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Cited by 76Teaching and Learning ProgrammingEducational Robotics and EngineeringChild Development and Digital Technology

The cognitive effects of computational thinking: a systematic review and meta-analytic study

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Montuori, Chiara, Gambarota, Filippo, Altoé, Gianmarco, Arfé, Barbara·2024·Computers & Education
DOI 10.1016/j.compedu.2023.104961
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Abstract

In this paper, we review and meta-analyze the findings of experimental studies published between 2006 and 2022 that examined the effects of coding and programming interventions on children's core and higher order executive functions (response inhibition, working memory, cognitive flexibility, planning and problem solving). The systematic review and meta-analysis aimed to address three research questions: 1) Which executive functions are most impacted by the teaching of CT? 2) Which instructional modality (educational robotics/virtual coding/unplugged coding) is most effective in enhancing executive function skills in learners aged 4–16 years? and 3) Does the cognitive effectiveness of coding vary with children's age? A total of 19 studies with 1523 participants met the selection criterion for the systematic review. The meta-analysis included 11 of those studies. The results reveal beneficial effects of structured virtual and tangible coding (educational robotics) activities for preschoolers and first graders, and significant effects of more unstructured virtual coding activities (e.g., Scratch-based) for older students. A multivariate fixed-effects model meta-analysis shows that the teaching of coding significantly improves problem-solving with the highest effect (dppc2 = 0.89), but also planning (dppc2 = 0.36), and inhibition and working memory with lower effects (dppc2 = 0.17, dppc2 = 0.20).

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  1. Akcaoglu, M., & Koehler, M. J. (2014). Cognitive outcomes from the game-design and learning (GDL) after-school program. Computers & Education, 75, 72-81. https:// doi.org/10.1016/j.compedu.2014.02.003 · DOI
  2. Arfé, B., Vardanega, T., Montuori, C., & Lavanga, M. (2019). Coding in primary grades boosts children's executive functions. Frontiers in Psychology, 10. https://www. frontiersin.org/article/10.3389/fpsyg.2019.02713. · DOI
  3. Arfé, B., Vardanega, T., & Ronconi, L. (2020). The effects of coding on children's planning and inhibition skills. Computers & Education, 148, Article 103807. https:// doi.org/10.1016/j.compedu.2020.103807 · DOI
  4. Barkley, R. A. (2001). The executive functions and self-regulation: An evolutionary neuropsychological perspective. Neuropsychology Review, 11(1), 1-29.
  5. Belur, J., Tompson, L., Thornton, A., & Simon, M. (2021). Interrater reliability in systematic review methodology: Exploring variation in coder decision-making. Sociological Methods & Research, 50(2), 837-865. https://doi.org/10.1177/0049124118799372 · DOI · Paper page
  6. Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145-157. https://doi.org/10.1016/j.compedu.2013.10.020 · DOI · Paper page
  7. Best, J. R., & Miller, P. H. (2010). A developmental perspective on executive function. Child Development, 81(6), 1641-1660. https://doi.org/10.1111/j.1467- 8624.2010.01499.x · DOI · Paper page
  8. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. https://doi.org/10.1002/9780470743386 · DOI · Paper page
  9. Brocki, K. C., & Bohlin, G. (2004). Executive functions in children aged 6 to 13: A dimensional and developmental study. Developmental Neuropsychology, 26(2), 571-593. https://doi.org/10.1207/s15326942dn2602_3 · DOI
  10. Brown, Q., Mongan, W., Kusic, D., Garbarine, E., Fromm, E., & Fontecchio, A. (2008). Computer aided instruction as a vehicle for problem solving: Scratch programming environment in the middle years classroom. In ASEE Annual Conference and Exposition, Conference Proceedings. https://doi.org/10.18260/1-2-3826, 10.18260, 13.319.1 -13.319. · DOI
  11. Cai, Z., & Fan, X. (2020). A comparison of fixed-effects and random-effects models for multivariate meta-analysis using an SEM approach. Multivariate Behavioral Research, 55(6), 839-854. https://doi.org/10.1080/00273171.2019.1689348 · DOI
  12. Çakir, R., Korkmaz, Ö., İdil, Ö., & Ugur Erdogmus ¸, F. (2021). The effect of robotic coding education on preschoolers' problem solving and creative thinking skills. Thinking Skills and Creativity, 40, Article 100812. https://doi.org/10.1016/j.tsc.2021.100812 · DOI
  13. Campbell, M. K., Elbourne, D. R., & Altman, D. G. (2004). CONSORT statement: Extension to cluster randomised trials. BMJ, 328(7441), 702-708. https://doi.org/ 10.1136/bmj.328.7441.702 · DOI · Paper page
  14. Cao, Y., Huang, T., Huang, J., Xie, X., & Wang, Y. (2020). Effects and moderators of computer-based training on children's executive functions: A systematic review and meta-analysis. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.580329 · DOI
  15. Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (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 · DOI
  16. Cheung, M. W.-L. (2013). Multivariate meta-analysis as structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 20(3), 429 454. https:// doi.org/10.1080/10705511.2013.797827 · DOI
  17. Cheung, M. W.-L. (2019). A guide to conducting a meta-analysis with non-independent effect sizes. Neuropsychology Review, 29(4), 387-396. https://doi.org/10.1007/ s11065-019-09415-6 · DOI · Paper page
  18. Çiftci, S., & Bildiren, A. (2020). The effect of coding courses on the cognitive abilities and problem-solving skills of preschool children. Computer Science Education, 30 (1), 3-21. https://doi.org/10.1080/08993408.2019.1696169 · DOI
  19. Çınar, M., & Tüzün, H. (2021). Comparison of object-oriented and robot programming activities: The effects of programming modality on student achievement, abstraction, problem solving, and motivation. Journal of Computer Assisted Learning, 37(2), 370-386. https://doi.org/10.1111/jcal.12495 · DOI
  20. Clements, D. H., & Nastasi, B. K. (1999). Metacognition, learning, and educational computer environments. Information Technology in Childhood Education Annual, 1999(1), 3-36.
  21. Colzato, L. S., van Wouwe, N. C., Lavender, T. J., & Hommel, B. (2006). Intelligence and cognitive flexibility: Fluid intelligence correlates with feature "unbinding" across perception and action. Psychonomic Bulletin & Review, 13(6), 1043-1048. https://doi.org/10.3758/BF03213923 · DOI
  22. Conklin, H. M., Luciana, M., Hooper, C. J., & Yarger, R. S. (2007). Working memory performance in typically developing children and adolescents: Behavioral evidence of protracted frontal lobe development. Developmental Neuropsychology, 31(1), 103-128. https://doi.org/10.1080/87565640709336889 · DOI · Paper page
  23. Crocetti, E. (2016). Systematic reviews with meta-analysis: Why, when, and how? Emerging Adulthood, 4(1), 3-18. https://doi.org/10.1177/2167696815617076 · DOI
  24. Davidson, M. C., Amso, D., Anderson, L. C., & Diamond, A. (2006). Development of cognitive control and executive functions from 4 to 13 years: Evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia, 44(11), 2037-2078. https://doi.org/10.1016/j.neuropsychologia.2006.02.006 · DOI · Paper page
  25. De Luca, C. R., Wood, S. J., Anderson, V., Buchanan, J.-A., Proffitt, T. M., Mahony, K., & Pantelis, C. (2003). Normative data from the cantab. I: Development of executive function over the lifespan. Journal of Clinical and Experimental Neuropsychology, 25(2), 242-254. https://doi.org/10.1076/jcen.25.2.242.13639 · DOI · Paper page
  26. Demir, Ü. (2021). The effect of unplugged coding education for special education students on problem-solving skills. International Journal of Computer Sciences and Engineering Systems, 4(3). https://doi.org/10.21585/ijcses.v4i3.95. Article 3. · DOI
  27. Di Lieto, M. C., Castro, E., Pecini, C., Inguaggiato, E., Cecchi, F., Dario, P., Cioni, G., & Sgandurra, G. (2020a). Improving executive functions at school in children with special needs by educational robotics. Frontiers in Psychology, 10. https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02813. · DOI
  28. Di Lieto, M. C., Pecini, C., Castro, E., Inguaggiato, E., Cecchi, F., Dario, P., Cioni, G., & Sgandurra, G. (2020b). Empowering executive functions in 5-and 6-year-old typically developing children through educational robotics: An RCT study. Frontiers in Psychology, 10. https://www.frontiersin.org/article/10.3389/fpsyg.2019. 03084. · DOI
  29. Diamond, A., & Ling, D. S. (2016). Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Developmental Cognitive Neuroscience, 18, 34-48. https://doi.org/10.1016/j.dcn.2015.11.005 · DOI · Paper page
  30. Egger, M., Smith, G. D., Schneider, M., & Minder, C. (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 · DOI · Paper page
  31. 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 · DOI
  32. Fessakis, G., Gouli, E., & Mavroudi, E. (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 · DOI
  33. Feurzeig, W., & Papert, S. A. (2011). Programming-languages as a conceptual framework for teaching mathematics. Interactive Learning Environments, 19(5), 487-501. https://doi.org/10.1080/10494820903520040 · DOI
  34. Finke, S., Kemény, F., Sommer, M., Krnjic, V., Arendasy, M., Slany, W., & Landerl, K. (2022). Unravelling the numerical and spatial underpinnings of computational thinking: A pre-registered replication study. Computer Science Education, 32(3), 313-334. https://doi.org/10.1080/08993408.2022.2079864 · DOI
  35. Flórez, B. F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation's way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834-860. https://doi.org/10.3102/0034654317710096 · DOI
  36. Frensch, P. A., & Funke, J. (1995). Complex problem solving. The European perspective. Psychology Press.
  37. Furuya-Kanamori, L., Xu, C., Lin, L., Doan, T., Chu, H., Thalib, L., & Doi, S. A. R. (2020). P value-driven methods were underpowered to detect publication bias: Analysis of Cochrane review meta-analyses. Journal of Clinical Epidemiology, 118, 86-92. https://doi.org/10.1016/j.jclinepi.2019.11.011 · DOI
  38. Ganesan, K., & Steinbeis, N. (2022). Development and plasticity of executive functions: A value-based account. Current Opinion in Psychology, 44, 215-219. https://doi. org/10.1016/j.copsyc.2021.09.012 · DOI
  39. Gerosa, A., Koleszar, V., Tejera, G., Gómez-Sena, L., & Carboni, A. (2021). Cognitive abilities and computational thinking at age 5: Evidence for associations to sequencing and symbolic number comparison. Computers and Education Open, 2, Article 100043. https://doi.org/10.1016/j.caeo.2021.100043 · DOI
  40. Heppner, P. P. (1988). The problem-solving inventory: Manual. Consulting Psychologists Press.
  41. Huepe, D., Roca, M., Salas, N., Canales-Johnson, A., Rivera-Rei, Á. A., Zamorano, L., Concepción, A., Manes, F., & Ibañez, A. (2011). Fluid intelligence and psychosocial outcome: From logical problem solving to social adaptation. PLoS One, 6(9), Article e24858. https://doi.org/10.1371/journal.pone.0024858 · DOI
  42. Huizinga, M., Dolan, C. V., & van der Molen, M. W. (2006). Age-related change in executive function: Developmental trends and a latent variable analysis. Neuropsychologia, 44(11), 2017-2036. https://doi.org/10.1016/j.neuropsychologia.2006.01.010 · DOI · Paper page
  43. Jacob, R., & Parkinson, J. (2015). The potential for school-based interventions that target executive function to improve academic achievement: A review. Review of Educational Research, 85, 512-552. https://doi.org/10.3102/0034654314561338 · DOI · Paper page
  44. Jin, Z.-C., Zhou, X.-H., & He, J. (2015). Statistical methods for dealing with publication bias in meta-analysis. Statistics in Medicine, 34(2), 343-360. https://doi.org/ 10.1002/sim.6342 · DOI
  45. Jonkman, L. M., Lansbergen, M., & Stauder, J. E. A. (2003). Developmental differences in behavioral and event-related brain responses associated with response preparation and inhibition in a go/nogo task. Psychophysiology, 40(5), 752-761. https://doi.org/10.1111/1469-8986.00075 · DOI
  46. Kalelioglu, F. (2015). A new way of teaching programming skills to K-12 students: Code.org. Computers in Human Behavior, 52, 200-210. https://doi.org/10.1016/j. chb.2015.05.047 · DOI
  47. Kassai, R., Futo, J., Demetrovics, Z., & Takacs, Z. K. (2019). A meta-analysis of the experimental evidence on the near-and far-transfer effects among children's executive function skills. Psychological Bulletin, 145(2), 165-188. https://doi.org/10.1037/bul0000180 · DOI · Paper page
  48. 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.f · DOI
  49. Keen, R. (2011). The development of problem solving in young children: A critical cognitive skill. Annual Review of Psychology, 62(1), 1-21. https://doi.org/10.1146/ annurev.psych.031809.130730 · DOI
  50. Keren, G., & Fridin, M. (2014). Kindergarten social assistive robot (KindSAR) for children's geometric thinking and metacognitive development in preschool education: A pilot study. Computers in Human Behavior, 35, 400-412. https://doi.org/10.1016/j.chb.2014.03.009 · DOI
  51. Klenberg, L., Korkman, M., & Lahti-Nuuttila, P. (2001). Differential development of attention and executive functions in 3-to 12-year-old Finnish children. Developmental Neuropsychology, 20(1), 407-428. https://doi.org/10.1207/S15326942DN2001_6 · DOI · Paper page
  52. Korkman, M., Kirk, U., & Kemp, S. (2007). NEPSY-II: A developmental neurospychological assessment. The Psychological Corporation. https://psycnet.apa.org/ doiLanding?doi=10.1037%2Ft15125-000. · DOI · Paper page
  53. La Paglia, F., La Cascia, C., Francomano, M. M., & La Barbera, D. (2017). Educational robotics to improve mathematical and metacognitive skills. Annual Review of CyberTherapy and Telemedicine, 15.
  54. Lai, A. F., & Yang, S. M. (2011). The learning effect of visualized programming learning on 6th graders' problem solving and logical reasoning abilities. In 2011 international conference on electrical and control engineering (pp. 6940-6944). https://doi.org/10.1109/ICECENG.2011.6056908 · DOI
  55. Lee, K. T. H., Sullivan, A., & Bers, M. U. (2013). Collaboration by design: Using robotics to foster social interaction in kindergarten. Computers in the Schools, 30(3), 271-281. https://doi.org/10.1080/07380569.2013.805676 · DOI
  56. Liao, Y. (2000). A meta-analysis of computer programming on cognitive outcomes: An updated synthesis. In Paper presented at the proceedings of world conference on educational multimedia, hypermedia and telecommunications, Montreal, québec, Canada. Retrieved from https://www.learntechlib.org/primary/p/16132/.
  57. Liao, Y. K. C., & Bright, G. 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 · DOI
  58. Liu, Y., Ma, Z., & Qian, Y. (2019). Developing Chinese elementary school students' computational thinking: A convergent cognition perspective. Proceedings of the ACM Conference on Global Computing Education, 238. https://doi.org/10.1145/3300115.3312514 · DOI
  59. Luna, B., Garver, K. E., Urban, T. A., Lazar, N. A., & Sweeney, J. A. (2004). Maturation of cognitive processes from late childhood to adulthood. Child Development, 75 (5), 1357-1372. https://doi.org/10.1111/j.1467-8624.2004.00745.x · DOI · Paper page
  60. 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 · DOI · Paper page
  61. Macdonald, J. A., Beauchamp, M. H., Crigan, J. A., & Anderson, P. J. (2014). Age-related differences in inhibitory control in the early school years. Child Neuropsychology, 20(5), 509-526. https://doi.org/10.1080/09297049.2013.822060 · DOI
  62. Marinus, E., Powell, Z., Thornton, R., McArthur, G., & Crain, S. (2018). Unravelling the Cognition of Coding in 3-to-6-year Old: The development of an assessment tool and the relation between coding ability and cognitive compiling of syntax in natural language. Proceedings of the 2018 ACM Conference on International Computing Education Research, 133-141. https://doi.org/10.1145/3230977.3230984 · DOI
  63. Marshall, P. J., & Drew, A. R. (2014). What makes Simon Says so difficult for young children? Journal of Experimental Child Psychology, 126, 112-119. https://doi.org/ 10.1016/j.jecp.2014.03.011 · DOI
  64. Mathes, T., & Kuss, O. (2018). A comparison of methods for meta-analysis of a small number of studies with binary outcomes. Research Synthesis Methods, 9(3), 366-381. https://doi.org/10.1002/jrsm.1296 · DOI
  65. McGuckian, T. B., Wilson, P. H., Johnston, R. D., Rahimi-Golkhandan, S., Piek, J., Green, D., Rogers, J. M., Maruff, P., Steenbergen, B., & Ruddock, S. (2023). Development of complex executive function over childhood: Longitudinal growth curve modeling of performance on the Groton Maze Learning Task. Child Development, 94(3), 648-658. https://doi.org/10.1111/cdev.13888 · DOI
  66. Meixner, J. M., Warner, G. J., Lensing, N., Schiefele, U., & Elsner, B. (2019). The relation between executive functions and reading comprehension in primary-school students: A cross-lagged-panel analysis. Early Childhood Research Quarterly, 46, 62-74. https://doi.org/10.1016/j.ecresq.2018.04.010 · DOI
  67. Montuori, C., Ronconi, L., Vardanega, T., & Arfé, B. (2022). Exploring gender differences in coding at the beginning of primary school. Frontiers in Psychology, 13. https://www.frontiersin.org/articles/10.3389/fpsyg.2022.887280. · DOI
  68. Moreno-León, J., Robles, G., & Román-González, M. (2016). Comparing computational thinking development assessment scores with software complexity metrics. In 2016 IEEE global engineering education conference (EDUCON) (pp. 1040-1045). https://doi.org/10.1109/EDUCON.2016.7474681 · DOI
  69. Nam, D., Kim, Y., & Lee, T. (2010). The effects of scaffolding-based courseware for the scratch programming learning on student problem solving skill. In Proceedings of the 18th international conference on computers in education (ICCE).
  70. Nam, K. W., Kim, H. J., & Lee, S. (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 · DOI
  71. Nardelli, E. (2019). Do we really need computational thinking? Communications of the ACM, 62(2), 32-35. https://doi.org/10.1145/3231587 · DOI
  72. Nouwens, S., Groen, M. A., Kleemans, T., & Verhoeven, L. (2021). How executive functions contribute to reading comprehension. British Journal of Educational Psychology, 91(1), Article e12355. https://doi.org/10.1111/bjep.12355 · DOI
  73. Novack, M. A., Congdon, E. L., Hemani-Lopez, N., & Goldin-Meadow, S. (2014). From action to abstraction: Using the hands to learn math. Psychological Science, 25(4), 903-910. https://doi.org/10.1177/0956797613518351 OECD. (2013). PISA 2012 Assessment and Analytical Framework: Mathematics, Reading, Science, Problem Solving and Financial Literacy, OECD Publishing. https://www. oecd.org/pisa/pisaproducts/PISA%202012%20framework%20e-book_final.pdf. https://doi.org/10.1787/9789264190511-en · DOI
  74. Oluk, A., & Saltan, F. (2015). Effects of using the scratch program in 6th grade information technologies courses on algorithm development and problem solving skills. Participatory Educational Research (PER), 2(5), 10-20. https://doi.org/10.17275/per.15.spi.2.2 · DOI
  75. Özcan, M.S ¸., Çetinkaya, E., Göksun, T., & Kisbu-Sakarya, Y. (2021). Does learning to code influence cognitive skills of elementary school children? Findings from a randomized experiment. British Journal of Educational Psychology, 91(4), 1434-1455. https://doi.org/10.1111/bjep.12429 · DOI
  76. Pardamean, B., Evelin, E., & Honni, H. (2011). The effect of logo programming language for creativity and problem solving. Proceedings of the 10th WSEAS International Conference on E-Activities, 151-156.
  77. Passolunghi, M. C., & Costa, H. M. (2016). Working memory and early numeracy training in preschool children. Child Neuropsychology, 22(1), 81-98. https://doi.org/ 10.1080/09297049.2014.971726 · DOI
  78. Perkins, D. N., & Salomon, G. (1992). Transfer of learning. In International encyclopedia of education (Vol. 2, pp. 6452-6457). Pergamon Press.
  79. Ping, R., & Goldin-Meadow, S. (2008). Hands in the air. Developmental Psychology, 44(5), 1277-1287. https://doi.org/10.1037/0012-1649.44.5.1277 · DOI
  80. Polat, E., Hopcan, S., Kucuk, S., & Sisman, B. (2021). A comprehensive assessment of secondary school students' computational thinking skills. British Journal of Educational Technology, 52(5), 1965-1980. https://doi.org/10.1111/bjet.13092. Scopus. · DOI
  81. Pozuelos, J. P., Combita, L. M., Abundis, A., Paz-Alonso, P. M., Conejero, Á., Guerra, S., & Rueda, M. R. (2019). Metacognitive scaffolding boosts cognitive and neural benefits following executive attention training in children. Developmental Science, 22(2), Article e12756. https://doi.org/10.1111/desc.12756 · DOI
  82. Román-González, M., Pérez-González, J.-C., & Jiménez-Fernández, C. (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 · DOI
  83. Román-González, M., Pérez-González, J.-C., Moreno-León, J., & Robles, G. (2018). Can computational talent be detected? Predictive validity of the computational thinking test. International Journal of Child-Computer Interaction, 18, 47-58. https://doi.org/10.1016/j.ijcci.2018.06.004 · DOI
  84. 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 · DOI
  85. Salas, N., & Silvente, S. (2020). The role of executive functions and transcription skills in writing: A cross-sectional study across 7 years of schooling. Reading and Writing, 33(4), 877-905. https://doi.org/10.1007/s11145-019-09979-y · DOI
  86. 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, Article 106349. https://doi.org/10.1016/j.chb.2020.106349 · DOI
  87. Schmitt, S. A., McClelland, M. M., Tominey, S. L., & Acock, A. C. (2015). Strengthening school readiness for Head Start children: Evaluation of a self-regulation intervention. Early Childhood Research Quarterly, 30, 20-31. https://doi.org/10.1016/j.ecresq.2014.08.001 · DOI
  88. Scionti, N., Cavallero, M., Zogmaister, C., & Marzocchi, G. M. (2020). Is cognitive training effective for improving executive functions in preschoolers? A systematic review and meta-analysis. Frontiers in Psychology, 10. https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02812. · DOI · Paper page
  89. Serin, O., Bulut Serin, N., & Saygılı, G. (2010). ˙Ilkögretim düzeyindeki çocuklar için problem çözmeenvanteri'nin (ÇPÇE) gelis ¸tirilmesi [developing problem solving inventory for children at the levelof primary education (PSIC)]. ˙Ilkögretim Online, 9(2), 446-458. http://ilkogretim-online.org.tr/index.php/io/article/view/ 1774/1610.
  90. Socratous, C., & Ioannou, A. (2021). Structured or unstructured educational robotics curriculum? A study of debugging in block-based programming. Educational Technology Research & Development, 69(6), 3081-3100. https://doi.org/10.1007/s11423-021-10056-x · DOI
  91. Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11(5), 702-712. https://doi.org/10.1177/1745691616658637 · DOI
  92. Sterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., Carpenter, J., Rücker, G., Harbord, R. M., Schmid, C. H., Tetzlaff, J., Deeks, J. J., Peters, J., Macaskill, P., Schwarzer, G., Duval, S., Altman, D. G., Moher, D., & Higgins, J. P. T. (2011). Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ, 343, d4002. https://doi.org/10.1136/bmj.d4002 · DOI · Paper page
  93. Strawhacker, A., & Bers, M. U. (2019). What they learn when they learn coding: Investigating cognitive domains and computer programming knowledge in young children. Educational Technology Research & Development, 67(3), 541-575. https://doi.org/10.1007/s11423-018-9622-x · DOI
  94. Traverso, L., Viterbori, P., & Usai, M. C. (2015). Improving executive function in childhood: Evaluation of a training intervention for 5-year-old children. Frontiers in Psychology, 6. https://www.frontiersin.org/articles/10.3389/fpsyg.2015.00525. · DOI
  95. Tsai, C. Y. (2019). Improving students' understanding of basic programming concepts through visual programming language: The role of self-efficacy. Computers in Human Behavior, 95, 224-232. https://doi.org/10.1016/j.chb.2018.11.038 · DOI
  96. Tsarava, K., Leifheit, L., Ninaus, M., Román-González, M., Butz, M. V., Golle, J., Trautwein, U., & Moeller, K. (2019). Cognitive correlates of computational thinking: Evaluation of a blended unplugged/plugged-in course. Proceedings of the 14th Workshop in Primary and Secondary Computing Education, 1-9. https://doi.org/ 10.1145/3361721.3361729 · DOI
  97. Unterrainer, J. M., & Owen, A. M. (2006). Planning and problem solving: From neuropsychology to functional neuroimaging. Journal of Physiology Paris, 99(4), 308-317. https://doi.org/10.1016/j.jphysparis.2006.03.014 · DOI
  98. Usai, M. C., Viterbori, P., Traverso, L., & De Franchis, V. (2014). Latent structure of executive function in five-and six-year-old children: A longitudinal study. European Journal of Developmental Psychology, 11(4), 447-462. https://doi.org/10.1080/17405629.2013.840578 · DOI
  99. Van den Noortgate, W., López-López, J. A., Marín-Martínez, F., & Sánchez-Meca, J. (2015). Meta-analysis of multiple outcomes: A multilevel approach. Behavior Research Methods, 47(4), 1274-1294. https://doi.org/10.3758/s13428-014-0527-2 · DOI · Paper page
  100. Viechtbauer, W. (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 · DOI · Paper page
  101. Wechsler, D. (2011). Wechsler Abbreviated Scale of intelligence-second edition (WASI-II). San Antonio, TX: NCS Pearson.
  102. Welsh, J. A., Nix, R. L., Blair, C., Bierman, K. L., & Nelson, K. E. (2010). The development of cognitive skills and gains in academic school readiness for children from low-income families. Journal of Educational Psychology, 102(1), 43-53. https://doi.org/10.1037/a0016738 · DOI · Paper page
  103. Welsh, M. C., & Pennington, B. F. (1988). Assessing frontal lobe functioning in children: Views from developmental psychology. Developmental Neuropsychology, 4, 199-230. https://doi.org/10.1080/87565648809540405 · DOI
  104. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215 · DOI · Paper page
  105. Yas ¸ar, O. (2018). A new perspective on computational thinking. Communications of the ACM, 61(7), 33-39. https://doi.org/10.1145/3214354 · DOI
  106. Zelazo, P. D., Carter, A., Reznick, J. S., & Frye, D. (1997). Early development of executive function: A problem-solving framework. Review of General Psychology, 1(2), 198-226. https://doi.org/10.1037/1089-2680.1.2.198 · DOI
  107. Zelazo, P. D., Müller, U., Frye, D., Marcovitch, S., Argitis, G., Boseovski, J., Chiang, J. K., Hongwanishkul, D., Schuster, B. V., & Sutherland, A. (2003). The development of executive function in early childhood. Monographs of the Society for Research in Child Development, 68(3). https://doi.org/10.1111/j.0037- 976x.2003.00260.x. vii-137. · DOI
  108. Zhang, J. X., Liu, L., Pablos, P. O., & She, J. (2014). The auxiliary role of information technology in teaching: Enhancing programming course using alice. International Journal of Engineering Education, 30(3), 560-565.
  109. Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education, 141, Article 103607. https://doi. org/10.1016/j.compedu.2019.103607 · DOI

Cited by

Sorted by citations

Authors
Montuori, C., Gambarota, F., Altoé, G., & Arfé, B.
Affiliations
University of Padua (IT)
Corresponding author
Arfé, Barbara
ORCID
Venue
Computers & Education
Publisher
Elsevier BV
Published
2024-03
Volume
210
Pages
104961
ISSN
0360-1315 (ISSN-L)
DOI
10.1016/j.compedu.2023.104961
OpenAlex
W4388785132
Cited by
76
References
21
Citations by year
2026: 38 · 2025: 23 · 2024: 15
FWCI
3.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers & Education (publishedVersion, cc-by)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
  • Social Sciences › Education › Child Development and Digital Technology
Keywords
computational thinking, executive function, programming education, children, educational robotics, unplugged coding, Scratch, response inhibition, working memory, cognitive flexibility, planning, problem solving, age differences

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