Do 3–8 Years Old Children Benefit From Computational Thinking Development? A Meta-Analysis

Abstract

The interest in Computational Thinking (CT) development among young learners increases with the number of studies located in literature. In this study, a meta-analysis was conducted to address two main objectives: (a) the effectiveness of empirical interventions on the development of CT in children aged of 3–8 years; and (b) the variables that influence the effectiveness of the interventions. Following PRISMA procedures, we identified 17 empirical studies with 34 effect sizes and 1665 participants meeting the inclusion criteria from Web of Science database. Overall, we found a statistically significant large effect size (d = .83 [95% CI: 730, .890]; p < .001) on the CT development of 3–8 years old children, which provides empirical support for having young children to engage in CT experiences. The effect size was significantly influenced by moderating variables including gender, scaffolding, and education level. Intervention length showed a marginally significant effect. Therefore, educators could refer to the significant moderators when designing tailored interventions for CT development in early childhood education while a call for more empirical studies of CT development in young children is proposed.

Keywords

computational thinking 3–8 years old meta-analysis large effect size

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References

Aksit

Wiebe

E. N.

(2020). Exploring force and motion concepts in middle grades using computational modeling: A classroom intervention study. Journal of Science Education and Technology, 29(1), 65–82. https://doi.org/10.1007/s10956-019-09800-z

Aldemir

Kermani

(2017). Integrated STEM curriculum: Improving educational outcomes for head start children. Early Child Development and Care, 187(11), 1694–1706. https://doi.org/10.1080/03004430.2016.1185102

Angeli

Georgiou

(2023). Investigating the effects of gender and scaffolding in developing preschool children’s computational thinking during problem-solving with Bee-Bots. Frontiers in Education, 7, 757627. https://doi.org/10.3389/feduc.2022.757627

Angeli

Valanides

(2020). Developing young children’s computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, 105954. https://doi.org/10.1016/j.chb.2019.03.018

Arfé

Vardanega

Montuori

Lavanga

(2019). Coding in primary grades boosts children’s executive functions. Frontiers in Psychology, 10, 2713. https://doi.org/10.3389/fpsyg.2019.02713

Bati

(2022). A systematic literature review regarding computational thinking and programming in early childhood education. Education and Information Technologies, 27(2), 2059–2082. https://doi.org/10.1007/s10639-021-10700-2

Bers

(Ed.), (2021). Teaching computational thinking and coding to young children. IGI Global. https://doi.org/10.4018/978-1-7998-7308-2

Belland

B. R.

(2014). Scaffolding: Definition, current debates, and future directions. In Spector

J. M.

Merrill

M. D.

Elen

Bishop

M. J.

(Eds.), Handbook of research on educational communications and technology (4th ed., pp. 505–518). Springer.

Bers

M. U.

(2010). The TangibleK robotics program: Applied computational thinking for young children. Early Childhood Research & Practice, 12(2), 1–20. https://ecrp.uiuc.edu/v12n2/bers.html

10.

Bers

M. U.

(2019). Coding as another language: A pedagogical approach for teaching computer science in early childhood. Journal of Computers in Education, 6(4), 499–528. https://doi.org/10.1007/s40692-019-00147-3

11.

Bers

M. U.

González-González

Armas–Torres

M. B.

(2019). Coding as a playground: Promoting positive learning experiences in childhood classrooms. Computers & Education, 138, 130–145. https://doi.org/10.1016/j.compedu.2019.04.013

12.

Bers

M. U.

Strawhacker

Sullivan

(2022). The state of the field of computational thinking in early childhood education. In OECD education working papers. OECD Publishing. No. 274 https://doi.org/10.1787/3354387a-en

13.

Brennan

Resnick

(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, p. 25). Retrieved from https://scratched.gse.harvard.edu/ct/files/AERA2012.pdf

14.

Canbeldek

Isikoglu

(2023). Exploring the effects of “productive children: Coding and robotics education program” in early childhood education. Education and Information Technologies, 28(3), 3359–3379. https://doi.org/10.1007/s10639-022-11315-x

15.

Chan

K. K.

(2021). Fostering future teachers' competence in computational thinking in an educational technology course. In Handbook of research on using educational robotics to facilitate student learning (pp. 257–273). IGI Global. https://doi.org/10.4018/978-1-7998-6717-3.ch010

16.

Cheng

Wang

Ritzhaupt

A. D.

(2023). The effects of computational thinking integration in STEM on students’ learning performance in K-12 education: A meta-analysis. Journal of Educational Computing Research, 61(2), 416–443. https://doi.org/10.1177/07356331221114183

17.

Chiazzese

Arrigo

Chifari

Lonati

Tosto

(2019). Educational robotics in primary school: Measuring the development of computational thinking skills with the bebras tasks. In Informatics. (6, p. 43). MDPI. https://doi.org/10.3390/informatics6040043

18.

Chou

P. N.

(2020). Using ScratchJr to foster young children’s computational thinking competence: A case study in a third-grade computer class. Journal of Educational Computing Research, 58(3), 570–595. https://doi.org/10.1177/0735633119872908

19.

Cooper

(2017). Research synthesis and meta-analysis: A step-by-step approach (5th ed.). Sage Publications. https://doi.org/10.4135/9781071878644

20.

De Santo

Farah

J. C.

Martínez

M. L.

Moro

Bergram

Purohit

A. K.

Holzer

(2022). Promoting computational thinking skills in Non-computer-science students: Gamifying computational Notebooks to increase student engagement. IEEE Transactions on Learning Technologies, 15(3), 392-405. https://doi.org/10.1109/TLT.2022.3180588

21.

del Olmo-Muñoz

Cózar-Gutiérrez

González-Calero

J. A.

(2020). computational thinking through unplugged activities in early years of primary education. Computers & Education, 150, 103832. https://doi.org/10.1016/j.compedu.2020.103832

22.

Duval

Tweedie

(2000). Trim and fill: A simple funnel plot–based method of testing and adjusting for publication bias in meta analysis. Biometrics, 56(2), 455–463. https://doi.org/10.1111/j.0006-341X.2000.00455.x

23.

Egger

Smith

G. D.

Schneider

Minder

(1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634. https://doi.org/10.1136/bmj.315.7109.629

24.

Fantuzzo

J. W.

Gadsden

V. L.

McDermott

P. A.

(2011). An integrated curriculum to improve mathematics, language, and literacy for Head Start children. American Educational Research Journal, 48(3), 763–793. https://doi.org/10.3102/0002831210385446

25.

Fessakis

Gouli

Mavroudi

(2013). Problem solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education, 63, 87–97. https://doi.org/10.1016/j.compedu.2012.11.016

26.

Gerosa

Koleszar

Tejera

Gómez-Sena

Carboni

(2022). Educational robotics intervention to foster computational thinking in preschoolers: Effects of children’s task engagement. Frontiers in Psychology, 13, 904761. https://doi.org/10.3389/fpsyg.2022.904761

27.

Grover

Pea

(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

28.

Hedges

L.V.

Olkin

(1985). Statistical methods for meta-analysis. Academic Press.

29.

Higgins

J. P.

Thompson

S. G.

Deeks

J. J.

Altman

D. G.

(2003). Measuring inconsistency in meta-analyses. BMJ, 327(7414), 557–560. https://doi.org/10.1136/bmj.327.7414.557

30.

Hong

(2023). The impact of educational robots on students' computational thinking: A meta-analysis of K-12 (pp. 1–26). Education and Information Technologies. https://doi.org/10.1007/s10639-023-12415-y

31.

Kazakoff

E. R.

Bers

M. U.

(2014). Put your robot in, put your robot out: Sequencing through programming robots in early childhood. Journal of Educational Computing Research, 50(4), 553–573. https://doi.org/10.2190/EC.50.4

32.

Kazakoff

E. R.

Sullivan

Bers

M. U.

(2013). The effect of a classroom-based intensive robotics and programming workshop on sequencing ability in early childhood. Early Childhood Education Journal, 41(4), 245–255. https://doi.org/10.1007/s10643-012-0554-5

33.

Kong

S. C.

Wang

Y. Q.

(2020). Formation of computational identity through computational thinking perspectives development in programming learning: A mediation analysis among primary school students. Computers in Human Behavior, 106, 106230. https://doi.org/10.1016/j.chb.2019.106230

34.

Krause

G. H.

Vanderberg

Hung

E.E.

Skuratowicz

(2023). Computational thinking in a bilingual kindergarten classroom: Emergent ideas for teaching across content areas (pp. 1–16). Education and Information Technologies. https://doi.org/10.1007/s10639-022-11454-1

35.

Kumpulainen

Seppänen

(2022). Combining Web of Science and Scopus datasets in citation-based literature study. Scientometrics, 127(10), 5613–5631. https://doi.org/10.1007/s11192-02204475-7

36.

Kwon

Jeon

Zhou

Kim

Brush

T. A.

(2022). Embodied learning for computational thinking in early primary education. Journal of Research on Technology in Education. https://doi.org/10.1080/15391523.2022.2158146

37.

Lai

Wong

G. K. W.

(2022). Collaborative versus individual problem solving in computational thinking through programming: A meta analysis. British Journal of Educational Technology, 53(1), 150–170. https://doi.org/10.1111/bjet.13157

38.

Lavigne

H. J.

Lewis-Presser

Rosenfeld

(2020). An exploratory approach for investigating the integration of computational thinking and mathematics for preschool children. Journal of Digital Learning in Teacher Education, 36(1), 63–77. https://doi.org/10.1080/21532974.2019.1693940

39.

Lavigne

H. J.

Presser

A. L.

Rosenfeld

Cuellar

Vidiksis

Ferguson

Andrews

(2023). Computational thinking with families: Studying an at-home media intervention to promote joint media engagement between preschoolers and their parents. Early Childhood Research Quarterly, 65, 102–114. https://doi.org/10.1016/j.ecresq.2023.05.009

40.

Lee

Junoh

(2019). Implementing unplugged coding activities in early childhood classrooms. Early Childhood Education Journal, 47(6), 709–716. https://doi.org/10.1007/s10643-019-00967-z

41.

Lei

Chiu

M. M.

Wang

Geng

(2020). Computational thinking and academic achievement: A meta-analysis among students. Children and Youth Services Review, 118, 105439. https://doi.org/10.1016/j.childyouth.2020.105439

42.

Rollins

Yan

(2018). Web of science use in published research and review papers 1997-2017: A selective, dynamic, cross-domain, content-based analysis. Scientometrics, 115(1), 1–20. https://doi.org/10.1007/s11192-017-2622-5

43.

Lin

Chu

Hodges

J. S.

(2017). Alternative measures of between study heterogeneity in meta analysis: Reducing the impact of outlying studies. Biometrics, 73(1), 156–166. https://doi.org/10.1111/biom.12543

44.

Lin

S. Y.

Chien

S. Y.

Hsiao

C. L.

Hsia

C. H.

Chao

K. M.

(2020). Enhancing computational thinking capability of preschool children by game-based smart toys. Electronic Commerce Research and Applications, 44, 101011. https://doi.org/10.1016/j.elerap.2020.101011

45.

Lipsey

M. W.

Wilson

D. B.

(2001). Practical meta-analysis. Sage.

46.

Chiu

M. M.

Cui

Mao

Lei

(2023). Effects of game-based learning on students’ computational thinking: A meta-analysis. Journal of Educational Computing Research, 61(1), 235–256. https://doi.org/10.1177/07356331221100740

47.

McCormick

K. I.

Hall

J. A.

(2022). Computational thinking learning experiences, outcomes, and research in preschool settings: A scoping review of literature (pp. 1–36). Education and Information Technologies. https://doi.org/10.1007/s10639-021-10765-z

48.

Melsen

W. G.

Boostma

M. C. J.

Rovers

M. M.

Bonten

M. J. M.

(2013). The effects of clinical and statistical heterogeneity on the predictive values of result from meta-analysis. Clinical Microbiology and Infection, 20(2), 123–129. https://doi.org/10.1111/1469-0691.12494

49.

Merino-Armero

J. M.

González-Calero

J. A.

Cozar-Gutierrez

(2022). Computational thinking in K-12 education. An insight through meta-analysis. Journal of Research on Technology in Education, 54(3), 410–437. https://doi.org/10.1080/15391523.2020.1870250

50.

Montuori

Gambarota

Altoé

Arfé

(2024). The cognitive effects of computational thinking: A systematic review and meta-analytic study. Computers and Education. https://doi.org/10.1016/j.compedu.2023.104961

51.

Montuori

Ronconi

Vardanega

Arfé

(2022). Exploring gender differences in coding at the beginning of primary school. Frontiers in Psychology, 13, 887280. https://doi.org/10.3389/fpsyg.2022.887280

52.

Mouza

Yadav

Ottenbreit-Leftwich

(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. Retrieved from https://www.learntechlib.org/primary/p/184602/

53.

Nam

K. W.

Kim

H. J.

Lee

(2019). Connecting plans to action: The effects of a card-coded robotics curriculum and activities on Korean kindergartners. The Asia-Pacific Education Researcher, 28(5), 387–397. https://doi.org/10.1007/s40299-019-00438-4

54.

National Research Council . (2010). Report of a workshop on the scope and nature of computational thinking. National Academies Press.

55.

Nouri

Zhang

Mannila

Norén

(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

56.

Page

M. J.

McKenzie

J. E.

Bossuyt

P. M.

Boutron

Hoffmann

T. C.

Mulrow

C. D.

Shamseer

Tetzlaff

J. M.

Akl

E. A.

Brennan

S. E.

Chou

Glanville

Grimshaw

J. M.

Hróbjartsson

Lalu

M. M.

Loder

E. W.

Mayo-Wilson

McDonald

Moher

(2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 10(1), 1–11. https://doi.org/10.1136/bmj.n71

57.

Papadakis

Kalogiannakis

Zaranis

(2016). Developing fundamental programming concepts and computational thinking with ScratchJr in preschool education: A case study. International Journal of Mobile Learning and Organisation, 10(3), 187–202. https://doi.org/10.1504/IJMLO.2016.077867

58.

Pritschet

Powell

Horne

(2016). Marginally significant effects as evidence for hypotheses: Changing attitudes over four decades. Psychological Science, 27(7), 1036–1042. https://doi.org/10.1177/0956797616645672

59.

R Core Team . (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/

60.

Relkin

de Ruiter

L. E.

Bers

M. U.

(2021). Learning to code and the acquisition of computational thinking by young children. Computers in Education, 169(9), 104222. https://doi.org/10.1016/j.compedu.2021.104222

61.

Rothstein

H. R.

Sutton

A. J.

Borenstein

(Eds.), (2005). Publication bias in meta-analysis Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments. John Wiley and Sons, Ltd. https://doi.org/10.1002/0470870168

62.

Sengupta

Kinnebrew

J. S.

Basu

Biswas

Clark

(2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380. https://doi.org/10.1007/s10639-012-9240-x

63.

Sohr

E. R.

Gupta

Elby

Radoff

(2023). How a child entangles empathy and computational thinking in reasoning about fairness. Early Childhood Research Quarterly, 65, 92–101. https://doi.org/10.1016/j.ecresq.2023.05.010.

64.

Stamatios

(2022). Can preschoolers learn computational thinking and coding skills with ScratchJr? A systematic literature review. International Journal of Educational Reform, 33(1), 28–61. https://doi.org/10.1177/10567879221076077

65.

Strawhacker

Bers

M. U.

(2018). Promoting positive technological development in a kindergarten makerspace: A qualitative case study. European Journal of STEM Education, 3(3), 9. https://doi.org/10.20897/ejsteme/3869

66.

Sullivan

Bers

M. U.

(2013). Gender differences in kindergarteners’ robotics and programming achievement. International Journal of Technology and Design Education, 23(3), 691–702. https://doi.org/10.1007/s10798-012-9210-z

67.

Sullivan

Bers

M. U.

(2016). Robotics in the early childhood classroom: Learning outcomes from an 8-week robotics curriculum in pre-kindergarten through second grade. International Journal of Technology and Design Education, 26, 3–20. https://doi.org/10.1007/s10798-015-9304-5

68.

Sun

Guo

(2023). Educational games promote the development of students’ computational thinking: A meta-analytic review. Interactive Learning Environments, 31(6), 3476–3490. https://doi.org/10.1080/10494820.2021.1931891

69.

Sun

Zhou

(2021). Which way of design programming activities is more effective to promote K 12 students' computational thinking skills? A meta analysis. Journal of Computer Assisted Learning, 37(4), 1048–1062. https://doi.org/10.1111/jcal.12545

70.

Sung

Black

J. B.

(2020). Factors to consider when designing effective learning: Infusing computational thinking in mathematics to support thinking-doing. Journal of Research on Technology in Education, 53(4), 404–426. https://doi.org/10.1080/15391523.2020.1784066

71.

Van den Noortgate

López-López

J. A.

Marín-Martínez

Sánchez-Meca

(2013). Three-level meta-analysis of dependent effect sizes. Behavior Research Methods, 45(2), 576–594. https://doi.org/10.3758/s13428-012-0261-6

72.

Viechtbauer

(2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03

73.

Viechtbauer

Cheung

M. W. L.

(2010). Outlier and influence diagnostics for meta analysis. Research Synthesis Methods, 1(2), 112–125. https://doi.org/10.1002/jrsm.11

74.

Wang

Shi

Geng

Hao

Chanjuan

(2022). Effects of cognitive control strategies on coding learning outcomes in early childhood. The Journal of Educational Research, 115(2), 133–145. https://doi.org/10.1080/00220671.2022.2074946

75.

Wang

X. C.

Choi

Benson

Eggleston

Weber

(2021). Teacher’s role in fostering preschoolers’ computational thinking: An exploratory case study. Early Education & Development, 32(1), 26–48. https://doi.org/10.1080/10409289.2020.1759012

76.

Wing

(2011). Research notebook: Computational thinking—what and why. The link magazine, 6, 20–23.

77.

Wing

J. M.

(2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215

78.

Wang

(2022). A meta-analysis of the effectiveness of programming teaching in promoting K-12 students’ computational thinking. Education and Information Technologies, 28(6), 1–26. https://doi.org/10.1007/s10639-022-11445-2

79.

Yadav

Gretter

Hambrusch

Sands

(2016a). Expanding computer science education in schools: Understanding teacher experiences and challenges. Computer Science Education, 26(4), 235–254. https://doi.org/10.1080/08993408.2016.1257418

80.

Yadav

Hong

Stephenson

(2016b). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60(6), 565–568. https://doi.org/10.1007/s11528-016-0087-7

81.

Yadav

Chakraborty

(2023). Introducing schoolchildren to computational thinking using smartphone apps: A way to encourage enrollment in engineering education. In Computer applications in engineering education. https://doi.org/10.1002/cae.22609

82.

Yang

Luo

(2022a). Towards inclusiveness and sustainability of robot programming in early childhood: Child engagement, learning outcomes and teacher perception. British Journal of Educational Technology, 53(6), 1486–1510. https://doi.org/10.1111/bjet.13266

83.

Yang

D. T. K.

Gao

(2022b). Robot programming versus block play in early childhood education: Effects on computational thinking, sequencing ability, and self regulation. British Journal of Educational Technology, 53(6), 1817–1841. https://doi.org/10.1111/bjet.13215

84.

Yang

D. T. K.

(2023). The impact of story-inspired programming on preschool children’s computational thinking: A multi-group experiment. Thinking Skills and Creativity, 47, 101218. https://doi.org/10.1016/j.tsc.2022.101218

85.

Zhan

(2022). Effect of unplugged programming teaching aids on children’s computational thinking and classroom interaction: With respect to Piaget’s four stages theory. Journal of Educational Computing Research, 60(5), 1277–1300. https://doi.org/10.1177/07356331211057143