The effect of after-school extracurricular robotic classes on elementary students’ computational thinking

References

• Aho, A. V. (2012). Computation and computational thinking. Computer Journal, 55(7), 833–835. https://doi.org/10.1093/comjnl/bxs074
   Web of Science ®Google Scholar
• Almeida, L. S., Guisande, M. A., Primi, R., & Lemos, G. (2008). Contribuciones del factor general y de los factores específicos en la relación entre inteligencia y rendimiento escolar. European Journal of Education and Psychology, 1(3), 5–16. https://doi.org/10.30552/ejep.v1i3.13.
   Google Scholar
• Angeli, C., & Valanides, N. (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
   Web of Science ®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. https://doi.org/10.2307/jeductechsoci.19.3.47
   Web of Science ®Google Scholar
• Atun, H., & Usta, E. (2019). The effects of programming education planned with TPACK framework on learning outcomes. Participatory Educational Research, 6(2), 26–36. https://doi.org/10.17275/per.19.10.6.2
   Google Scholar
• Balanskat, A., & Engelhardt, K. (2015). Computing our future computer programming and coding. Priorities, school curricula and initiatives across Europe. European Schoolnet.
   Google Scholar
• Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016a, June 1–7). Developing Computational thinking : approaches and orientations in K-12 education. Proceedings EdMedia.
   Google Scholar
• Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016b, June). Developing Computational Thinking in compulsory education - implications for policy and practice. Joint Research Centre (JRC), 32. https://doi.org/10.2791/792158.
   Google Scholar
• Bocconi, S., Chioccariello, A., & Earp, J. (2018). The nordic approach to introducing computational thinking and programming in compulsory education. Report Prepared for the Nordic@BETT2018 Steering Group, 42. https://doi.org/10.17471/54007
   Google Scholar
• Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Annual American Educational Research Association Meeting, Vancouver, BC, Canada, 1–25.
   Google Scholar
• Buitrago Flórez, 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
   Web of Science ®Google Scholar
• Carbonaro, W., & Maloney, E. (2019). Extracurricular activities and student outcomes in elementary and middle school: Causal effects or self-selection? Socius: Sociological Research for a Dynamic World, 5, 1–17. https://doi.org/10.1177/2378023119845496.
   PubMedGoogle Scholar
• Cerda-Etcheoare, G., Flores-Solar, C., & Pérez-Wilson, C. (2010). Inteligencia lógica, rendimiento en matemáticas y factores asociados en estudiantes chilenos de educación básica. Paideia, 48.
   Google Scholar
• Cerminati, M. A. (2019). Relaciones entre comprensión lectora, memoria de trabajo e inteligencia fluida en niños de edad escolar. http://200.0.183.210/bitstream/handle/123456789/937/Cerminati-Martinez-Peña.pdf?sequence=1&isAllowed=y
   Google Scholar
• Chuderski, A. (2013). When are fluid intelligence and working memory isomorphic and when are they not? Intelligence, 41(4), 244–262. https://doi.org/10.1016/j.intell.2013.04.003
   Web of Science ®Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Academic Press.
   Google Scholar
• Cooper, S., Pérez, L. C., & Rainey, D. (2010). K–12 computational learning. Communications of the ACM, 53(11), 27–29. https://doi.org/10.1145/1839676.1839686.
   Web of Science ®Google Scholar
• Cordero, A., & Calogne, I. (2000). Test breve de inteligencia de Kaufman. TEA.
   Google Scholar
• Cormier, D. C., Bulut, O., McGrew, K. S., & Frison, J. (2016). The role of Cattell-Horn-Carroll (CHC) cognitive abilities in predicting writting achievement during the school-age years. Psychology in the Schools, 53(8), 787–803. https://doi.org/10.1002/pits.21945
   Web of Science ®Google Scholar
• CSTA. (2013). Bugs in the system: Computer science teacher certification in the U.S.
   Google Scholar
• Denner, J., Werner, L., Campe, S., & Ortiz, E. (2014). Pair programming: Under what conditions is it advantageous for middle school students? Journal of Research on Technology in Education, 46(3), 277–296. https://doi.org/10.1080/15391523.2014.888272
   Google Scholar
• Duncan, C., Bell, T., & Atlas, J. (2017). What do the teachers think? Introducing computational thinking in the primary school curriculum. ACM International Conference Proceeding Series, 65–74. https://doi.org/10.1145/3013499.3013506
   Google Scholar
• Durak, H. Y., & Saritepeci, M. (2018). Analysis of the relation between computational thinking skills and various variables with the structural equation model. Computers and Education, 116, 191–202. https://doi.org/10.1016/j.compedu.2017.09.004
   Web of Science ®Google Scholar
• European Commission. (2018). Communication from the commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions on de Digital Education Action Plan -COM/2018/022 final-.
   Google Scholar
• Flores, P., & Browne, R. (2017). Jóvenes y patriarcado en la sociedad TIC: Una reflexión desde la violencia simbólica de género en redes sociales. Revista Latinoamericana de Ciencias Sociales, Niñez y Juventud, 15(1), 147–160. https://doi.org/10.11600/1692715x.1510804082016
   Google Scholar
• Google. (2015). Searching for computer science: Access and barriers in U.S. K-12 education. https://services.google.com/fh/files/misc/searching-for-computer-science_report.pdf
   Google Scholar
• Grover, S., Pea, R., & Cooper, S. (2016). Factors influencing computer science learning in middle school. Proceedings of the 47th ACM Technical Symposium on Computing Science Education – SIGCSE’16, 552–557. https://doi.org/10.1145/2839509.2844564
   Google Scholar
• Guzdial, Mark. (2008). Education: Paving the way for computational thinking. Communications of the ACM, 51(8), 25–27. https://doi.org/10.1145/1378704.1378713
   Web of Science ®Google Scholar
• Haavisto, M. L., & Lehto, J. E. (2005). Fluid/spatial and crystallized intelligence in relation to domain-specific working memory: A latent-variable approach. Learning and Individual Differences, 15(1), 1–21. https://doi.org/10.1016/j.lindif.2004.04.002
   Web of Science ®Google Scholar
• Hansen, L. B., Macizo, P., Duñabeitia, J. A., Saldaña, D., Carreiras, M., Fuentes, L. J., & Bajo, M. T. (2016). Emergent bilingualism and working memory development in school aged children. Language learning, 66, 51–75. https://doi.org/10.1111/lang.12170
   Web of Science ®Google Scholar
• Hardy, L., Castles, M., Yeom, S., & Kang, B. H. (2019). Challenges and prospects of a robotics course to supplement Australia’s digital technology curriculum. Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019, 224–226. https://doi.org/10.1109/ICALT.2019.00074
   Google Scholar
• Hasesk, H. I., & Ilic, U. (2019). An investigation of the data collection instruments developed to measure computational thinking. Informatics in Education, 18(2), 297–319. https://doi.org/10.15388/infedu.2019.14
   Web of Science ®Google Scholar
• Horn, J. L. (1991). Measurement of intellectual capabilities: A review of theory. In K. S. McGrew, J. K. Werder, & R. W. Woodcock (Eds.), Woodcock-Johnson technical manual (pp. 197–232). Riverside.
   Google Scholar
• Horn, J. L., & Hofer, S. M. (1992). Major abilities and development in the adult period. In R. J. Sternberg & C. A. Berg (Eds.), . In Intellectual development (pp. 44–99). Cambridge University Press. https://psycnet.apa.org/record/1992-97724-003.
   Google Scholar
• Horn, J. L., Noll, J., Flanagan, P. L. H. D. P., & Genshaft, J. L. (1997). Human cognitive capabilities: Gf-Gc theory. In D. P. Flanagan, J. L. Genshaft, & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (pp. 53–91). Guilford Press. http://psycnet.apa.org/record/1997-97010-004
   Google Scholar
• Igbokwe, C. O. (2015). Recent curriculum reforms at the Basic education level in Nigeria aimed at catching them young to create change. American Journal of Educational Research, 3(1), 31–37. https://doi.org/10.12691/education-3-1-7
   Google Scholar
• INTEF. (2018). Programación, robótica y pensamiento computacional en el aula. Situación en España, enero 2018.
   Google Scholar
• Jenson, J., & Droumeva, M. (2016). Exploring media literacy and computational thinking: A game maker curriculum study. Electronic Journal of E-Learning, 14(2), 111–121. https://eric.ed.gov/?id=EJ1101239
   Web of Science ®Google Scholar
• Jeon, I., & Song, K. S. (2019). The effect of learning analytics system towards learner’s computational thinking capabilities. ACM International Conference Proceeding Series, 12–16. https://doi.org/10.1145/3313991.3314017
   Google Scholar
• Kaufman, A. S. (1990). Kaufman brief intelligence test: KBIT. American Guidance Service.
   Google Scholar
• Kaufman, A. S., & Wang, J.-J. (1992). Gender, race, and education differences on the K-Bit at ages 4 to 90 years. Journal of Psychoeducational Assessment, 10(3), 219–229. https://doi.org/10.1177/073428299201000302
   Web of Science ®Google Scholar
• Keyes, K. M., Platt, J., Kaufman, A. S., & McLaughlin, K. A. (2017). Association of fluid intelligence and psychiatric disorders in a population-representative sample of US adolescents. JAMA Psychiatry, 74(2), 179–188. https://doi.org/10.1001/jamapsychiatry.2016.3723
   PubMed Web of Science ®Google Scholar
• Kroes, N., & Vassiliou, A. (2014). Promoting coding skills in Europe is part of the solution to youth unemployment | Shaping Europe’s digital future. https://ec.europa.eu/digital-single-market/en/news/promoting-coding-skills-europe-part-solution-youth-unemployment
   Google Scholar
• Kvist, A. V., & Gustafsson, J. E. (2008). The relation between fluid intelligence and the general factor as a function of cultural background: A test of cattell’s investment theory. Intelligence, 36(5), 422–436. https://doi.org/10.1016/j.intell.2007.08.004
   Web of Science ®Google Scholar
• Léonard, M., Peter, Y., & Secq, Y. (2019). Patterns and loops: Early computational thinking: Vol. LNCS 11722. https://hal.archives-ouvertes.fr/hal-02383097
   Google Scholar
• Leviton, A., Dammann, O., Allred, E. N., Joseph, R. M., Fichorova, R. N., O’Shea, T. M., & Kuban, K. C. K. (2018). Neonatal systemic inflammation and the risk of low scores on measures of reading and mathematics achievement at age 10 years among children born extremely preterm. International Journal of Developmental Neuroscience, 66(1), 45–53. https://doi.org/10.1016/j.ijdevneu.2018.01.001
   PubMedGoogle Scholar
• 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
   Web of Science ®Google Scholar
• Mahoney, J., Larson, R., Eccles, J., Lord, H., Mahoney, J., Larson, R., & Eccles, J. (2005). Organized activities as developmental contexts for children and adolescents. In J. L. Mahoney, R. W. Larson, & J. S. Eccles (Eds.), Organized activities as contexts of development: Extracurricular activities, after-school and community programs (pp. 3–22). Lawrence Erlbaum Associates. https://doi.org/10.4324/9781410612748
   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
   Web of Science ®Google Scholar
• Martinez, A., Coker, C., McMahon, S. D., Cohen, J., & Thapa, A. (2016). Involvement in extracurricular activities: Identifying differences in perceptions of school climate. Educational and Developmental Psychologist, 33(1), 70–84. https://doi.org/10.1017/edp.2016.7
   Google Scholar
• Martínez, Á. M., Martín, A. B. B., Pérez-Esteban, M. D., del Mar, M., Jurado, M., del Carmen Pérez-Fuentes, M., & Linares, J. J. G. (2016). Plasticidad cerebral y aprendizaje a lo largo de toda la vida. In J. J. Gázquez, M. M. Molero, M. C. Pérez-Fuentes, A. B. Barragán, A. Martos, M. D. Pérez-Esteban (Eds). Salud, alimentación y sexualidad en el ciclo vital. Volumen I (pp. 123–129). ASUNIVEP.
   Google Scholar
• McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1–10. https://doi.org/10.1016/j.intell.2008.08.004
   Web of Science ®Google Scholar
• Meadows, A. (2015). The impact of oarticipation in extracurricular activities on elementary school students. Journal of Interdisciplinary Graduate Research, 11, 1. doi:10.1093/SF/SOY016
   Google Scholar
• Ministerio de Educación y Formación Profesional, & INTEF. (2019). La Escuela de Pensamiento Computacional y su impacto.
   Google Scholar
• Morris, E. (2019). Participation in extracurricular activities and academic achievement: A comprehensive review. Masters Theses & Specialist Projects. https://digitalcommons.wku.edu/theses/3097
   Google Scholar
• Mouza, C., Marzocchi, A., Pan, Y. C., & Pollock, L. (2016). Development, implementation, and outcomes of an equitable computer science after-school program: Findings from middle-school students. Journal of Research on Technology in Education, 48(2), 84–104. https://doi.org/10.1080/15391523.2016.1146561
   Web of Science ®Google Scholar
• Noh, J., & Lee, J. (2019). 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
   Web of Science ®Google Scholar
• Papert, S. (1983). Mindstorms: Children, computers and powerful ideas. In New ideas in psychology. Basic Books (Vol. 1). https://doi.org/10.1016/0732-118X(83)90034-X
   Google Scholar
• Pears, A., Dagiene, V., & Jasute, E. (2017). Baltic and nordic K-12 teacher perspectives on computational thinking and computing. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10696 LNCS, 141–152. https://doi.org/10.1007/978-3-319-71483-7_12
   Google Scholar
• R Core Team. (2020). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
   Google Scholar
• Resnick, M., Silverman, B., Kafai, Y., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., & Silver, J. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60–67. https://doi.org/10.1145/1592761.1592779.
   Web of Science ®Google Scholar
• Ritchie, S. J., Bates, T. C., & Plomin, R. (2015). Does learning to read improve intelligence? A longitudinal multivariate analysis in identical twins from Age 7 to 16. Child Development, 86(1), 23–36. https://doi.org/10.1111/cdev.12272
   PubMed Web of Science ®Google Scholar
• Román-González, M. (2016). Códigoalfabetización y Pensamiento Computacional en Educación Primaria y Secundaria: Validación de un instrumento y evaluación de programas. Universidad Nacional de Educación a Distancia (España). Escuela Internacional de Doctorado. Programa de Doctorado en Educación.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Roth, B., Becker, N., Romeyke, S., Schäfer, S., Domnick, F., & Spinath, F. M. (2015). Intelligence and school grades: A meta-analysis. Intelligence, 53, 118–137. https://doi.org/10.1016/j.intell.2015.09.002.
   Web of Science ®Google Scholar
• The Royal Society. (2012, January). Shut down or restart? The way forward for computing in UK schools. Technology, 1–122. https://doi.org/10.1088/2058-7058/25/07/21
   Google Scholar
• Sáez-López, J. M., González, M. R., & Cano, E. V. (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
   Web of Science ®Google Scholar
• Sentance, S., & Csizmadia, A. (2017). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. Education and Information Technologies, 22(2), 469–495. https://doi.org/10.1007/s10639-016-9482-0
   Web of Science ®Google Scholar
• Shih, J.-L., Huang, S.-H., Lin, C.-H., & Tseng, C.-C. (2017). STEAMing the ships for the great voyage: Design and evaluation of a technology-integrated maker game. Interaction Design and Architecture, 34, 61–87.
   Google Scholar
• Shipstead, Z., Harrison, T. L., & Engle, R. W. (2016). Working Memory capacity and fluid intelligence: Maintenance and disengagement. Perspectives on Psychological Science, 11(6), 771–799. https://doi.org/10.1177/1745691616650647
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Sisman, B., Kucuk, S., & Yaman, Y. (2020). The effects of robotics training on children’s spatial ability and attitude toward STEM. International Journal of Social Robotics, 13, 379–389. https://doi.org/10.1007/s12369-020-00646-9.
   Web of Science ®Google Scholar
• Steinmann, I., Strietholt, R., & Caro, D. (2019). Participation in extracurricular activities and student achievement: Evidence from German all-day schools. School Effectiveness and School Improvement, 30(2), 155–176. https://doi.org/10.1080/09243453.2018.1540435
   Web of Science ®Google Scholar
• Stevenson, C. E., Bergwerff, C. E., Heiser, W. J., & Resing, W. C. M. (2014). Working Memory and Dynamic measures of analogical reasoning as predictors of children’s math and reading achievement. Infant and Child Development, 23(1), 51–66. https://doi.org/10.1002/icd.1833
   Web of Science ®Google Scholar
• Swaid, S. I. (2015). Bringing computational thinking to STEM education. Procedia Manufacturing, 3, 3657–3662. https://doi.org/10.1016/j.promfg.2015.07.761
   Google Scholar
• Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers and Education, 148, 103798. https://doi.org/10.1016/j.compedu.2019.103798
   Web of Science ®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
   Web of Science ®Google Scholar
• van Bergen, E., de Jong, P. F., Maassen, B., Krikhaar, E., Plakas, A., & van der Leij, A. (2014). IQ of four-year-olds who go on to develop dyslexia. Journal of Learning Disabilities, 47(5), 475–484. https://doi.org/10.1177/0022219413479673
   PubMed Web of Science ®Google Scholar
• Webber, L. S., & McGillivray, J. A. (1998). An Australian validation of the Kaufman brief intelligence test (K-BIT) with adolescents with An intellectual disability. Australian Psychologist, 33(3), 234–237. https://doi.org/10.1080/00050069808257412
   Web of Science ®Google Scholar
• Werner, L., Denner, J., & Campe, S. (2014). Children programming games. ACM Transactions on Computing Education, 14(4), 1–22. https://doi.org/10.1145/2677091
   Web of Science ®Google Scholar
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215.
   Web of Science ®Google Scholar
• Wing, J. M. (2017). Computational thinking’s influence on research and education for all influenza del pensiero computazionale nella ricerca e nell’educazione per tutti. Italian Journal of Educational Technology, 25(2), 7–14. https://doi.org/10.1747/<otherinfo>1/2</otherinfo>499-4324/922
   Google Scholar
• Witherspoon, E. B., & Schunn, C. D. (2019). Teachers’ goals predict computational thinking gainsin robotics. Information and Learning Science, 120(5–6), 308–326. https://doi.org/10.1108/ILS-05-2018-0035
   Web of Science ®Google Scholar
• Zapata-Ros, M. (2015). Pensamiento computacional: Una nueva alfabetización digital. Revista de Educación a Distancia, 46(4), 1–47. https://doi.org/10.6018/red/46/4
   Google Scholar