Έλεγχος Συσχέτισης της Αλγοριθμικής Σκέψης με την Ηλικία των Μαθητών/τριών Πρώτης Σχολικής Ηλικίας, στα Πλαίσια του Μαθήματος της Μελέτης Περιβάλλοντος

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Δημοσιευμένα: Δεκ 29, 2023
DOI: https://doi.org/10.12681/riste.34477
Λέξεις-κλειδιά:
αλγοριθμική σκέψη αξιολόγηση μελέτη περιβάλλοντος πρώτη σχολική ηλικία υπολογιστική σκέψη
Καλλιόπη Κανάκη
Δευτερoβάθμια Εκπαίδευση Νομού Ηρακλείου Κρήτης
https://orcid.org/0000-0001-5122-3595
Μιχαήλ Καλογιαννάκης
Πανεπιστήμιο Κρήτης
https://orcid.org/0000-0002-9124-2245
Περίληψη

Στις μέρες μας, η εξάπλωση των νέων τεχνολογιών έχει αλλάξει τον τρόπο που ζούμε, μαθαίνουμε και εργαζόμαστε, γεγονός που καθιστά επιβεβλημένη την καλλιέργεια δεξιοτήτων, όπως αυτών της υπολογιστικής σκέψης (ΥΣ), οι οποίες δεν αφορούν μόνο στη μελλοντική επαγγελματική σταδιοδρομία των σημερινών μαθητών/τριών, αλλά επιπλέον είναι εφαρμόσιμες στην καθημερινή ζωή των πολιτών των μοντέρνων κοινωνιών. Στην παρούσα εργασία παρουσιάζεται ένα εργαλείο αξιολόγησης βασικών δεξιοτήτων της ΥΣ μαθητών/τριών προσχολικής και πρώτης σχολικής ηλικίας, το οποίο εφαρμόστηκε σε σχετική μελέτη που διεξήχθη στο Ηράκλειο της Κρήτης κατά το σχολικό έτος 2018-2019 σε δείγμα 435 μαθητών/τριών. Ανάμεσα στα άλλα, ελέγχηκε και η συσχέτιση της αλγοριθμικής σκέψης – η οποία είναι θεμελιώδης δεξιότητα της ΥΣ – με την ηλικία των μαθητών/τριών της Α΄ και Β΄ τάξης του Δημοτικού. Τα αποτελέσματα όχι μόνο επιβεβαίωσαν την υπό εξέταση συσχέτιση, αλλά, επιπλέον, ανέδειξαν ότι η ηλικία αποτελεί προγνωστικό παράγοντα των επιπέδων της αλγοριθμικής σκέψης, προβάλλοντας την ανάγκη δημιουργίας αναπτυξιακά κατάλληλων εκπαιδευτικών πρακτικών καλλιέργειας δεξιοτήτων της ΥΣ.

Λεπτομέρειες άρθρου
  • Ενότητα
  • Άρθρο Ερευνητικό

Οι συγγραφείς διατηρούν τα πνευματικά δικαιώματα και παρέχουν στο περιοδικό το δικαίωμα της πρώτης δημοσίευσης μαζί με την αδειοδότηση της εργασίας με CC-BY-NC-SA, που επιτρέπει σε άλλους να μοιράζονται αυτή την εργασία με αναγνώριση του συγγραφικού δικαιώματος και την αρχική δημοσίευση σε αυτό το περιοδικό.

Λήψεις
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Αναφορές
Καλογιαννάκης, Μ., Γούπος, Θ., Ιμβριώτη, Δ., Ιωακειμίδου, Β. & Ριζάκη, Α. (2021). Πρόγραμμα Σπουδών Μελέτης Περιβάλλοντος. Στο πλαίσιο της Πράξης «Αναβάθμιση των Προγραμμάτων Σπουδών και Δημιουργία Εκπαιδευτικού Υλικού Πρωτοβάθμιας και Δευτεροβάθμιας Εκπαίδευσης», Αθήνα: Ινστιτούτο Εκπαιδευτικής Πολιτικής. Ανακτήθηκε στις 24/3/2023, από:
http://iep.edu.gr/el/nea-ps-provoli
Acevedo-Borrega, J., Valverde-Berrocoso, J., & Garrido-Arroyo, M. D. C. (2022). Computational thinking and educational technology: A scoping review of the literature. Education Sciences, 12(1), 39. https://doi.org/10.3390/educsci12010039.
Ardoin, N. M., & Bowers, A. W. (2020). Early childhood environmental education: A systematic review of the research literature. Educational Research Review, 31, 100353.
https://doi.org/10.1016/j.edurev.2020.100353.
Asbell-Clarke, J., Rowe, E., Almeda, V., Edwards, T., Bardar, E., Gasca, S., ... & Scruggs, R. (2021). The development of students’ computational thinking practices in elementary-and middle-school classes using the learning game, Zoombinis. Computers in Human Behavior, 115, 106587.
https://doi.org/10.1016/j.chb.2020.106587.
Bempechat, J., & Shernoff, D. J. (2012). Parental influences on achievement motivation and student engagement. Handbook of research on student engagement, 315-342.
https://doi.org/10.1007/978-1-4614-2018-7_15.
Bers, M. U., 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/10.1016/j.compedu.2019.04.013.
Breien, F. S., & Wasson, B. (2021). Narrative categorization in digital game‐based learning: Engagement, motivation & learning. British Journal of Educational Technology, 52(1), 91-111. https://doi.org//10.1111/bjet.13004.
Burton, B. A. (2010). Encouraging Algorithmic Thinking Without a Computer. Olympiads in Informatics, 4. Ανακτήθηκε στις 2/3/2023, από: https://ioinformatics.org/journal/INFOL053.pdf.
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.
Chongo, S., Osman, K., & Nayan, N. A. (2020). Level of Computational Thinking Skills among Secondary Science Student: Variation across Gender and Mathematics Achievement. Science Education International, 31(2), 159-163. https://doi.org/10.33828/sei.v31.i2.4.
Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education. Routledge. ISBN 0-203-02905-4. e-book ISBN 9780203029053. https://doi.org/10.4324/9780203029053.
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), 4111.
https://doi.org/10.3390/app11094111.
Dagienė, V., & Futschek, G. (2008). Bebras international contest on informatics and computer literacy: Criteria for good tasks. In Informatics Education-Supporting Computational Thinking: Third International Conference on Informatics in Secondary Schools-Evolution and Perspectives, ISSEP 2008 Torun Poland, July 1-4, 2008 Proceedings 3 (pp. 19-30). Springer Berlin Heidelberg.
https://doi.org/10.1007/978-3-540-69924-8_2.
del Olmo-Muñoz, J., Cózar-Gutiérrez, R., & González-Calero, J. A. (2020). Computational thinking through unplugged activities in early years of Primary Education. Computers & Education, 150, 103832. https://doi.org/10.1016/j.compedu.2020.103832.
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.
Ferrari, A., Poggi, A., & Tomaiuolo, M. (2016). Object oriented puzzle programming. Mondo Digitale, 15(64), 2016-3. ISBN: 9788898091447.
Figueiredo, M., Amante, S., Gomes, H. M. D. S. V., Gomes, M. A., Rego, B., Alves, V., & Duarte, R. P. (2021). Algorithmic thinking in early childhood education: Opportunities and supports in the portuguese context. In EDULEARN21 Proceedings (pp. 9339-9348). IATED.
https://doi.org/10.21125/edulearn.2021.1885.
Fragkiadaki, G., & Ravanis, K. (2021). The unity between intellect, affect, and action in a child's learning and development in science. Learning, Culture and Social Interaction, 29, 100495.
https://doi.org/10.1016/j.lcsi.2021.100495.
Freeman, H. B. (2002). Trade epidemic: the impact of the mad cow crisis on EU-US relations. BC Int'l Comp. L. Rev., 25, 343. Ανακτήθηκε στις 10/03/2023, από:
http://nationalaglawcenter.org/wp-content/uploads/assets/bibarticles/freeman_trade.pdf.
Freina, L., Bottino, R., & Ferlino, L. (2019). Fostering Computational Thinking skills in the Last Years of Primary School. International Journal of Serious Games, 6(3), 101-115.
https://doi.org/10.17083/ijsg.v6i3.304.
Friendly, M. (2000, April). Visualizing categorical data: Data, stories, and pictures. In Proceedings of the Twenty-Fifth Annual SAS Users Group International Conference. Ανακτήθηκε στις 12/03/2023, από: https://www.datavis.ca/papers/sugi/vcdstory/vcdstory.pdf.
Futschek, G. (2006). Algorithmic thinking: the key for understanding computer science. In Informatics Education–The Bridge between Using and Understanding Computers: International Conference in Informatics in Secondary Schools–Evolution and Perspectives, ISSEP 2006, Vilnius, Lithuania, November 7-11, 2006. Proceedings (pp. 159-168). Springer Berlin Heidelberg.
https://doi.org/10.1007/11915355_15.
Gallagher, A. C. (2012, June). Jigsaw puzzles with pieces of unknown orientation. In 2012 IEEE Conference on computer vision and pattern recognition (pp. 382-389). IEEE.
https://ieeexplore.ieee.org/abstract/document/6247699.
Grizioti, M., & Kynigos, C. (2021, June). Children as players, modders, and creators of simulation games: A design for making sense of complex real-world problems: Children as players, modders and creators of simulation games. In Interaction Design and Children (pp. 363-374). Ανακτήθηκε στις 12/03/2023, από: https://doi.org/10.1145/3459990.3460706.
Grover, S. (2017). Assessing algorithmic and computational thinking in K-12: Lessons from a middle school classroom. Emerging research, practice, and policy on computational thinking, 269-288.
https://doi.org/10.1007/978-3-319-52691-1_17.
Grover, S., Biswas, G., Dickes, A., Farris, A., Sengupta, P., Covitt, B., ... & Blikstein, P. (2020, June). Integrating STEM and computing in PK-12: Operationalizing computational thinking for STEM learning and assessment. In The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020 (Vol. 3). Ανακτήθηκε στις 20/3/2023, από:
https://par.nsf.gov/servlets/purl/10289291.
Grover, S., Fisler, K., Lee, I., & Yadav, A. (2020, February). Integrating computing and computational thinking into K-12 STEM learning. In Proceedings of the 51st ACM technical symposium on computer science education (pp. 481-482). https://doi.org/10.1145/3328778.3366970.
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/10.3102/0013189x12463051.
Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296-310.
https://doi.org/10.1016/j.compedu.2018.07.004.
Huroyan, V., Lerman, G., & Wu, H. T. (2020). Solving jigsaw puzzles by the graph connection Laplacian. SIAM Journal on Imaging Sciences, 13(4), 1717-1753.
https://doi.org/10.1137/19m1290760.
Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., ... & McElhaney, K. (2020). C2STEM: A system for synergistic learning of physics and computational thinking. Journal of Science Education and Technology, 29, 83-100. https://doi.org/10.1007/s10956-019-09804-9.
Janakiraman, S., Watson, S. L., Watson, W. R., & Newby, T. (2021). Effectiveness of digital games in producing environmentally friendly attitudes and behaviors: A mixed methods study. Computers & Education, 160, 104043. https://doi.org/10.1016/j.compedu.2020.104043.
Janke, E., Brune, P., & Wagner, S. (2015, May). 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.
Jiang, B., & Li, Z. (2021). Effect of Scratch on computational thinking skills of Chinese primary school students. Journal of Computers in Education, 8(4), 505-525.
https://doi.org/10.1007/s40692-021-00190-z.
Kalogiannakis, M., Papadakis, S., & Zourmpakis, A. I. (2021). Gamification in science education. A systematic review of the literature. Education Sciences, 11(1), 22.
https://doi.org/10.3390/educsci11010022.
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. (2022a). Assessing algorithmic thinking skills in relation to age in early childhood STEM education. Education Sciences, 12(6), 380.
https://doi.org/10.3390/educsci12060380.
Kanaki, K., & Kalogiannakis, M. (2022b). Assessing Algorithmic Thinking Skills in Relation to Gender in Early Childhood. Educational Process: International Journal, 11(2), 44-59.
https://doi.org/10.22521/edupij.2022.112.3.
Kanaki, K., & Kalogiannakis, M. (2023). Sample design challenges: an educational research paradigm. International Journal of Technology Enhanced Learning, 15(3), 266-285.
https://doi.org/10.1504/ijtel.2023.10055808.
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. Int. J. Interact. Mob. Technol. IJIM, 16, 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 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
Kiss, G., & Arki, Z. (2017). The influence of game-based programming education on the algorithmic thinking. Procedia-Social and Behavioral Sciences, 237, 613-617.
https://doi.org/10.1016/j.sbspro.2017.02.020.
Labusch, A., Eickelmann, B., & Vennemann, M. (2019). Computational thinking processes and their congruence with problem-solving and information processing. Computational thinking education, 65-78. https://doi.org/10.1007/978-981-13-6528-7_5.
Li, Y., Schoenfeld, A. H., diSessa, A. A., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). On computational thinking and STEM education. Journal for STEM Education Research, 3, 147-166. https://doi.org/10.1007/s41979-020-00044-w.
Lodi, M., & Martini, S. (2021). Computational thinking, between Papert and Wing. Science & Education, 30(4), 883-908. https://doi.org/10.1007/s11191-021-00202-5.
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.
Malyn-Smith, J., Lee, I. A., Martin, F., Grover, S., Evans, M. A., & Pillai, S. (2018, June). Developing a framework for computational thinking from a disciplinary perspective. In Proceedings of the international conference on computational thinking education (Vol. 5). Ανακτήθηκε στις 15/3/2023, από: https://d-miller.github.io/DRK12/topic1/7440.pdf.
McManis, L. D., & Gunnewig, S. B. (2012). Finding the education in educational technology with early learners. Young children, 67(3), 14-24. Ανακτήθηκε στις 14/3/2023, από:
https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1be1d42ffc83f56f72fdbd205952100922d328b2.
Merry, B., Gallotta, M., & Hultquist, C. (2008). Challenges in running a computer olympiad in South Africa. Olympiads in Informatics, 2, 105-114. Ανακτήθηκε στις 12/3/2023, από:
https://ioi.te.lv/oi/pdf/INFOL026.pdf.
Misra, R., Eyombo, L., & Phillips, F. T. (2022). Benefits and Challenges of Using Educational Games. In Research Anthology on Developments in Gamification and Game-Based Learning (pp. 1560-1570). IGI Global. https://doi.org/10.4018/978-1-6684-3710-0.ch075.
Nafea, I. T. (2018). Machine learning in educational technology. Machine learning-advanced techniques and emerging applications, 175-183. https://doi.org/10.5772/intechopen.72906.
NGSS Lead States. Next Generation Science Standards: for States, by States; The National Academies Press: Washington, DC, USA, 2013; Ανακτήθηκε στις 30/01/2023, από:
https://epsc.wustl.edu/seismology/book/presentations/2014_Promotion/NGSS_2013.pdf.
Nordby, S. K., Bjerke, A. H., & Mifsud, L. (2022). Computational thinking in the primary mathematics classroom: A systematic review. Digital Experiences in Mathematics Education, 8(1), 27-49.
https://doi.org/10.1007/s40751-022-00102-5.
Paikin, G., & Tal, A. (2015). Solving multiple square jigsaw puzzles with missing pieces. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4832-4839).
https://doi.org/10.1109/cvpr.2015.7299116.
Petousi, V., & Sifaki, E. (2020). Contextualising harm in the framework of research misconduct. Findings from discourse analysis of scientific publications. International Journal of Sustainable Development, 23(3-4), 149-174. https://doi.org/10.1504/IJSD.2020.10037655.
Piatti, A., Adorni, G., El-Hamamsy, L., Negrini, L., Assaf, D., Gambardella, L., & Mondada, F. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166.
https://doi.org/10.1016/j.chbr.2021.100166.
Pomeranz, D., Shemesh, M., & Ben-Shahar, O. (2011, June). A fully automated greedy square jigsaw puzzle solver. In CVPR 2011 (pp. 9-16). IEEE. https://doi.org/10.1109/CVPR.2011.5995331.
Poulakis, E., & Politis, P. (2021). Computational thinking assessment: literature review. Research on E-Learning and ICT in Education: Technological, Pedagogical and Instructional Perspectives, 111-128. https://doi.org/10.1007/978-3-030-64363-8_7.
Qian, M., & Clark, K. R. (2016). Game-based Learning and 21st century skills: A review of recent research. Computers in human behavior, 63, 50-58. https://doi.org/10.1016/j.chb.2016.05.023.
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/10.1016/j.compedu.2021.104222.
Rijke, W. J., Bollen, L., Eysink, T. H., & Tolboom, J. L. (2018). Computational thinking in primary school: An examination of abstraction and decomposition in different age groups. Informatics in education, 17(1), 77-92. https://doi.org/10.15388/infedu.2018.05.
Román-González, M., Moreno-León, J., & Robles, G. (2019). Combining assessment tools for a comprehensive evaluation of computational thinking interventions. Computational thinking education, 79-98. 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.
Rompapas, D., Steven, Y., & Chan, J. (2021). A Hybrid Approach to Teaching Computational Thinking at a K-1 and K-2 Level. In Proceedings of the CTE-STEM 2021: 5th APSCE International Conference on Computational Thinking and STEM Education 2021 (pp. 26-31), Singapore: National Institute of Education. ISSN 2737-5641.
Rowe, E., Almeda, M. V., Asbell-Clarke, J., Scruggs, R., Baker, R., Bardar, E., & Gasca, S. (2021). Assessing implicit computational thinking in Zoombinis puzzle gameplay. Computers in Human Behavior, 120, 106707. https://doi.org/10.1016/j.chb.2021.106707.
Rushton, S., Juola-Rushton, A., & Larkin, E. (2010). Neuroscience, play and early childhood education: Connections, implications and assessment. Early Childhood Education Journal, 37, 351-361.
https://doi.org/10.1007/s10643-009-0359-3.
Sanford, J. F., & Naidu, J. T. (2016). Computational thinking concepts for grade school. Contemporary Issues in Education Research (CIER), 9(1), 23-32. https://doi.org/10.19030/cier.v9i1.9547.
Saqr, M., Ng, K., Oyelere, S. S., & Tedre, M. (2021). People, ideas, milestones: a scientometric study of computational thinking. ACM Transactions on Computing Education (TOCE), 21(3), 1-17.
https://doi.org/10.1145/3445984.
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.
Sigman, M., Peña, M., Goldin, A. P., & Ribeiro, S. (2014). Neuroscience and education: prime time to build the bridge. Nature neuroscience, 17(4), 497-502. https://doi.org/10.1038/nn.3672.
Sullivan, G. M. (2011). A primer on the validity of assessment instruments. Journal of graduate medical education, 3(2), 119-120. https://doi.org/10.4300/jgme-d-11-00075.1.
Sung, W., Ahn, J., & Black, J. B. (2017). Introducing computational thinking to young learners: Practicing computational perspectives through embodiment in mathematics education. Technology, Knowledge and Learning, 22, 443-463.
https://doi.org/10.1007/s10758-017-9328-x.
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.
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., ... & 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.
Tengler, K., Kastner-Hauler, O., & Sabitzer, B. (2021, April). Enhancing Computational Thinking Skills using Robots and Digital Storytelling. In CSEDU (1) (pp. 157-164).
https://doi.org/10.5220/0010477001570164.
Tsarava, K., Moeller, K., Román-González, M., Golle, J., Leifheit, L., Butz, M. V., & Ninaus, M. (2022). A cognitive definition of computational thinking in primary education. Computers & Education, 179, 104425. https://doi.org/10.1016/j.compedu.2021.104425.
Vaz, S., Falkmer, T., Passmore, A. E., Parsons, R., & Andreou, P. (2013). The case for using the repeatability coefficient when calculating test–retest reliability. PloS one, 8(9), e73990.
https://doi.org/10.1371/journal.pone.0073990.
Vujičić, L., Jančec, L., & Mezak, J. (2021). Development of algorithmic thinking skills in early and preschool education. In EDULEARN21 Proceedings (pp. 8152-8161). IATED.
https://doi.org/10.21125/edulearn.2021.1650.
Washer, P. (2006). Representations of mad cow disease. Social science & medicine, 62(2), 457-466.
https://doi.org/10.1016/j.socscimed.2005.06.001.
Waterman, K. P., Goldsmith, L., & Pasquale, M. (2020). Integrating computational thinking into elementary science curriculum: An examination of activities that support students’ computational thinking in the service of disciplinary learning. Journal of Science Education and Technology, 29, 53-64. https://doi.org/10.1007/s10956-019-09801-y.
Werner, L., Denner, J., & Campe, S. (2014). Children programming games: A strategy for measuring computational learning. ACM Transactions on Computing Education (TOCE), 14(4), 1-22.
https://doi.org/10.1145/2677091.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
https://doi.org/10.1145/1118178.1118215.
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725. https://doi.org/10.1109/ipdps.2008.4536091.
Wing, J. (2011). Research notebook: Computational thinking—What and why. The link magazine, 6, 20-23. Ανακτήθηκε στις 15/1/2023, από:
https://people.cs.vt.edu/~kafura/CS6604/Papers/CT-What-And-Why.pdf.
Yang, D., Baek, Y., Ching, Y. H., Swanson, S., Chittoori, B., & Wang, S. (2021). Infusing Computational Thinking in an Integrated STEM Curriculum: User Reactions and Lessons Learned. European Journal of STEM Education, 6(1), 4. https://doi.org/10.20897/ejsteme/9560.
Zevenbergen, R., & Logan, H. (2008). Computer use by preschool children: Rethinking practice as digital natives come to preschool. Australasian Journal of Early Childhood, 33(1), 37-44. ISSN 0312-5033.
Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education, 141, 103607. https://doi.org/10.1016/j.compedu.2019.103607.
Zhao, Y. X., Su, M. C., Chou, Z. L., & Lee, J. (2007, January). A puzzle solver and its application in speech descrambling. In WSEAS International Conference on Computer Engineering and Applications (pp. 171-176). Ανακτήθηκε στις 15/1/2023, από:
https://www.researchgate.net/profile/Mu-Chun Su/publication/234794266_A_puzzle_solver_and_its_application_in_speech_descrambling.