References
- Akshoomoff, N., Brown, T. T., Bakeman, R., & Hagler, D. J. (2018). Developmental differentiation of executive functions on the NIH toolbox cognition battery. Neuropsychology, 32(7), 777–783. https://doi.org/https://doi.org/10.1037/neu0000476
- Andrä, C., Mathias, B., Schwager, A., Macedonia, M., & von Kriegstein, K. (2020). Learning foreign language vocabulary with gestures and pictures enhances vocabulary memory for several months post-learning in eight-year-old school children. Educational Psychology Review, 32(3), 815–850. https://doi.org/https://doi.org/10.1007/s10648-020-09527-z
- 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/https://doi.org/10.1016/j.chb.2019.03.018
- Araujo, A. L. S. O., Andrade, W. L., Guerrero, D. D. S., & Melo, M. R. A. (2019, February 27–March 2). How many abilities can we measure in computational thinking? A study on Bebras challenge. In 50th ACM Technical Symposium on Computer Science Education, Minneapolis, MN.
- Arfé, B., Vardanega, T., & Ronconi, L. (2020). The effects of coding on children’s planning and inhibition skills. Computers & Education, 148, 103807. https://doi.org/https://doi.org/10.1016/j.compedu.2020.103807
- Arfé, B., Vardanega, T., Montuori, C., & Lavanga, M. (2019). Coding in primary grades boosts children’s executive functions. Frontiers in Psychology, 10, 2713. https://doi.org/https://doi.org/10.3389/fpsyg.2019.02713
- Barr, V., & Stephenson, C. (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/https://doi.org/10.1145/1929887.1929905
- Bebras. (2018). International challenge on informatics computational thinking. https://www.bebras.org/
- Bers, M. (2012). Designing digital experiences for positive youth development: From playpen to playground. Oxford University Press.
- 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/https://doi.org/10.1016/j.compedu.2013.10.020
- Bers, M., González-González, C., & Armas–Torres, M. B. (2019). Coding as a playground: Promoting positive learning experiences in childhood classrooms. Computers & Education, 138, 130–145. https://doi.org/https://doi.org/10.1016/j.compedu.2019.04.013
- Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624–652. https://doi.org/https://doi.org/10.1037//0033-295X.108.3.624
- Braver, T. S. (2012). The variable nature of cognitive control: A dual mechanisms framework. Trends in Cognitive Sciences, 16(2), 106–113. https://doi.org/https://doi.org/10.1016/j.tics.2011.12.010
- Braver, T. S., Gary, J. R., & Burgess, G. C. (2007). Explaining the many varieties of working memory variation: Dual mechanisms of cognitive control. In C. Jarrold (Ed.), Variation in working memory (pp. 76–94). Oxford University Press. https://doi.org/https://doi.org/10.1093/acprof:oso/9780195168648.003.0004
- Broaders, S., Cook, S., Mitchell, Z., & Goldin-Meadow, S. (2007). Making children gesture brings out implicit knowledge and leads to learning. Journal of Experimental Psychology. General, 136(4), 539–550. https://doi.org/https://doi.org/10.1037/0096-3445.136.4.539
- Burleson, W. S., Harlow, D. B., Nilsen, K. J., Perlin, K., Freed, N., Jensen, C. N., Lahey, B., Lu, P., & Muldner, K. (2018). Active learning environments with robotic tangibles: Children’s physical and virtual spatial programming experiences. IEEE Transactions on Learning Technologies, 11(1), 96–106. https://doi.org/https://doi.org/10.1109/TLT.2017.2724031
- Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review, 97(3), 404–431. https://doi.org/https://doi.org/10.1037/0033-295X.97.3.404
- Chatham, C. H., Frank, M. J., & Munakata, Y. (2009). Pupillometric and behavioral markers of a developmental shift in the temporal dynamics of cognitive control. Proceedings of the National Academy of Sciences of the United States of America, 106(14), 5529–5533. https://doi.org/https://doi.org/10.1073/pnas.0810002106
- Chevalier, N., Martis, S. B., Curran, T., & Munakata, Y. (2015). Metacognitive processes in executive control development: The case of reactive and proactive control. Journal of Cognitive Neuroscience, 27(6), 1125–1136. https://doi.org/https://doi.org/10.1162/jocn_a_00782
- Chiew, K. S., & Braver, T. S. (2017). Context processing and cognitive control: From gating models to dual mechanisms. In T. Egner (Ed.), The Wiley handbook of cognitive control (pp. 143–166). Wiley Blackwell. https://doi.org/https://doi.org/10.1002/9781118920497.ch9
- Christoff, K., Keramatian, K., Gordon, A. M., Smith, R., & Mädler, B. (2009). Prefrontal organization of cognitive control according to levels of abstraction. Brain Research, 1286, 94–105. https://doi.org/https://doi.org/10.1016/j.brainres.2009.05.096
- 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/https://doi.org/10.1080/08993408.2019.1696169
- Cohen, J. A. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/https://doi.org/10.1037/0033-2909.112.1.155
- Coldren, J. T. (2013). Cognitive control predicts academic achievement in kindergarten children. Mind, Brain, and Education, 7(1), 40–48. https://doi.org/https://doi.org/10.1111/mbe.12006
- Crone, E. A., & Steinbeis, N. (2017). Neural perspectives on cognitive control development during childhood and adolescence. Trends in Cognitive Sciences, 21(3), 205–215. https://doi.org/https://doi.org/10.1016/j.tics.2017.01.003
- Dagiene, V., & Sentance, S. (2016). It’s computational thinking! Bebras tasks in the curriculum. In A. Brodnik & F. Tort (Eds.), Informatics in schools: Improvement of informatics knowledge and perception. ISSEP 2016. Lecture notes in computer science (Vol. 9973, pp. 28–39). Springer International Publishing. https://doi.org/https://doi.org/10.1007/978-3-319-46747-4_3
- 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/https://doi.org/10.1016/j.neuropsychologia.2006.02.006
- Demetriou, A., Kazali, E., Kazi, S., & Spanoudis, G. (2020). Cognition and cognizance in preschool predict school achievement in primary school. Cognitive Development, 54, 100872. https://doi.org/https://doi.org/10.1016/j.cogdev.2020.100872
- Demetriou, A., Kazi, S., Makris, N., & Spanoudis, G. (2020). Cognitive ability, cognitive self-awareness, and school performance: From childhood to adolescence. Intelligence, 79, 101432. https://doi.org/https://doi.org/10.1016/j.intell.2020.101432
- Demetriou, A., Makris, N., Kazi, S., Spanoudis, G., & Shayer, M. (2018). The developmental trinity of mind: Cognizance, executive control, and reasoning. Wiley Interdisciplinary Reviews. Cognitive Science, 9(4), e1461. https://doi.org/https://doi.org/10.1002/wcs.1461
- Di Lieto, M. C., Inguaggiato, E., Castro, E., Cecchi, F., Cioni, G., Dell’Omo, M., Laschi, C., Pecini, C., Santerini, G., Sgandurra, G., & Dario, P. (2017). Educational robotics intervention on executive functions in preschool children: A pilot study. Computers in Human Behavior, 71, 16–23. https://doi.org/https://doi.org/10.1016/j.chb.2017.01.018
- Djambong, T., & Freiman, V. (2016, October 28–30). Task-based assessment of students’ computational thinking skills developed through visual programming or tangible coding environments. In 13th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2016), Mannheim, Germany.
- Erb, C. D., & Marcovitch, S. (2019). Tracking the within-trial, cross-trial, and developmental dynamics of cognitive control: Evidence from the Simon task. Child Development, 90(6), e831–e848. https://doi.org/https://doi.org/10.1111/cdev.13111
- Espinet, S. D., Anderson, J. E., & Zelazo, P. D. (2013). Reflection training improves executive function in preschool-age children: Behavioral and neural effects. Developmental Cognitive Neuroscience, 4, 3–15. https://doi.org/https://doi.org/10.1016/j.dcn.2012.11.009
- Fandakova, Y., & Hartley, C. A. (2020). Mechanisms of learning and plasticity in childhood and adolescence. Developmental Cognitive Neuroscience, 42, 100764. https://doi.org/https://doi.org/10.1016/j.dcn.2020.100764
- Fee, S. B., & Holland-Minkley, A. M. (2010). Teaching computer science through problems, not solutions. Computer Science Education, 20(2), 129–144. https://doi.org/https://doi.org/10.1080/08993408.2010.486271
- Gadanidis, G., Cendros, R., Floyd, L., & Namukasa, I. (2017). Computational thinking in mathematics teacher education. Contemporary Issues in Technology and Teacher Education, 17, 458–477.
- Gonthier, C., Zira, M., Colé, P., & Blaye, A. (2019). Evidencing the developmental shift from reactive to proactive control in early childhood and its relationship to working memory. Journal of Experimental Child Psychology, 177, 1–16. https://doi.org/https://doi.org/10.1016/j.jecp.2018.07.001
- 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/https://doi.org/10.3102/0013189X12463051
- Hadley, L. v., Acluche, F., & Chevalier, N. (2020). Encouraging performance monitoring promotes proactive control in children. Developmental Science, 23(1), e12861. https://doi.org/https://doi.org/10.1111/desc.12861
- Harel, I., & Papert, S. (1991). Constructionism. Ablex Publishing.
- Hazzan, O., Lapidot, T., & Ragonis, N. (2014). Guide to teaching computer science. Springer.
- Heintz, F., Mannila, L., & Farnqvist, T. (2016, October 12–15). A review of models for introducing computational thinking, computer science and computing in K-12 education. In 2016 IEEE Frontiers in Education Conference (FIE), Eire, PA. https://doi.org/https://doi.org/10.1109/FIE.2016.7757410
- Hsu, Y. C., Irie, N. R., & Ching, Y. H. (2019). Computational thinking educational policy initiatives (CTEPI) across the globe. TechTrends, 63(3), 260–270. https://doi.org/https://doi.org/10.1007/s11528-019-00384-4
- Ivanova, A. A., Srikant, S., Sueoka, Y., Kean, H. H., Dhamala, R., O’Reilly, U.-M., Bers, M. U., & Fedorenko, E. (2020). Comprehension of computer code relies primarily on domain-general executive brain regions. eLife, 9, e58906. https://doi.org/https://doi.org/10.7554/eLife.58906
- K-12 Computer Science Framework Steering Committee. (2016). K-12 computer science framework. https://k12cs.org/
- Koechlin, E., Ody, C., & Kouneiher, F. (2003). The architecture of cognitive control in the human prefrontal cortex. Science (New York, N.Y.), 302(5648), 1181–1185. https://doi.org/https://doi.org/10.1126/science.1088545
- Kubota, M., Hadley, L. V., Schaeffner, S., Könen, T., Meaney, J. A., Auyeung, B., Morey, C. C., Karbach, J., & Chevalier, N. (2020). Consistent use of proactive control and relation with academic achievement in childhood. Cognition, 203, 104329. https://doi.org/https://doi.org/10.1016/j.cognition.2020.104329
- Liu, Y. F., Kim, J., Wilson, C., & Bedny, M. (2020). Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network. eLife, 9, e59340. https://doi.org/https://doi.org/10.7554/eLife.59340
- Lucenet, J., & Blaye, A. (2019). What do I do next? The influence of two self-cueing strategies on children’s engagement of proactive control. Cognitive Development, 50, 167–176. https://doi.org/https://doi.org/10.1016/j.cogdev.2019.05.001
- 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/https://doi.org/10.1016/j.chb.2014.09.012
- Ma, Y. P. (2021). Development and evaluation of online tools for measuring programming thinking skills in young children. Bachelor [thesis]. Central China Normal University.
- Margolis, A. E., Pagliaccio, D., Davis, K. S., Thomas, L., Banker, S. M., Cyr, M., & Marsh, R. (2020). Neural correlates of cognitive control deficits in children with reading disorder. Brain Imaging and Behavior, 14(5), 1531–1542. https://doi.org/https://doi.org/10.1007/s11682-019-00083-x
- Matatalab. (2019). Matatalab. https://matatalab.com/zh-hans
- Mayer, R. E. (2014). Principles based on social cues in multimedia learning: Personalization, voice, image, and embodiment principles. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 345–370). Cambridge University Press. https://doi.org/https://doi.org/10.1017/CBO9781139547369.017
- Moreno-León, J., Robles, G., & Román-González, M. (2016). Code to learn: Where does it belong in the K-12 curriculum? Journal of Information Technology Education: Research, 15, 283–303. https://doi.org/https://doi.org/10.28945/3521
- Munakata, Y., Snyder, H. R., & Chatham, C. H. (2012). Developing cognitive control: Three key transitions. Current Directions in Psychological Science, 21(2), 71–77. https://doi.org/https://doi.org/10.1177/0963721412436807
- 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/https://doi.org/10.1007/s40299-019-00438-4
- Niessen, C., & Lang, J. W. B. (2021). Cognitive control strategies and adaptive performance in a complex work task. Journal of Applied Psychology, 106(10), 1586–1552. https://doi.org/https://doi.org/10.1037/apl0000830
- Palts, T., & Pedaste, M. (2017, June 3–5). Tasks for assessing skills of computational thinking. In 2017 ACM Conference on Innovation and Technology in Computer Science Education, Bologna, Italy. https://doi.org/https://doi.org/10.1145/3059009.3072999
- Papert, S. (1980). Mindstorms: Children, computers and powerful ideas. Basic Books.
- Pelletier, K., Brown, M., Brooks, D. C., Mccormack, M., Reeves, J., Arbino, N., Bozkurt, A., Crawford, S., Czerniewicz, L., Gibson, R., Linder, K., Mason, J., & Mondelli, V. (2021). 2021 EDUCAUSE Horizon report, teaching and learning edition. https://www.learntechlib.org/p/219489/
- Popat, S., & Starkey, L. (2019). Learning to code or coding to learn? A systematic review. Computers & Education, 128, 365–376. https://doi.org/https://doi.org/10.1016/j.compedu.2018.10.005
- Raven, J. (2000). The Raven’s progressive matrices: Change and stability over culture and time. Cognitive Psychology, 41(1), 1–48. https://doi.org/https://doi.org/10.1006/cogp.1999.0735
- Relkin, E., de Ruiter, L. E., & Bers, M. U. (2021). Learning to code and the acquisition of computational thinking by young children. Computers & Education, 169, 104222. https://doi.org/https://doi.org/10.1016/j.compedu.2021.104222
- Riggins, T., Canada, K. L., & Botdorf, M. (2020). Empirical evidence supporting neural contributions to episodic memory development in early childhood: Implications for childhood amnesia. Child Development Perspectives, 14(1), 41–48. https://doi.org/https://doi.org/10.1111/cdep.12353
- Ruthmann, A., Heines, J. M., Greher, G. R., Laidler, P., & Saulters, C. (2010, March 10–13). Teaching computational thinking through musical live coding in Scratch. In SIGCSE’10 - Proceedings of the 41st ACM Technical Symposium on Computer Science Education, Milwaukee, WI. https://doi.org/https://doi.org/10.1145/1734263.1734384
- Sapounidis, T., Demetriadis, S., & Stamelos, I. (2015). Evaluating children performance with graphical and tangible robot programming tools. Personal and Ubiquitous Computing, 19(1), 225–237. https://doi.org/https://doi.org/10.1007/s00779-014-0774-3
- Scherer, R., Siddiq, F., & Sánchez Viveros, B. (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/https://doi.org/10.1037/edu0000314
- Sharma, K., Papavlasopoulou, S., & Giannakos, M. (2019). Coding games and robots to enhance computational thinking: How collaboration and engagement moderate children’s attitudes? International Journal of Child-Computer Interaction, 21, 65–76. https://doi.org/https://doi.org/10.1016/j.ijcci.2019.04.004
- Spanoudis, G., & Demetriou, A. (2020). Mapping mind-brain development: Towards a comprehensive theory. Journal of Intelligence, 8(2), 19. https://doi.org/https://doi.org/10.3390/jintelligence8020019
- 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 and Development, 67(3), 541–575. https://doi.org/https://doi.org/10.1007/s11423-018-9622-x
- Strawhacker, A., Verish, C., Shaer, O., & Bers, M. U. (2020). Designing with genes in early childhood: An exploratory user study of the tangible CRISPEE technology. International Journal of Child-Computer Interaction, 26, 100212. https://doi.org/https://doi.org/10.1016/j.ijcci.2020.100212
- Sullivan, A., & 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(1), 3–20. https://doi.org/https://doi.org/10.1007/s10798-015-9304-5
- Sullivan, A., & Bers, M. U. (2018). Dancing robots: Integrating art, music, and robotics in Singapore’s early childhood centers. International Journal of Technology and Design Education, 28(2), 325–346. https://doi.org/https://doi.org/10.1007/s10798-017-9397-0
- Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798. https://doi.org/https://doi.org/10.1016/j.compedu.2019.103798
- Troller-Renfree, S. v., Buzzell, G. A., & Fox, N. A. (2020). Changes in working memory influence the transition from reactive to proactive cognitive control during childhood. Developmental Science, 23(6), e12959. https://doi.org/https://doi.org/10.1111/desc.12959
- Wang, L., Li, Y., & Geng, F. (2021, April 7–9). Effects of coding learning on computational thinking and creative thinking in young children: Integrating cognitive control strategies.Poster Presentation at the Biennial Meeting of the Society for Research in Child Development (Virtual Meeting).
- Wiebe, E., London, J., Aksit, O., Mott, B. W., Boyer, K. E., & Lester, J. C. (2019, February 27–March 2). Development of a lean computational thinking abilities assessment for middle grades students. In 50th ACM Technical Symposium on Computer Science Education, Minneapolis, MN. https://doi.org/https://doi.org/10.1145/3287324.3287390
- Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/https://doi.org/10.1145/1118178.1118215
- Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 366(1881), 3717–3725. https://doi.org/https://doi.org/10.1098/rsta.2008.0118
- Wise, A., Chang, J., Duffy, T., & del Valle, R. (2004). The effects of teacher social presence on student satisfaction, engagement, and learning. Journal of Educational Computing Research, 31(3), 247–271. https://doi.org/https://doi.org/10.2190/V0LB-1M37-RNR8-Y2U1
- Witherspoon, E. B., Higashi, R. M., Schunn, C. D., Baehr, E. C., & Shoop, R. (2017). Developing computational thinking through a virtual robotics programming curriculum. ACM Transactions on Computing Education, 18(1), 1–20. https://doi.org/https://doi.org/10.1145/3104982
- Yanaoka, K., Moriguchi, Y., & Saito, S. (2020). Cognitive and neural underpinnings of goal maintenance in young children. Cognition, 203, 104378. https://doi.org/https://doi.org/10.1016/j.cognition.2020.104378
- 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/https://doi.org/10.1037/1089-2680.1.2.198
- Zhang, H. C., & Wang, X. P. (1989). Revision raven progressive matrices test in China. Acta Psychologica Sinica, 21(2), 9.