Abstract

Background/purpose – In terms of computational thinking core facets, algorithmic thinking is a key competency applicable not only in Computer Science but also in aspects of daily life. Considering the global phenomenon of gender stereotypes with regards to the academic and professional orientation in STEM fields, we focused on investigating the level of students’ algorithmic thinking skills by gender in early childhood. This article provides evidence of research implemented under the umbrella of quantitative methodology, employing an innovative assessment tool constructed to meet the requirements of the study. The findings obtained could facilitate researchers and policymakers to support equity in learning opportunities, starting from the first stage of compulsory education.

Keywords: computational thinking, algorithmic thinking, gender, early childhood, game-based learning

Acharya, A. S., Prakash, A., Saxena, P., & Nigam, A. (2013). Sampling: Why and how of it. Indian Journal of Medical Specialties, 4(2), 330-333.

Ampartzaki, M., Kalogiannakis, M., Papadakis, S., & Giannakou, V. (2022). Perceptions About STEM and the Arts: Teachers’, Parents’ Professionals’ and Artists’ Understandings About the Role of Arts in STEM Education. In St. Papadakis & M. Kalogiannakis (Eds.), STEM, Robotics, Mobile Apps in Early Childhood and Primary Education. Lecture Notes in Educational Technology (pp. 601-624). Springer https://doi.org/10.1007/978-981-19-0568-1_25

Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, Article 106185. https://doi.org/10.1016/j.chb.2019.106185

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, Article 105954. https://doi.org/10.1016/j.chb.2019.03.018

Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23. https://www.learningandleading-digital.com/learning_leading/20110304?pm=2&pg=22#pg22

Bempechat, J., & Shernoff, D. J. (2012). Parental influences on achievement motivation and student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 315-342). Springer. https://doi.org/10.1007/978-1-4614-2018-7_15

Bers, M. U. (2018). Coding and computational thinking in early childhood: the impact of ScratchJr in Europe. European Journal of STEM Education, 3(3), Article 8. https://doi.org/10.20897/ejsteme/3868

Card, D., & Payne, A. A. (2021). High school choices and the gender gap in STEM. Economic Inquiry, 59(1), 9-28. https://doi.org/10.3386/w23769

Charlesworth, T. E., & Banaji, M. R. (2019). Gender in science, technology, engineering, and mathematics: Issues, causes, solutions. Journal of Neuroscience, 39(37), 7228-7243. https://doi.org/10.1523/jneurosci.0475-18.2019

Chatzopoulos, A., Kalogiannakis, M., Papoutsidakis, M., Psycharis, S., & Papachristos, D. (2020). Measuring the Impact on Student’s Computational Thinking Skills Through STEM and Educational Robotics Project Implementation. In M. Kalogiannakis, & S. Papadakis (Eds.), Handbook of Research on Tools for Teaching Computational Thinking in P-12 Education (pp. 234-284). IGI Global. https://doi.org/10.4018/978-1-7998-4576-8.ch010

Clarke-Midura, J., Silvis, D., Shumway, J. F., Lee, V. R., & Kozlowski, J. S. (2021). Developing a kindergarten computational thinking assessment using evidence-centered design: the case of algorithmic thinking. Computer Science Education, 31(2), 117-140. https://doi.org/10.1080/08993408.2021.1877988

Cochran, W. G. (2007). Sampling techniques. Wiley.

Cohen, L., Manion, L., & Morrison, K. (2013). Research methods in education. Routledge.

Criollo-C, S., Guerrero-Arias, A., Jaramillo-Alcázar, Á., & Luján-Mora, S. (2021). Mobile learning technologies for education: Benefits and pending issues. Applied Sciences, 11(9), Article 4111. https://doi.org/10.3390/app11094111

Cunningham, B. C., Hoyer, K. M., & Sparks, D. (2015). Gender Differences in Science, Technology, Engineering, and Mathematics (STEM) Interest, Credits Earned, and NAEP Performance in the 12th Grade. Stats in Brief. NCES 2015-075. National Center for Education Statistics. https://nces.ed.gov/pubs2015/2015075.pdf

Darejeh, A., & Singh, D. (2013). A review on user interface design principles to increase software usability for users with less computer literacy. Journal of Computer Science, 9(11), 1443-1450. https://doi.org/10.3844/jcssp.2013.1443.1450

Doherty, M. J., Wimmer, M. C., Gollek, C., Stone, C., & Robinson, E. J. (2021). Piecing together the puzzle of pictorial representation: How jigsaw puzzles index metacognitive development. Child Development, 92(1), 205-221. https://doi.org/10.1111/cdev.13391

Durak, H. Y., & Saritepeci, M. (2018). Analysis of the relation between computational thinking skills and various variables with the structural equation model. Computers & Education, 116, 191-202. https//:doi.org/10.1016/j.compedu.2017.09.004

Elfil, M., & Negida, A. (2017). Sampling methods in clinical research; an educational review. Emergency, 5(1), e52. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325924/.

Emerson, A., Cloude, E. B., Azevedo, R., & Lester, J. (2020). Multimodal learning analytics for game‐based learning. British Journal of Educational Technology, 51(5), 1505-1526. https://doi.org/10.1111/bjet.12992.

Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 215-217. https://doi.org/10.15406/bbij.2017.05.00149

Ferrari, A., Poggi, A., & Tomaiuolo, M. (2016). Object oriented puzzle programming. Mondo Digitale, 15, 64. https://mondodigitale.aicanet.net/2016-3/DidamaticaSessioni/Programmazione/paper_39.pdf

Figueiredo, M. P., Amante, S., Gomes, H. M. D. S. V., Gomes, C. A., Rego, B., Alves, V., & Duarte, R. P. (2021). Algorithmic Thinking in Early Childhood Education: Opportunities and Supports in the Portuguese Context. In L. Gómez Chova, A. López Martínez, & I. Candel Torres (Eds.), EduLearn 2021 Proceedings. 13th International Conference on Education and New Learning Technologies (pp. 9339-9348). IATED. https://doi.org/10.21125/edulearn.2021.1885

Guggemos, J. (2021). On the predictors of computational thinking and its growth at the high-school level. Computers & Education, 161, Article 104060. https://doi.org/10.1016/j.compedu.2020.104060

Güler, Ç. (2021). Algorithmic thinking skills without computers for prospective computer science teachers. Kuramsal Eğitimbilim Dergisi [Journal of Theoretical Educational Science], 14(4), 570-585. http://doi.org/10.30831/akukeg.892869

Huang, W., & Looi, C.-K. (2021). A critical review of literature on “unplugged” pedagogies in K-12 computer science and computational thinking education. Computer Science Education, 31(1), 83-111. https://doi.org/10.1080/08993408.2020.1789411

Janke, E., Brune, P., & Wagner, S. (2015). Does outside-in teaching improve the learning of object-oriented programming? In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (Vol. 2, pp. 408-417). IEEE. https://doi.org/10.1109/icse.2015.173

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://academic-publishing.org/index.php/ejel/article/view/1748

Jiang, T., Chen, J.-G., & Wu, Y.-Y. (2021). Impact Of Instruction On Science Performance: Learning Initiative As A Mediator And Gender As A Limited Moderator. Journal of Baltic Science Education, 20(1), 50-66. https://doi.org/10.33225/jbse/21.20.50

Kahn, S., & Ginther, D. (2017). Women and STEM (No. w23525). National Bureau of Economic Research. https://www.nber.org/papers/w23525

Kallia, M., van Borkulo, S. P., Drijvers, P., Barendsen, E., & Tolboom, J. (2021). Characterising computational thinking in mathematics education: a literature-informed Delphi study. Research in Mathematics Education, 23(2), 159-187. https://doi.org/10.1080/14794802.2020.1852104

Kalogiannakis, M., & Papadakis, S. (2022). Preparing Greek Pre-service Kindergarten Teachers to Promote Creativity: Opportunities Using Scratch and Makey Makey. In K.-J, Murcia, C., Campbell, M.-M. Joubert & S. Wilson (Eds.), Children’s Creative Inquiry in STEM. Sociocultural Explorations of Science Education (Vol. 25, pp. 347-354). Springer, https://doi.org/10.1007/978-3-030-94724-8_20

Kanaki, K., & Kalogiannakis, M. (2018). Introducing fundamental object-oriented programming concepts in preschool education within the context of physical science courses. Education and Information Technologies, 23(6), 2673-2698. https://doi.org/10.1007/s10639-018-9736-0

Kanaki, K., & Kalogiannakis, M. (2019). Assessing computational thinking skills at first stages of schooling. In Proceedings of the 2019 3rd International Conference on Education and E-Learning (pp. 135-139). ACM. https://doi.org/10.1145/3371647.3371651

Kanaki, K., Kalogiannakis, M., Poulakis, E., & Politis, P. (2022). Employing Mobile Technologies to Investigate the Association Between Abstraction Skills and Performance in Environmental Studies in Early Primary School. International Journal of Interactive Mobile Technologies, 16(6), 241-249. https://doi.org/10.3991/ijim.v16i06.28391

Kanaki, K., Kalogiannakis, M., & Stamovlasis, D. (2020). Assessing Algorithmic Thinking Skills in Early Childhood Education: Evaluation in Physical and Natural Science Courses. In M. Kalogiannakis & S. Papadakis (Eds.), Handbook of Research on Tools for Teaching Computational Thinking in P-12 Education (pp. 104-139). IGI Global. https://doi.org/10.4018/978-1-7998-4576-8.ch005

Kastriti, E., Kalogiannakis, M., Psycharis, S., & Vavougios, D. (2022). The teaching of Natural Sciences in kindergarten based on the principles of STEM and STEAM approach. Advances in Mobile Learning Educational Research (AMLER), 2(1), 268-277. https://doi.org/10.25082/AMLER.2022.01.011

Lee, I., Grover, S., Martin, F., Pillai, S., & Malyn-Smith, J. (2020). Computational thinking from a disciplinary perspective: Integrating computational thinking in K-12 science, technology, engineering, and mathematics education. Journal of Science Education and Technology, 29(1), 1-8. https://doi.org/10.1007/s10956-019-09803-w

Legewie, J., & DiPrete, T. (2012). High School Environments, STEM Orientations, and the Gender Gap in Science and Engineering Degrees. SSRN. https://doi.org/10.2139/ssrn.2008733

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, Article 101011. https://doi.org/10.1016/j.elerap.2020.101011

Lin, Y., & Weintrop, D. (2021). The landscape of Block-based programming: Characteristics of block-based environments and how they support the transition to text-based programming. Journal of Computer Languages, 67, Article 101075. https://doi.org/10.1016/j.cola.2021.101075

Liou, P.-Y., Wang, C.-L., Lin, J. J., & Areepattamannil, S. (2020). Assessing students’ motivational beliefs about learning science across grade level and gender. The Journal of Experimental Education, 89(4), 605-624. https://doi.org/10.1080/00220973.2020.1721413

LoBue, V., Bloom Pickard, M., Sherman, K., Axford, C., & DeLoache, J. S. (2013). Young children’s interest in live animals. British Journal of Developmental Psychology, 31(1), 57-69. https://doi.org/10.1111/j.2044-835x.2012.02078.x

Makarova, E., Aeschlimann, B., & Herzog, W. (2019). The gender gap in STEM fields: The impact of the gender stereotype of math and science on secondary students’ career aspirations. In Frontiers in Education, 2019(4), Article 60). https://doi.org/10.3389/feduc.2019.00060

Master, A., Cheryan, S., & Meltzoff, A. N. (2016). Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of Educational Psychology, 108(3), 424-437. http://dx.doi.org/10.1037/edu0000061

NGSS Lead States. (2013). Next generation science standards: For states, by states. The National Academies Press. https://nap.nationalacademies.org/catalog/18290/next-generation-science-standards-for-states-by-states

Nieto-Márquez, N. L., Baldominos, A., & Pérez-Nieto, M. Á. (2020). Digital Teaching Materials and Their Relationship with the Metacognitive Skills of Students in Primary Education. Education Sciences, 10(4), Article 113. https://doi.org/10.3390/educsci10040113

Nikolopoulou, K. (2022). Digital Technology in Early STEM Education: Exploring Its Supportive Role. In S. Papadakis and M. Kalogiannakis (Eds.), STEM, Robotics, Mobile Apps in Early Childhood and Primary Education. Lecture Notes in Educational Technology (pp. 103-115). Springer. https://doi.org/10.1007/978-981-19-0568-1_6

Normile, D. (2006). Getting women scientists back on the career track in Japan. Science, 311(5765), 1235-1236. https://doi.org/10.1126/science.311.5765.1235

Papavlasopoulou, S., Sharma, K., & Giannakos, M. N. (2020). Coding activities for children: Coupling eye-tracking with qualitative data to investigate gender differences. Computers in Human Behavior, 105, Article 105939. https://doi.org/10.1016/j.chb.2019.03.003

Petousi, V., & Sifaki, E. (2020). Contextualizing harm in the framework of research misconduct. Findings from a discourse analysis of scientific publications. International Journal of Sustainable Development, 23(3/4), 149-174, https://doi.org/10.1504/IJSD.2020.10037655

Poulakis, E., & Politis, P. (2021). Computational Thinking Assessment: Literature Review. In T. Tsiatsos, S. Demetriadis, A. Mikropoulos, & V. Dagdilelis (Eds.), Research on E-Learning and ICT in Education (pp. 111-128). Springer. https://doi.org/10.1007/978-3-030-64363-8_7

Raabe, I. J., Boda, Z., & Stadtfeld, C. (2019). The social pipeline: How friend influence and peer exposure widen the STEM gender gap. Sociology of Education, 92(2), 105-123. https://doi.org/10.1177/0038040718824095

Radford, A. W., Fritch, L. B., Leu, K., & Duprey, M. (2018). High School Longitudinal Study of 2009 (HSLS: 09) Second Follow-Up: A First Look at Fall 2009 Ninth-Graders in 2016. NCES 2018-139. National Center for Education Statistics. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2018139

Relkin, E., de Ruiter, L., & Bers, M. U. (2020). TechCheck: Development and validation of an unplugged assessment of computational thinking in early childhood education. Journal of Science Education and Technology, 29(4), 482-498. https://doi.org/10.1007/s10956-020-09831-x

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, Article 104222. https://doi.org/10.1016/j.compedu.2021.104222

Román-González, M., Moreno-León, J., & Robles, G. (2019). Combining assessment tools for a comprehensive evaluation of computational thinking interventions. In S.-C. Kong & H. Abelson (Eds.), Computational thinking education (pp. 79-98). Springer. https://doi.org/10.1007/978-981-13-6528-7_6

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

Santos, P. A. (2020). The Past, Present and Future of Serious Games and Gamification in STEM Learning. In M. Ludwig, S. Jablonski, A. Caldeira, & A. Moura (Eds.), Research on Outdoor STEM Education in the digiTal Age. Proceedings of the ROSETA Online Conference in June 2020 (pp. 147-154). WTM. https://doi.org/10.37626/ga9783959871440.0.18

Saujani, R. (2017). Girl code. Scientific American, 317(3), 66-69. https://doi.org/10.1038/scientificamerican0917-66

Sax, L. J., Lehman, K. J., Jacobs, J. A., Kanny, M. A., Lim, G., Monje-Paulson, L., & Zimmerman, H. B. (2017). Anatomy of an enduring gender gap: The evolution of women’s participation in computer science. The Journal of Higher Education, 88(2), 258-293. https://doi.org/10.1080/00221546.2016.1257306

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

Stein, C. (2004). Botball robotics and gender differences in middle school teams. In 2004 Annual Conference (pp. 9.262.1-9.262.10). ASEE. https://doi.org/10.18260/1-2--13534

Stoet, G., & Geary, D. C. (2018). The gender-equality paradox in science, technology, engineering, and mathematics education. Psychological Science, 29(4), 581-593. https://doi.org/10.1177/0956797617741719

Tan, C. Y., Lyu, M., & Peng, B. (2020). Academic benefits from parental involvement are stratified by parental socioeconomic status: A meta-analysis. Parenting, 20(4), 241-287. https://doi.org/10.1080/15295192.2019.1694836.

Tang, H., Xu, Y., Lin, A., Heidari, A. A., Wang, M., Chen, H., Luo, Y., & Li, C. (2020). Predicting green consumption behaviors of students using efficient firefly grey wolf-assisted K-nearest neighbor classifiers. IEEE Access, 8, 35546-35562. https://doi.org/10.1109/ACCESS.2020.2973763.

Tselegkaridis, S., & Sapounidis, T. (2022). A Systematic Literature Review on STEM Research in Early Childhood. In S. Papadakis and M. Kalogiannakis (Eds.), STEM, Robotics, Mobile Apps in Early Childhood and Primary Education. Lecture Notes in Educational Technology (pp. 117-134). Springer. https://doi.org/10.1007/978-981-19-0568-1_7

Vincent‐Ruz, P., & Schunn, C. D. (2017). The increasingly important role of science competency beliefs for science learning in girls. Journal of Research in Science Teaching, 54(6), 790-822. https://doi.org/10.1002/tea.21387

Vujičić, L., Jančec, L., & Mezak, J. (2021). Development of algorithmic thinking skills in early and preschool education. In L. Gómez Chova, A. López Martínez, & I. Candel Torres (Eds.), Proceedings of EDULEARN21 Conference (Vol. 5). IATED. https://doi.org/10.21125/edulearn.2021.1650

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education & Technology, 25(1), 127-147

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

Zhao, L., Liu, X., Wang, C., & Su, Y.-S. (2022). Effect of different mind mapping approaches on primary school students’ computational thinking skills during visual programming learning. Computers & Education, 181, Article 104445. https://doi.org/10.1016/j.compedu.2022.104445.