM. https://doi.org/10.1145/2818314.2818316

  • Kumar, D. (2014). Digital playgrounds for early computing education. ACM Inroads, 5(1), 20–21. https://doi.org/10.1145/2568195.2568200

    Article  Google Scholar 

  • Lakanen, A. J., & Kärkkäinen, T. (2019). Identifying pathways to computer science: The long-term impact of short-term game programming outreach interventions. ACM Transactions on Computing Education (TOCE), 19(3), 1–30. https://doi.org/10.1145/3283070

    Article  Google Scholar 

  • Lambert, L., & Guiffre, H. (2009). Computer science outreach in an elementary school. Journal of Computing Sciences in Colleges, 24(3), 118–124

    Google Scholar 

  • Lambić, D., Đorić, B., & Ivakić, S. (2020). Investigating the effect of the use of code.org on younger elementary school students’ attitudes towards programming. Behaviour and Information Technology. Advance online publication. https://doi.org/10.1080/0144929X.2020.1781931

  • Lee, Y., & Cho, J. (2019). Quantifying the effects of programming education on students’ knowledge representation and perception in computational thinking. International Journal of Innovation, Creativity and Change, 9(4), 27–38

    Google Scholar 

  • Leedy, P. D., & Ormrod, J. E. (2016). Practical research: Planning and design. Pearson

  • Lewis, C. M. (2010). How programming environment shapes perception, learning and goals: Logo vs. Scratch. Proceedings of the 41st ACM Technical Symposium on Computer Science Education (pp. 346–350). ACM. https://doi.org/10.1145/1734263.1734383

  • Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage Publications.

  • Lu, J. J., & Fletcher, G. H. (2009). Thinking about computational thinking. Proceedings of Proceedings of the 40th ACM Technical Symposium on Computer Science Education (pp. 260–264). ACM. https://doi.org/10.1145/1508865.1508959

  • 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, 51–61. https://doi.org/10.1016/j.chb.2014.09.012

    Article  Google Scholar 

  • Maloney, J. H., Peppler, K., Kafai, Y., Resnick, M., & Rusk, N. (2008). Programming by choice: Urban youth learning programming with Scratch. Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (pp. 367– 371). ACM. https://doi.org/10.1145/1352135.1352260

  • Maloney, J. H., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The Scratch programming language and environment. ACM Transactions on Computing Education, 10(4), 16. https://doi.org/10.1145/1868358.1868363

    Article  Google Scholar 

  • Manches, A., & Plowman, L. (2017). Computing education in children’s early years: A call for debate. British Journal of Educational Technology, 48(1), 191–201. https://doi.org/10.1111/bjet.12355

    Article  Google Scholar 

  • Matere, I. M., Weng, C., Astatke, M., Hsia, C. H., & Fan, C. G. (2021). Effect of design-based learning on elementary students computational thinking skills in visual programming maker course. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2021.1938612

  • Meyer, D., & Batzner, A. (2016, November). Engaging computer science non-majors by teaching K-12 pupils programming: first experiences with a large-scale voluntary program. Proceedings of the 16th Koli Calling International Conference on Computing Education Research (pp. 174–175). ACM. https://doi.org/10.1145/2999541.2999563

  • Mioduser, D., Levy, S., & Talis, V. (2009). Episodes to scripts to rules: Concrete abstractions in kindergarten children’s explanations of a robot’s behaviors. International Journal of Technology and Design Education, 19(1), 15–36. https://doi.org/10.1007/s10798-007-9040-6

    Article  Google Scholar 

  • Mladenović, M., Žanko, Ž., & Aglić Čuvić, M. (2021). The impact of using program visualization techniques on learning basic programming concepts at the K–12 level. Computer Applications in Engineering Education, 29(1), 145–159. https://doi.org/10.1002/cae.22315

    Article  Google Scholar 

  • Morelli, R., De Lanerolle, T., Lake, P., Limardo, N., Tamotsu, E., & Uche, C. (2011). Can android app inventor bring computational thinking to K-12. Proceedings. 42nd ACM Technical Symposium on Computer Science Education (SIGCSE’11) (pp. 1–6). ACM

  • Mouza, C., Yadav, A., & Ottenbreit-Leftwich, A. (2018). Developing computationally literate teachers: Current perspectives and future directions for teacher preparation in computing education. Journal of Technology and Teacher Education, 26(3), 333–352

    Google Scholar 

  • Namukasa, I. K., Kotsopoulos, D., Floyd, L., Weber, J., Kafai, Y. B., Khan, S., et al. (2015). From computational thinking to computational participation: Towards achieving excellence through coding in elementary schools. In G. Gadanidis (Ed.), Math + coding symposium. Western University

  • Neri, F. (2021). Teaching mathematics to computer scientists: Reflections and a case study. SN Computer Science, 2(2), https://doi.org/10.1007/s42979-021-00461-7

  • Niemelä, P. S., & Helevirta, M. (2017). K-12 curriculum research: The chicken and the egg of math-aided ICT teaching. International Journal of Modern Education and Computer Science, 9(1), 1–14. https://doi.org/10.5815/ijmecs.2017.01.01

    Article  Google Scholar 

  • Niemelä, P., Partanen, T., Harsu, M., Leppänen, L., & Ihantola, P. (2017). Computational thinking as an emergent learning trajectory of mathematics. ACM International Conference Proceeding Series, 70–79. https://doi.org/10.1145/3141880.3141885

  • Noh, J., & Lee, J. (2020). Effects of robotics programming on the computational thinking and creativity of elementary school students. Educational Technology Research and Development, 68(1), 463–484. https://doi.org/10.1007/s11423-019-09708-w

    Article  Google Scholar 

  • Papastergiou, M. (2009). Digital game-based learning in high-school computer science education: Impact on educational effectiveness and student motivation. Computers and Education, 52(1), 1–12. https://doi.org/10.1016/j.compedu.2008.06.004

    Article  Google Scholar 

  • Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). Sage Publications

    Google Scholar 

  • Papert, S., Watt, D., diSessa, A., & Weir, S. (1979). Final report of the Brookline Logo Project: Project summary and data analysis (Logo Memo 53). MIT Logo Group

    Google Scholar 

  • Powers, J., & Azhar, M. (2020). Preparing teachers to engage students in computational thinking through an introductory robot design activity. Journal of Computers in Mathematics and Science Teaching, 39(1), 49–70

    Google Scholar 

  • Prottsman, K. (2014). Computer science for the elementary classroom. ACM Inroads, 5(4), 60–63

    Article  Google Scholar 

  • Qualls, J. A., & Sherrell, L. B. (2010). Why computational thinking should be integrated into the curriculum. Journal of Computing Sciences in Colleges, 25(5), 66–71

    Google Scholar 

  • Razak, M. R. B., & Ismail, N. Z. B. (2018). Influence of mathematics in programming subjects. In American Institute Physics Conference Proceedings, 1974, Article 050011. https://doi.org/10.1063/1.5041711

  • Relkin, E., de Ruiter, L. E., & Bers, M. U. (2021). Learning to code and the acquisition of computational thinking by young children. Computers and Education, 169, 104222. https://doi.org/10.1016/j.compedu.2021.104222

    Article  Google Scholar 

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

    Article  Google Scholar 

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

  • Rich, P. J., Leatham, K. R., & Wright, G. A. (2013). Convergent cognition. Instructional Science, 41(2), 431–453. https://doi.org/10.1007/s11251-012-9240-7

    Article  Google Scholar 

  • Rich, K. M., Yadav, A., & Schwarz, C. V. (2019). Computational thinking, Mathematics, and Science: Elementary teachers’ perspectives on integration. Journal of Technology and Teacher Education, 27(2), 165–205

    Google Scholar 

  • Rodríguez-Martínez, J. A., González-Calero, J. A., & Sáez-López, J. M. (2020). Computational thinking and mathematics using Scratch: an experiment with sixth-grade students. Interactive Learning Environments, 28(3), 316–327. https://doi.org/10.1080/10494820.2019.1612448

    Article  Google Scholar 

  • Schanzer, E. T. (2015). Algebraic functions, computer programming, and the challenge of transfer (Doctoral dissertation). Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:16461037

  • 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. Hodges (Eds.), Computational Thinking: Research and Practice (pp. 221–238). Springer. https://doi.org/10.1007/978-3-319-52691-1_14

  • Sáez-López, J. M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers & Education, 97, 129–141. https://doi.org/10.1016/j.compedu.2016.03.003

    Article  Google Scholar 

  • Saritepeci, M. (2020). Developing Computational Thinking Skills of High School Students: Design-Based Learning Activities and Programming Tasks. The Asia-Pacific Education Researcher, 29(1), 35–54. https://doi.org/10.1007/s40299-019-00480-2

    Article  Google Scholar 

  • Scherer, R., Siddiq, F., & Sánchez Viveros, B. (2020). A meta-analysis of teaching and learning computer programming: Effective instructional approaches and conditions. Computers in Human Behavior, 109, 1–18. https://doi.org/10.1016/j.chb.2020.106349

    Article  Google Scholar 

  • Seiter, L. (2015). Using solo to classify the programming responses of primary grade students. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 540–545). New York, NY, USA: ACM. https://doi.org/10.1145/2676723.2677244

  • Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003

    Article  Google Scholar 

  • Soboleva, E. V., Sabirova, E. G., Babieva, N. S., Sergeeva, M. G., & Torkunova, J. V. (2021). Formation of computational thinking skills using computer games in teaching mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 17(10), Article em2012. https://doi.org/10.29333/ejmste/11177

  • Staples, A., Pugach, M. C., & Himes, D. J. (2005). Rethinking the technology integration challenge: Cases from three urban elementary schools. Journal of Research on Technology in Education, 37(3), 285–311. https://doi.org/10.1080/15391523.2005.10782438

    Article  Google Scholar 

  • Strawhacker, A., & Bers, M. A. (2019). What they learn when they learn coding: Investigating cognitive domains and computer programming knowledge in young children. Educational Technology Research and Development, 67, 541–575. https://doi.org/10.1007/s11423-018-9622-x

    Article  Google Scholar 

  • Subhi, T. (1999). The impact of LOGO on gifted children’s achievement and creativity. Journal of Computer Assisted Learning, 15(2), 98–108. https://doi.org/10.1046/j.1365-2729.1999.152082.x

    Article  Google Scholar 

  • Tran, Y. (2019). Computational thinking equity in elementary classrooms: What third-grade students know and can do. Journal of Educational Computing Research, 57(1), 3–31. https://doi.org/10.1177/0735633117743918

    Article  Google Scholar 

  • Vasconcelos, L., & Kim, C. (2020). Coding in scientific modeling lessons (CS-Model). Educational Technology Research and Development, 68, 1247–1273. https://doi.org/10.1007/s11423-019-09724-w

    Article  Google Scholar 

  • Weintrop, D., & Wilensky, U. (2017). Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education, 18(1), 1–25. https://doi.org/10.1145/3089799

    Article  Google Scholar 

  • Wiedermann, W., & von Eye, A. (2013). Robustness and power of the parametric t test and the nonparametric Wilcoxon test under non-independence of observations. Psychological Test and Assessment Modeling, 55(1), 39–61

    Google Scholar 

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

    Article  Google Scholar 

  • Wright, G., Rich, P., & Lee, R. (2013). The influence of teaching programming on learning mathematics. Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 4612–4615). New Orleans, Louisiana, United States: Association for the Advancement of Computing in Education. https://www.learntechlib.org/primary/p/48851/

Download references

Funding

This study was not funded by any agency.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tian Luo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Compliance with Ethical Standards

This research project has received IRB approval from Old Dominion University.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, T., Reynolds, J. & Muljana, P.S. Elementary Students Learning Computer Programming: an investigation of their knowledge Retention, Motivation, and perceptions. Education Tech Research Dev 70, 783–806 (2022). https://doi.org/10.1007/s11423-022-10112-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11423-022-10112-0

Keywords

Navigation