Most studies suggest that students develop computational thinking (CT) through learning programming. However, when the target of CT is decoupled from programming, emerging evidence challenges the assertion of CT transferability from programming. In this study, CT was operationalized in everyday problem-solving contexts in a learning experiment (n = 59) that investigated whether learning programming enhances students’ CT skills. Specifically, this study examined the influence of a novel, systematic and micro instructional strategy that is grounded in abstraction and comprised of four independent but related processes – discover, extract, create, and assemble (DECA) towards simplification of problem-solving. Subsidiary questions explored the effects of students’ age, gender, computer proficiency, and prior programming experience on the development of CT. No significant difference was found between the CT skill and programming knowledge of the groups at the posttest. However, within-group paired t-tests showed that the experimental group that integrated DECA had significant improvement in CT but not in the control group across the pretest-posttest axis. Implications of the inconclusive finding about the transfer of programming skills to CT are emphasized and the arguments for disentangling CT from programming are highlighted.
AkcaogluM. (2014). Learning problem-solving through making games at the game design and learning summer program. Educational Technology Research and Development, 62(5), 583–600. https://doi.org/10.1007/s11423-014-9347-4
2.
BaekY.WangS.YangD.ChingY.SwansonS.ChittooriB. (2019). Revisiting second graders' robotics with an understand/use-modify-create (U2MC) strategy. European Journal of STEM Education, 4(1), Article 07. https://doi.org/10.20897/ejsteme/5772
BarrV.StephensonC. (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. https://doi.org/10.1145/1929887.1929905
5.
BersM. U.FlanneryL.KazakoffE. R.SullivanA. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72(C), 145–157. https://doi.org/10.1016/j.compedu.2013.10.020
6.
BrennanK.ResnickM. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 Annual Meeting of the American Educational Research Association, 1, 1–25. http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf
7.
ChenG.ShenJ.Barth-CohenL.JiangS.HuangX.EltoukhyM. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175. https://doi.org/10.1016/j.compedu.2017.03.001
CsizmadiaA.CurzonP.DorlingM.HumphreysS.NgT.SelbyC. (2015). Computational thinking: A guide for teachers. https://eprints.soton.ac.uk/424545/
10.
DagienėV.SentanceS. (2016). It’s computational thinking! Bebras tasks in the curriculum. In BrodnikA.TortF. (Eds.), Informatics in schools: Improvement of informatics knowledge and perception (pp. 28–39). Springer. https://doi.org/10.1007/978-3-319-46747-4_3
11.
DennerJ.CampeS.WernerL. (2019). Does computer game design and programming benefit children? A meta-synthesis of research. ACM Transactions on Computing Education, 19(3), Article 19. https://doi.org/10.1145/3277565
12.
DenningP. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33–39. https://doi.org/10.1145/2998438
13.
DenningP. J.TedreM.YongpraditP. (2017). Misconceptions about computer science. Communications of the ACM, 60(3), 31–33. https://doi.org/10.1145/3041047
14.
EzeamuzieN. O. (2022). Project-first approach to programming in K–12: Tracking the development of novice programmers in technology-deprived environments. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11180-8
15.
EzeamuzieN. O.LeungJ. S. C. (2022). Computational thinking through an empirical lens: A systematic review of literature. Journal of Educational Computing Research, 60(2), 481–511. https://doi.org/10.1177/07356331211033158
16.
EzeamuzieN. O.LeungJ. S. C.GarciaR.TingF. S. T. (2022a). Discovering computational thinking in everyday problem solving: A multiple case study of route planning. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12720
17.
EzeamuzieN. O.LeungJ. S. C.TingF. S. T. (2022b). Unleashing the potential of abstraction from cloud of computational thinking: A systematic review of literature. Journal of Educational Computing Research, 60(4), 877–905. https://doi.org/10.1177/07356331211055379
18.
GroverS.JackiwN.LundhP. (2019). Concepts before coding: Non-programming interactives to advance learning of introductory programming concepts in middle school. Computer Science Education, 29(2–3), 106–135. https://doi.org/10.1080/08993408.2019.1568955
GuzdialM. (2015). Learner-centered design of computing education: Research on computing for everyone. Synthesis Lectures on Human-Centered Informatics, 8(6), 1–165. https://doi.org/10.2200/S00684ED1V01Y201511HCI033
JonassenD. H. (1997). Instructional design models for well-structured and III-structured problem-solving learning outcomes. Educational Technology, Research and Development, 45(1), 65–94. https://doi.org/10.1007/BF02299613
23.
JonassenD. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85. https://doi.org/10.1007/bf02300500
KorkmazÖ.ÇakirR.ÖzdenM. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, 558–569. https://doi.org/10.1016/j.chb.2017.01.005
KyzaE. A.GeorgiouY.AgesilaouA.SouropetsisM. (2022). A cross-sectional study investigating primary school children’s coding practices and computational thinking using ScratchJr. Journal of Educational Computing Research, 60(1), 220–257. https://doi.org/10.1177/07356331211027387
28.
LauW. W. F.YuenA. H. K. (2009). Exploring the effects of gender and learning styles on computer programming performance: Implications for programming pedagogy. British Journal of Educational Technology, 40(4), 696–712. https://doi.org/10.1111/j.1467-8535.2008.00847.x
29.
LauW. W. F.YuenA. H. K. (2011). The impact of the medium of instruction: The case of teaching and learning of computer programming. Education and Information Technologies, 16(2), 183–201. https://doi.org/10.1007/s10639-009-9118-8
30.
LeeM.LeeJ. (2021). Enhancing computational thinking skills in informatics in secondary education: The case of South Korea. Educational Technology Research and Development, 69(5), 2869–2893. https://doi.org/10.1007/s11423-021-10035-2
31.
LiaoY.-K. (2000). A meta-analysis of computer programming on cognitive outcomes: An updated synthesis. In BourdeauJ.HellerR. (Eds.), Proceedings of ED-MEDIA 2000--world conference on educational multimedia, hypermedia & telecommunications (pp. 598–604). Association for the Advancement of Computing in Education. https://www.learntechlib.org/p/16132
32.
LiaoY.-K. C.BrightG. W. (1991). Effects of computer programming on cognitive outcomes: A meta-analysis. Journal of Educational Computing Research, 7(3), 251–268. https://doi.org/10.2190/e53g-hh8k-ajrr-k69m
33.
LyeS. Y.KohJ. 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
34.
MannilaL.DagieneV.DemoB.GrgurinaN.MiroloC.RolandssonL.SettleA. (2014). Computational thinking in K-9 education. In ClearA.ListerR. (Eds.), Proceedings of the working group reports of the 2014 on innovation & technology in computer science education conference (pp. 1–29). ACM. https://doi.org/10.1145/2713609.2713610
35.
MayerR. E. (1998). Cognitive, metacognitive, and motivational aspects of problem solving. Instructional Science, 26(1–2), 49–63. https://doi.org/10.1023/A:1003088013286
36.
MerkourisA.ChorianopoulosK.KameasA. (2017). Teaching programming in secondary education through embodied computing platforms: Robotics and wearables. ACM Transactions on Computing Education, 17(2), Article 9. https://doi.org/10.1145/3025013
37.
Moreno-LeónJ.RoblesG.Román-GonzálezM. (2015). Dr. Scratch: Automatic analysis of scratch projects to assess and foster computational thinking. Revista de Educación a Distancia, 46(10), 1–23. https://doi.org/10.6018/red/46/10
38.
NardelliE. (2019). Do we really need computational thinking?Communications of the ACM, 62(2), 32–35. https://doi.org/10.1145/3231587
39.
NohJ.LeeJ. (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
40.
Organisation for Economic Co-operation and Development. (2013a). PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy. OECD Publishing. https://doi.org/10.1787/9789264190511-6-en
41.
Organisation for Economic Co-operation and Development. (2013b). PISA 2012 mathematics framework. In PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy (pp. 23–58). OECD Publishing. https://doi.org/10.1787/9789264190511-6-en
Organization for Economic Co-operation and Development. (2016). PISA 2015 results (volume i): Excellence and equity in education. OECD Publishing. https://doi.org/10.1787/9789264266490-en
44.
PapertS. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.
Pérez-MarínD.Hijón-NeiraR.BaceloA.PizarroC. (2020). Can computational thinking be improved by using a methodology based on metaphors and scratch to teach computer programming to children?Computers in Human Behavior, 105, 105849. https://doi.org/10.1016/j.chb.2018.12.027
Román-GonzálezM.Pérez-GonzálezJ.-C.Jiménez-FernándezC. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the computational thinking test. Computers in Human Behavior, 72, 678–691. https://doi.org/10.1016/j.chb.2016.08.047
49.
SaritepeciM. (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
50.
SchererR.SiddiqF.ViverosB. S. (2019). The cognitive benefits of learning computer programming: A meta-analysis of transfer effects. Journal of Educational Psychology, 111(5), 764–792. https://doi.org/10.1037/edu0000314
ShenJ.ChenG.Barth-CohenL.JiangS.EltoukhyM. (2020). Connecting computational thinking in everyday reasoning and programming for elementary school students. Journal of Research on Technology in Education, 1-21. https://doi.org/10.1080/15391523.2020.1834474
53.
ShroutP. E.FleissJ. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428. https://doi.org/10.1037/0033-2909.86.2.420
SullivanA.BersM. U. (2019). Investigating the use of robotics to increase girls’ interest in engineering during early elementary school. International Journal of Technology and Design Education, 29(5), 1033–1051. https://doi.org/10.1007/s10798-018-9483-y
TedreM.DenningP. J. (2016). The long quest for computational thinking. In SheardJ.MonteroC. S. (Eds.), Proceedings of the 16th koli calling international conference on computing education research (pp. 120–129). ACM. https://doi.org/10.1145/2999541.2999542
58.
WeiX.LinL.MengN.TanW.KongS.-C.Kinshuk. (2021). The effectiveness of partial pair programming on elementary school students’ Computational Thinking skills and self-efficacy. Computers & Education, 160, Article 104023. https://doi.org/10.1016/j.compedu.2020.104023
59.
WeintropD.BeheshtiE.HornM.OrtonK.JonaK.TrouilleL.WilenskyU. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5
WitherspoonE.HigashiR.SchunnC.BaehrE.ShoopR. (2017). Developing computational thinking through a virtual robotics programming curriculum. ACM Transactions on Computing Education, 18(1), Article 4. https://doi.org/10.1145/3104982
62.
ZhaoL.LiuX.WangC.SuY.-S. (2022). Effect of different mind mapping approaches on primary school students’ computational thinking skills during visual programming learning. Computers & Education, 181, Article 104445. https://doi.org/10.1016/j.compedu.2022.104445
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
