Η Μηχανική Μάθηση στο Γυμνάσιο: Μια Διδακτική Παρέμβαση Εκπαίδευσης Μοντέλων Εικόνων με το Machine Learning for Kids

¨Έρευνα για την Εκπαίδευση στις Φυσικές Επιστήμες και την Τεχνολογία, Τόμος 5, Αρ. 1 (2025)
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Published: Dec 29, 2025
DOI: https://doi.org/10.12681/riste.38622
CHRYSOULA XIXI
Δημοτικό Σχολείο Λεωνιδίου
https://orcid.org/0009-0007-1197-1063
Αργυρώ Βλαχοδημητροπούλου
Γυμνάσιο με Λυκειακές Τάξεις Κέας
https://orcid.org/0009-0006-9035-4401
Abstract

Η μάθηση είναι πυλώνας ανάπτυξης, καθορίζοντας την πρόοδο ατόμων και κοινωνιών. Στον σύγχρονο κόσμο, η Τεχνητή Νοημοσύνη (ΤΝ) διαδραματίζει καθοριστικό ρόλο, φέρνοντας επανάσταση σε πλήθος εφαρμογών. Η εκπαίδευση στη Μηχανική Μάθηση (ΜΜ) καθίσταται αναγκαία, ειδικά για τους νέους, ώστε να κατανοήσουν τη λειτουργία και τη σημασία της τεχνολογίας. Στη μελέτη μας βασιστήκαμε σε έρευνες που χρησιμοποίησαν εργαλεία και πλατφόρμες ΜΜ για τη διδασκαλία των βασικών αρχών της στην τυπική, μη τυπική και άτυπη εκπαίδευση. Σχεδιάσαμε και εφαρμόσαμε ένα εργαστηριακό μάθημα με το Machine Learning for Kids για να εμπλακούν οι μαθητές/τριες στον τρόπο που μαθαίνουν οι μηχανές. Ερευνήσαμε αν μεταβάλλονται οι αντιλήψεις τους για την ΤΝ και τη ΜΜ και αν αναπτύσσουν δεξιότητες υπολογιστικής σκέψης. Υποστηρίζουμε ότι η ένταξη της ΜΜ στα προγράμματα σπουδών συμβάλλει στην κατανόηση των βασικών αρχών της, στην ανάδειξη της επιστήμης πίσω από τις σύγχρονες εφαρμογές της και στην ανάπτυξη υπολογιστικών δεξιοτήτων των μαθητών/τριών παρούσας έρευνας.

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References
Creswell, J. (2016). Η έρευνα στην εκπαίδευση: σχεδιασμός, διεξαγωγή και αξιολόγηση της ποσοτικής και ποιοτικής έρευνας (Ν. Kουβαράκου, Μετ.· 2η εκδ.). Ίων. (Το πρωτότυπο έργο «Educational research: Planning, conducting, and evaluating quantitative and qualitative research»(5η εκδ.), δημοσιεύτηκε το 2014). ISBN 9789605082017.
Υπουργική Απόφαση 49728/Δ1/2023. (2023). Πρόγραμμα Σπουδών για το μάθημα Πληροφορική και Τεχνολογίες της Πληροφορίας και των Επικοινωνιών στο Δημοτικό Σχολείο. Εφημερίς της Κυβερνήσεως (ΦΕΚ 3022/Β/8-5-2023).
Aravantinos, S., Lavidas, K., Voulgari, I., Papadakis, S., Karalis, T., & Komis, V. (2024). Educational Approaches with AI in Primary School Settings: A Systematic Review of the Literature Available in Scopus. Education Sciences, 14(7), 744. https://doi.org/10.3390/educsci14070744
Burgsteiner, H., Kandlhofer, M., & Steinbauer, G. (2016). IRobot: Teaching the basics of artificial intelligence in high schools. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9864
Burkart, N., & Huber, M. F. (2021). A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research, 70, 245-317. https://doi.org/10.1613/jair.1.12228
Council of the European Union. (2021). Council resolution on a new European agenda for adult learning 2021–2030 (2021/C 504/02). Official Journal of the European Union. Ανακτήθηκε στις 10/7/2025 από: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32021G1214(01)
European Commission: Directorate-General for Education, Youth, Sport and Culture. (2024). International Computer and Information Literacy Study (ICILS) in Europe, 2023: main findings and educational policy implications. Publications Office of the European Union. https://data.europa.eu/doi/10.2766/5221263
Evangelista, I., Blesio, G., & Benatti, E. (2018). Why are we not teaching machine learning at high school? A proposal. In 2018 world engineering education forum-global engineering deans council (WEEF-GEDC) (pp. 1-6). IEEE. https://doi.org/10.1109/WEEF-GEDC.2018.8629750
Ezeamuzie, N. O., Leung, J. S., Fung, D. C., & Ezeamuzie, M. N. (2024). Educational policy as predictor of computational thinking: A supervised machine learning approach. Journal of Computer Assisted Learning, 40(6), 2872-2885. https://doi.org/10.1111/jcal.13041
Fraillon, J., Ainley, J., Schulz, W., Duckworth, D., & Friedman, T. (2019). IEA international computer and information literacy study 2018 assessment framework (p. 74). Springer Nature. https://doi.org/10.1007/978-3-030-19389-8
Hitron, T., Orlev, Y., Wald, I., Shamir, A., Erel, H., & Zuckerman, O. (2019). Can children understand machine learning concepts? The effect of uncovering black boxes. CHI ‘19: CHI Conference on Human Factors in Computing Systems, 415, 1-11. May 4 - 9, 2019, Glasgow, Scotland Uk. ACM. https://doi.org/10.1145/3290605.3300645
Hitron, T., Wald Ι., Erel Η., and Zuckerman Ο. (2018). Introducing children to machine learning concepts through hands-on experience. IDC ‘18: Proceedings of the 17th ACM Conference on Interaction Design and Children, 563-568, June 19 - 22, 2018, Trondheim Norway. ACM. https://doi.org/10.1145/3202185.3210776
Huang, C. J., Wu, T., Lu, J. T., Lin, B., Chang, D., Wang, P., Wang, M.-C., Lee, P. & Wang, W. (2021). Developing a medical artificial intelligence course for high school students. International Forum on Medical Imaging in Asia 2021, April 20, 2021, Taipei, Taiwan. SPIE. https://doi.org/10.1117/12.2590769
Kahn, K., & Winters, N. (2021). Constructionism and AI: A history and possible futures. British Journal of Educational Technology, 52(3), 1130-1142. https://doi.org/10.1111/bjet.13088
Karalekas, G., Vologiannidis, S., & Kalomiros, J. (2023). Teaching Machine Learning in K–12 Using Robotics. Education Sciences, 13(1), 67. https://doi.org/10.3390/educsci13010067
Kim, S., Jang, Y., Kim, W., Choi, S., Jung, H., Kim, S., & Kim, H. (2021). Why and what to teach: AI curriculum for elementary school. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15569-15576. https://doi.org/10.1609/aaai.v35i17.17833
Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424. https://doi.org/10.3390/su131810424
Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., Komis, V., & Karacapilidis, N. (2024). Determinants of Humanities and Social Sciences Students’ Intentions to Use Artificial Intelligence Applications for Academic Purposes. Information, 15(6), 314. https://doi.org/10.3390/info15060314
Long, D., Blunt, T., & Magerko, B. (2021). Co-designing AI literacy exhibits for informal learning spaces. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1-35. https://doi.org/10.1145/3476034
Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1-16. https://doi.org/10.1145/3313831.3376727
Martins, R. M., & Gresse Von Wangenheim, C. (2023). Findings on teaching machine learning in high school: A ten-year systematic literature review. Informatics in Education, 22(3), 421-440. https://doi.org/10.15388/infedu.2023.18
Mertala, P., Fagerlund, J., Lehtoranta, J., Mattila, E., & Korhonen, T. (2024). Finnish primary school students’ conceptions of machine learning. arXiv preprint. https://doi.org/10.48550/arXiv.2402.09582
Moreno-Guerrero, A. J., López-Belmonte, J., Marín-Marín, J. A., & Soler-Costa, R. (2020). Scientific development of educational artificial intelligence in Web of Science. Future Internet, 12(8), 124. https://doi.org/10.3390/fi12080124
Organisation for Economic Co-operation and Development (OECD)(2018). PISA 2021 mathematics framework (draft). OECD Publishing. Ανακτήθηκε στις 12/8/2024, από: PISA 2021 Mathematics Framework Draft.pdf
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. UNESCO. Ανακτήθηκε στις 4/8/2024, από: https://hdl.handle.net/20.500.12799/6533
Rodríguez-García, J. D., Moreno-León, J., Román-González, M., & Robles, G. (2019). Developing computational thinking at school with machine learning: an exploration. 2019 International symposium on computers in education (SIIE), November 21-23, 2019, Tomar, Portugal. IEEE. https://doi.org/10.1109/SIIE48397.2019.8970124
Rodríguez-García, J. D., Moreno-León, J., Román-González, M., & Robles, G. (2021). Evaluation of an Online Intervention to Teach Artificial Intelligence with LearningML to 10-16-Year-Old Students. SIGCSE ‘21: The 52nd ACM Technical Symposium on Computer Science Education, March 13 - 20, 2021, Virtual Event USA. ACM. https://doi.org/10.1145/3408877.3432393
Royal Society. (2017). Machine Learning: The Power and Promise of Computers That Learn by Example. Royal Society. Ανακτήθηκε στις 4/8/2024, από: https://royalsociety.org/~/media/policy/projects/machinelearning/publications/machine-learning-report.pdf
Sabuncuoglu, A. (2020). Designing one year curriculum to teach artificial intelligence for middle school. ITiCSE ‘20: Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education, 96-102. June 15 - 19, 2020, Trondheim, Norway. ACM. https://doi.org/10.1145/3341525.3387364
Sallow, A. B., Asaad, R. R., Ahmad, H. B., Abdulrahman, S. M., Hani, A. A., & Zeebaree, S. R. (2024). Machine Learning Skills To K–12. Journal of Soft Computing and Data Mining, 5(1), 132-141. Ανακτήθηκε στις 7/8/2024, από: https://publisher.uthm.edu.my/ojs/index.php/jscdm/article/view/17670
Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023). A systematic review of teaching and learning machine learning in K-12 education. Education and Information Technologies, 28(5), 5967-5997. https://doi.org/10.1007/s10639-022-11416-7
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/10.1037/edu0000314
Shah, D., Patel, D., Adesara, J., Hingu, P., & Shah, M. (2021). Exploiting the capabilities of blockchain and machine learning in education. Augmented Human Research (6)1, 1. https://doi.org/10.1007/s41133-020-00039-7
Tan, B., Jin, H. Y., & Cutumisu, M. (2024). The applications of machine learning in computational thinking assessments: a scoping review. Computer Science Education, 34(2), 193-221. https://doi.org/10.1080/08993408.2023.2245687
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE access, 9, 110558-110572. https://doi.org/10.1109/ACCESS.2021.3097962
United Nations Educational, Scientific and Cultural Organization (UNESCO)(2021). AI and education: Guidance for policy-makers. Paris: United Nations Educational, Scientific and Cultural Organization. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000376709
United Nations Educational, Scientific and Cultural Organization (UNESCO)(2023). Guidance for generative AI in education and research. https://doi.org/10.54675/EWZM9535
Voulgari, I., Zammit, M., Stouraitis, E., Liapis, A., & Yannakakis, G. (2021). Learn to machine learn: designing a game based approach for teaching machine learning to primary and secondary education students. IDC ‘21: Proceedings of the 20th Annual ACM Interaction Design and Children, 593-598, June 24 - 30, 2021, Athens Greece. ACM. https://doi.org/10.1145/3459990.3465176
Williams, R., Park, H. W., & Breazeal, C. (2019). A is for artificial intelligence: the impact of artificial intelligence activities on young children’s perceptions of robots. CHI ‘19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 447, 1-11. May 4 - 9, 2019, Glasgow, Scotland UΚ. ACM. https://doi.org/10.1145/3290605.3300677
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215