Abstract
Computational thinking is an important competence for learners in the twenty-first century. As an effective approach for cultivating competence in computational thinking, programming education has been extended from college to elementary school teaching. However, it is challenging to engage beginners in programming in elementary school education. In the current study, a contrasting cases approach for learning programming was designed to facilitate learning outcomes of novice programmers in elementary schools. To evaluate the effectiveness of the proposed approach, a quasi-experiment was conducted during a 3-week programming course with 72 elementary school students. The results revealed that the contrasting cases approach for learning programming was effective for improving learning outcomes in terms of learning performance, learning engagement, and cognitive load. Furthermore, suggestions for promoting the learning of programming by novice learners at the elementary school level using the contrasting cases approach were proposed. In addition, prospects for future research and practice were discussed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Materials and data availability
Materials and data designed and/or generated in the study are available from the corresponding author on reasonable request.
References
Alfieri, L., Nokes-Malach, T. J., & Schunn, C. D. (2013). Learning through case comparisons: A meta-analytic review. Educational Psychologist, 48(2), 87–13. https://doi.org/10.1080/00461520.2013.775712
Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, 106185. https://doi.org/10.1016/j.chb.2019.106185
Arslan, K., & Tanel, Z. (2021). Analyzing the effects of Arduino applications on students’ opinions, attitude and self-efficacy in programming class. Education and Information Technologies, 26(1), 1143–1163. https://doi.org/10.1007/s10639-020-10290-5
Artino, A. R. (2012). Academic self-efficacy: From educational theory to instructional practice. Perspectives on Medical Education, 1(2), 76–85. https://doi.org/10.1007/s40037-012-0012-5
Bandura, A. (1978). Self-efficacy: Toward a unifying theory of behavioral change. Advances in Behaviour Research and Therapy, 1(4), 139–161. https://doi.org/10.1016/0146-6402(78)90002-4
Barendregt, H., & Barendsen, E. (2002). Autarkic computations in formal proofs. Journal of Automated Reasoning, 28(3), 321–336. https://doi.org/10.1023/A:1015761529444
Bransford, J. D., & Schwartz, D. L. (1999). Chapter 3: Rethinking transfer: A simple proposal with multiple implications. Review of Research in Education, 24(1), 61–100. https://doi.org/10.3102/0091732X024001061
Chase, C. C., Malkiewich, L., & Kumar, S. A. (2019). Learning to notice science concepts in engineering activities and transfer situations. Science Education, 103(2), 440–471. https://doi.org/10.1002/sce.21496
Cheng, L. C., Li, W., & Tseng, J. C. (2021). Effects of an automated programming assessment system on the learning performances of experienced and novice learners. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2021.2006237
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.
Cui, Z., & Ng, O. L. (2021). The interplay between mathematical and computational thinking in primary school students’ mathematical problem-solving within a programming environment. Journal of Educational Computing Research, 59(5), 988–1012. https://doi.org/10.1177/0735633120979930
Demir, F. (2021). The effect of different usage of the educational programming language in programming education on the programming anxiety and achievement. Education and Information Technologies, 27(3), 4171–4194. https://doi.org/10.1007/s10639-021-10750-6
Demirkiran, M. C., & Tansu Hocanin, F. (2021). An investigation on primary school students’ dispositions towards programming with game-based learning. Education and Information Technologies, 26(4), 3871–3892. https://doi.org/10.1007/s10639-021-10430-5
Dongo, T. A., Reed, A. H., & O’Hara, M. T. (2016). Exploring pair programming benefits for MIS majors. Journal of Information Technology Education: Innovations in Practice, 15, 223–239. Retrieved December 30, 2022, from https://www.informingscience.org/Publications/3625
Dunleavy, S., Kestin, G., Callaghan, K., McCarty, L., & Deslauriers, L. (2022). Increased learning in a college physics course with timely use of short multimedia summaries. Physical Review Physics Education Research, 18(1), 010110. https://doi.org/10.1103/PhysRevPhysEducRes.18.010110
Erol, O., & Çırak, N. S. (2022). The effect of a programming tool scratch on the problem-solving skills of middle school students. Education and Information Technologies, 27(3), 4065–4086. https://doi.org/10.1007/s10639-021-10776-w
Es, N., & Jeuring, J. (2017). Designing and comparing two Scratch-based teaching approaches for students aged 10–12 years—extended version. Technical Report Series, (UU-CS-2017–015).
Ezeamuzie, N. O. (2022). Project-first approach to programming in K–12: Tracking the development of novice programmers in technology-deprived environments. Education and Information Technologies, 1–31. https://doi.org/10.1007/s10639-022-11180-8
Fesakis, G., & Serafeim, K. (2009). Influence of the familiarization with “scratch” on future teachers’ opinions and attitudes about programming and ICT in education. Acm SIGCSE Bulletin, 41(3), 258–262. https://doi.org/10.1145/1595496.1562957
Fessakis, G., Gouli, E., & Mavrodi, E. (2013). Problem solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers and Education, 63, 87–97. https://doi.org/10.1016/j.compedu.2012.11.016
Feurzeig, W., Papert, S. A. & Lawler, B. (2011) Programming-languages as a conceptual framework for teaching mathematics. Interactive Learning Environments, 19(5), 487–501. https://doi.org/10.1080/10494820903520040
Flores, R. M., & Rodrigo, M. M. T. (2020). Wheel-spinning models in a novice programming context. Journal of Educational Computing Research, 58(6), 1101–1120. https://doi.org/10.1177/0735633120906063
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
Gentner, D., Levine, S. C., Ping, R., Isaia, A., Dhillon, S., Bradley, C., & Honke, G. (2016). Rapid learning in a children’s museum via analogical comparison. Cognitive Science, 40(1), 224–240. https://doi.org/10.1111/cogs.12248
Gibson, J. J., & Gibson, E. J. (1955). Perceptual learning: Differentiation or enrichment? Psychological Review, 62(1), 32–41. https://doi.org/10.1037/h0048826
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
Heintz, F., Mannila, L., & Färnqvist, T. (2016). A review of models for introducing computational thinking, computer science and computing in K-12 education. IEEE Frontiers in Education Conference (FIE), 2016, 1–9. https://doi.org/10.1109/FIE.2016.7757410
Hwang, G. J., Yang, L. H., & Wang, S. Y. (2013). A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Computers & Education, 69, 121–130. https://doi.org/10.1016/j.compedu.2013.07.008
Kalkstein, D. A., Hubbard, A. D., & Trope, Y. (2018). Beyond direct reference: Comparing the present to the past promotes abstract processing. Journal of Experimental Psychology: General, 147(6), 933–938. https://doi.org/10.1037/xge0000448
Katchapakirin, K., Anutariya, C., & Supnithi, T. (2022). ScratchThAI: A conversation-based learning support framework for computational thinking development. Education and Information Technologies, 27, 8533–8560. https://doi.org/10.1007/s10639-021-10870-z
Kim, C., Kim, D., Yuan, J., Hill, R., Doshi, P., & Thai, C. (2015). Robotics to promote elementary education pre-service teachers’ STEM engagement, learning, and teaching. Computers & Education, 91, 14–31. https://doi.org/10.1016/j.compedu.2015.08.005
Koupritzioti, D., & Xinogalos, S. (2020). PyDiophantus maze game: Play it to learn mathematics or implement it to learn game programming in Python. Education and Information Technologies, 25(4), 2747–2764. https://doi.org/10.1007/s10639-019-10087-1
Kulas, J. T. (2021). IBM SPSS essentials: managing and analyzing social sciences data (2nd ed.). John Wiley & Sons, Inc.
Kuo, E., & Wieman, C. E. (2016). Toward instructional design principles: Inducing Faraday’s law with contrasting cases. Physical Review Physics Education Research, 12(1), 010128. https://doi.org/10.1103/PhysRevPhysEducRes.12.010128
Li, F., Wang, X., He, X., Cheng, L., & Wang, Y. (2022). The effectiveness of unplugged activities and programming exercises in computational thinking education: A Meta-analysis. Education and Information Technologies, 27, 7993–8013. https://doi.org/10.1007/s10639-022-10915-x
Lin-Siegler, X., Shaenfield, D., & Elder, A. D. (2015). Contrasting case instruction can improve self-assessment of writing. Educational Technology Research and Development, 63(4), 517–537. https://doi.org/10.1007/s11423-015-9390-9
Loibl, K., Tillema, M., Rummel, N., & van Gog, T. (2020). The effect of contrasting cases during problem solving prior to and after instruction. Instructional Science, 48(2), 115–136. https://doi.org/10.1007/s11251-020-09504-7
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
Ma, H., Zhao, M., Wang, H., Wan, X., Cavanaugh, T. W., & Liu, J. (2021). Promoting pupils’ computational thinking skills and self-efficacy: A problem-solving instructional approach. Educational Technology Research and Development, 69(3), 1599–1616. https://doi.org/10.1007/s11423-021-10016-5
Malkiewich, L. J., & Chase, C. C. (2019). What’s your goal? The importance of shaping the goals of engineering tasks to focus learners on the underlying science. Instructional Science, 47(5), 551–588. https://doi.org/10.1007/s11251-019-09493-2
Merkouris, A., Chorianopoulos, K., & Kameas, A. (2017). Teaching programming in secondary education through embodied computing platforms: Robotics and wearables. ACM Transactions on Computing Education (TOCE), 17(2), 1–22. https://doi.org/10.1145/3025013
Mladenović, M., Boljat, I., & Žanko, Ž. (2018). Comparing loops misconceptions in block-based and text-based programming languages at the K-12 level. Education and Information Technologies, 23(4), 1483–1500. https://doi.org/10.1007/s10639-017-9673-3
National Survey of Student Engagement. (2007). Experiences that matter: Enhancing student learning and success—Annual report 2007 (Center for Postsecondary Research). Retrieved December 30, 2022, from https://files.eric.ed.gov/fulltext/ED512620.pdf
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
Nouri, J., Zhang, L., Mannila, L., & Norén, E. (2020). Development of computational thinking, digital competence and 21st century skills when learning programming in K-9. Education Inquiry, 11(1), 1–17. https://doi.org/10.1080/20004508.2019.1627844
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. (2016). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71. https://doi.org/10.1207/S15326985EP3801_8
Piaget, J. (1952). The origins of intelligence in children. (M. Cook, Trans.). W W Norton & Co. https://doi.org/10.1037/11494-000
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Popat, S., & Starkey, L. (2019). Learning to code or coding to learn? a systematic review. Computers & Education, 128, 365–376. https://doi.org/10.1016/j.compedu.2018.10.005
Reynolds, A. J., Temple, J. A., Ou, S. R., Arteaga, I. A., & White, B. A. (2011). School-based early childhood education and age-28 well-being: Effects by timing, dosage, and subgroups. Science, 333(6040), 360–364. https://doi.org/10.1126/science.1203618
Rich, P. J., Browning, S. F., Perkins, M., 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
Rittle-Johnson, B., & Star, J. R. (2007). Does comparing solution methods facilitate conceptual and procedural knowledge? An experimental study on learning to solve equations. Journal of Educational Psychology, 99(3), 561. https://doi.org/10.1037/0022-0663.99.3.561
Rittle-Johnson, B., & Star, J. R. (2009). Compared with what? The effects of different comparisons on conceptual knowledge and procedural flexibility for equation solving. Journal of Educational Psychology, 101(3), 529–544. https://doi.org/10.1037/a0014224
Roelle, J., & Berthold, K. (2015). Effects of comparing contrasting cases on learning from subsequent explanations. Cognition and Instruction, 33(3), 199–225. https://doi.org/10.1080/07370008.2015.1063636
Roelle, J., & Berthold, K. (2016). Effects of comparing contrasting cases and inventing on learning from subsequent instructional explanations. Instructional Science, 44(2), 147–176. https://doi.org/10.1007/s11251-016-9368-y
Salmerón, L., & Llorens, A. (2018). Instruction of digital reading strategies based on eye-movements modeling examples. Journal of Educational Computing Research, 57(2), 343–359. https://doi.org/10.1177/0735633117751605
Schaufeli, W. B., Martinez, I. M., Pinto, A. M., Salanova, M., & Bakker, A. B. (2002). Burnout and engagement in university students: A cross-national study. Journal of Cross-Cultural Psychology, 33(5), 464–481. https://doi.org/10.1177/0022022102033005003
Scherer, R. (2016). Learning from the past–the need for empirical evidence on the transfer effects of computer programming skills. Frontiers in Psychology, 7, 1390. https://doi.org/10.3389/fpsyg.2016.01390
Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and Instruction, 16(4), 475–5223. https://doi.org/10.1207/s1532690xcil604
Schwartz, D. L., Chase, C. C., Oppezzo, M. A., & Chin, D. B. (2011). Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. Journal of Educational Psychology, 103(4), 759–775. https://doi.org/10.1037/a0025140
Sun, L., Hu, L., & Zhou, D. (2021). Improving 7th-graders’ computational thinking skills through unplugged programming activities: A study on the influence of multiple factors. Thinking Skills and Creativity, 42, 100926. https://doi.org/10.1016/j.tsc.2021.100926
Sun, L., Guo, Z., & Zhou, D. (2022). Developing K-12 students’ programming ability: A systematic literature review. Education and Information Technologies, 27, 7059–7097. https://doi.org/10.1007/s10639-022-10891-2
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5
Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/A:1022193728205
Tang, K. Y., Chou, T. L., & Tsai, C. C. (2020). A content analysis of computational thinking research: An international publication trends and research typology. The Asia-Pacific Education Researcher, 29(1), 9–19. https://doi.org/10.1007/s40299-019-00442-8
Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature review. Computers & Education, 162, 104083. https://doi.org/10.1016/j.compedu.2020.104083
Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715–728. https://doi.org/10.1007/s10639-015-9412-6
Wing, J. (2014). Computational thinking benefits society. 40th Anniversary Blog of Social Issues in Computing, 26
Wittwer, J., & Renkl, A. (2008). Why instructional explanations often do not work: A framework for understanding the effectiveness of instructional explanations. Educational Psychologist, 43(1), 49–64. https://doi.org/10.1080/00461520701756420
Yang, Y. F., Lee, C. I., & Chang, C. K. (2016). Learning motivation and retention effects of pair programming in data structures courses. Education for Information, 32(3), 249–267. https://doi.org/10.3233/EFI-160976
Zhong, B., Xia, L., & Su, S. (2022). Effects of programming tools with different degrees of embodiment on learning Boolean operations. Education and Information Technologies, 27, 6211–6231. https://doi.org/10.1007/s10639-021-10884-7
Author information
Authors and Affiliations
Contributions
All authors contributed to this study and all authors approved the final manuscript.
Corresponding author
Ethics declarations
Ethics in publishing
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee.
Ethics approval
All procedures performed in the study involving human participants were in accordance with the World Medical Association Declaration of Helsinki. The research participants agreed to participate in the study and their complete anonymity was ensured.
Informed consent
Informed consent was obtained from all individual participants included in the study. The test and questionnaire were conducted anonymously. Students’ and teachers’ participation was voluntary.
Conflict of interest
There is no potential conflict of interest in this study.
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ma, N., Qian, J., Gong, K. et al. Promoting programming education of novice programmers in elementary schools: A contrasting cases approach for learning programming. Educ Inf Technol 28, 9211–9234 (2023). https://doi.org/10.1007/s10639-022-11565-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-022-11565-9