Using Machine Learning to Enhance Interaction and Creativity Among Children By Using The Scratch And Mblock Programming Languages and Many Different Kids’ Machine Learning Platforms For Designing A.I Programs
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Abstract
Artificial intelligence (AI) and machine learning (ML) technologies have experienced substantial growth in the last decade, affecting billions of individuals across all facets of contemporary life. This trend of AI's expanding influence is expected to persist. The increasing significance of AI and ML in computer science and society supports the integration of AI and ML principles at an early stage.ML can be made more approachable and interesting for children by utilizing beginner-friendly kids’ programming languages like scratch. We design models for incorporating machine learning techniques using scratch and mblock programming languages to recognize images and text. These models are created using web-based cloud service tools such as "Machine Learning for Kids" and "teachable machine," as well as programming using Scratch 3 programming language and mblock programming language. The proposed methodology is introduced to 5th and 4th grade classrooms in Alhijragroup education in Iraq through provided practical exercises involving the creation of models that can recognize images and text. The survey shows that the best practices for incorporating machine learning into 5th grade classrooms are so that students can actively participate in their own learning and develop their own unique ideas for artificial intelligence (A.I) programs using different platforms. Alhijragroup education students are familiarized with the concept of machine learning through the summer course provided to them by IOTKIDS Company in Iraq. The survey shows that the F parameter is confidence which means every increase in the importance of artificial intelligence (the independent axis X) is linked to a similar increase in the quality of education (the dependent axis Y). The correlation between the two axes is very strong. These output modules are designed by using a special computer equipped with GPU memory Nividia Geforce RTX 2060 6G, IBM SPSS Statistics and Microsoft Excel 2010 programs.
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[1] M. U. Bers and A. Sullivan, “Computer science education in early childhood: The case of scratchjr,” J. Inf. Technol. Educ. Innov. Pract., vol. 18, p. 113, 2019, doi: 10.28945/4437.
[2] J. V. Brummelen, V. Tabunshchyk, and T. Heng, “‘Alexa, Can I Program You?’: Student Perceptions of Conversational Artificial Intelligence Before and After Programming Alexa,” in Proc. 20th Annu. ACM Interact. Des. Children Conf. (IDC '21), New York, NY, USA, 2021, pp. 305–313, doi: 10.1145/3459990.3460730.
[3] S. Salehi, K. D. Wang, R. Toorawa, and C. Wieman, “Can majoring in computer science improve general problem-solving skills?,” in Proc. 51st ACM Tech. Symp. Comput. Sci. Educ., 2020, pp. 156–161.
[4] IOTKIDS company, “IOTKIDS,” 2025. [Online]. Available: https://messarat.com.
[5] J. Pacheco, J. Ferreira, H. Tavares, and M. Miranda, “Machine learning tool for kids: A contribution to teaching computational thinking in schools,” 2023.
[6] I. Cetin and T. OTU, “The Effect of the Modality on Students’ Computational Thinking, Programming Attitude, and Programming Achievement,” Int. J. Comput. Sci. Educ. Sch., vol. 6, no. 2, 2023, doi: 10.21585/ijcses.v6i2.170.
[7] C.-J. Chung and L. Shamir, “Introducing machine learning with scratch and robots as a pilot program for K-12 computer science education,” Sci. Educ., vol. 6, no. 7, pp. 181–186, 2020.
[8] N. Alturayeif, N. Alturaief, and Z. Alhathloul, “DeepScratch: Scratch programming language extension for deep learning education,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 7, 2020.
[9] N. Pope, J. Kahila, H. Vartiainen, and M. Tedre, “Children’s AI Design Platform for Making and Deploying ML-Driven Apps,” Authorea Prepr., 2023.
[10] K. Kahn and N. Winters, “AI Programming by Children,” in Proc. Constructionism 2018 Conf., 2018, pp. 315–324.
[11] S. Dasgupta and B. M. Hill, “Scratch community blocks: Supporting Children as data scientists,” in Conf. Hum. Factors Comput. Syst. - Proc., vol. 2017-May, 2017, pp. 3620–3631.
[12] Teachable Machine. [Online]. Available: https://teachablemachine.withgoogle.com/, 2018.
[13] R. J. Gaona and M. M. González, “STEAM Skills Developed in Basic General Education Through the Creation of the Digital Robot Through the mBlock Platform,” in 2022 Third Int. Conf. Inf. Syst. Software Technol. (ICI2ST), 2022, pp. 152–157.
[14] D. Lane, Machine Learning for Kids: A Project-Based Introduction to Artificial Intelligence. San Francisco, CA, USA: No Starch Press, 2021.
[15] D. Lane, “Machine learning for kids.” [Online]. Available: https://machinelearningforkids.co.uk/, 2018.
[16] E. Aivaloglou and F. Hermans, “How kids code and how we know: An exploratory study on the Scratch repository,” in Proc. 2016 ACM Conf. Int. Comput. Educ. Res., 2016, pp. 53–61.
[17] J. H. Maloney, K. Peppler, Y. Kafai, M. Resnick, and N. Rusk, “Programming by choice: urban youth learning programming with scratch,” in Proc. 39th SIGCSE Tech. Symp. Comput. Sci. Educ., 2008, pp. 367–371.
[18] P. Cardaliaguet, F. Delarue, J.-M. Lasry, and P.-L. Lions, The Master Equation and the Convergence Problem in Mean Field Games. Princeton, NJ, USA: Princeton Univ. Press, 2019.
[19] D. K. Lee, J. In, and S. Lee, “Standard deviation and standard error of the mean,” Korean J. Anesthesiol., vol. 68, no. 3, pp. 220–223, 2015.
[20] V. Khare, C. Khare, and P. Baredar, Decision Science and Operations Management of Solar Energy Systems. London, UK: Elsevier Inc., 2022.