Effectiveness of Active Methodologies and Technological Support in Teaching Machine Learning in K-12.
Abstract
Considering the relevance and insertion of Machine Learning (ML) in people's daily lives, it becomes fundamental to popularize ML skills from an early age. As it is an emerging content in K-12 education, there is still a need to analyze the effectiveness of pedagogical strategies to support learning. In this context, we present a report of the analysis of the active learning methodologies and the technological support used in the ML4ALL course, for teaching the application of basic ML concepts through a series of case studies with a total of 87 students from the final years of elementary and high school. The evaluation results showed that students at this educational level were able to build an understanding of ML and to develop an image classification model.
Keywords:
Computing education, Machine Learning, K-12
References
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Asok, D. et al. (2016). “Active Learning Environment for Achieving Higher-Order Thinking Skills in Engineering Education”. In IEEE 4th International Conference on MOOCs, Innovation and Technology in Education, 47–53.
Basili, V. R., Caldiera, G., Rombach, H. D. (1994) “Goal, Question Metric Paradigm”. In J. J. Marciniak, Encyclopedia of Software Engineering, 528–532. New York: Wiley-Interscience.
Bonwell, C. C. e Eison, J. A. (1991). "Active Learning: Creating Excitement in the Classroom". ASHE-ERIC Higher Education Report, Washington DC: School of Education and Human Development, George Washington University, Washington, DC, USA.
Camada M. Y. e Durães G. M. (2020), Ensino da Inteligência Artificial na Educação Básica: um novo horizonte para as pesquisas brasileiras. Anais do XXXI Simpósio Brasileiro de Informática na Educação, SBC, 1553–1562.
Carney, M. et al. (2020) “Teachable Machine: Approachable Web-based tool for exploring machine learning classification”. In Proc. of the Conference on Human Factors in Computing Systems, New York, NY, USA, 1–8.
Estevez, J. G., Garate, G., Graña, M. (2019) “Gentle Introduction to Artificial Intelligence for High-School Students Using Scratch”, IEEE Access, 7.
Google (2022) “Google Teachable Machine”, https://teachablemachine.withgoogle.com/
Gresse von Wangenheim, C. et al. (2022). A Proposal for Performance-based Assessment of the Learning of Machine Learning Concepts and Practices in K-12”, Informatics in Education, 21(3).
Gresse von Wangenheim, C. et al. (2021) “Visual tools for teaching machine learning in K-12: A ten-year systematic mapping. Education and Information Technology”, Education and Information Technologies, 26(5), 5733–5778
Gresse von Wangenheim, C. et al. (2017). dETECT: A model for the evaluation of instructional units for teaching computing in middle school. Informatics in Education, 16(2).
Kyriacou, C. (1992). “Active Learning in Secondary School Mathematics”. British Educational Research Journal, 18(3), 309–318.
Long, D. e Magerko, B. (2020) “What is AI Literacy? Competencies and Design Considerations”. In Proc. of the Conference on Human Factors in Computing Systems. New York, NY, USA, 1–6.
Marques, L., Gresse von Wangenheim, C., Hauck, J. (2020) “Ensino de Machine Learning na Educação Básica: um Mapeamento Sistemático do Estado da Arte”. Anais do Simpósio Brasileiro de Informática na Educação, Natal, Brasil.
Martins, R. M. and Gresse von Wangenheim (2022). Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review, Informatics in Education.
MEC. (2017) “Base Nacional Comum Curricular''. shorturl.at/adfnS.
Mourão, A. (2017) “Uma proposta da eficiência do uso da Metodologia Ativa Baseada em Problemas, utilizando Dojo de Programação, aplicada na disciplina de Lógica de Programação“. Anais do Workshop de Informática na Escola, Recife, Brasil
Rodríguez-García, J. D., et al. (2021) “Evaluation of an Online Intervention to Teach Artificial Intelligence with LearningML to 10-16-Year-Old Students”. In Proc. of the 52nd ACM Technical Symposium on Computer Science Education, New York, NY, USA
Russell, S e Norvig, P. (2020). “Artificial Intelligence: A Modern Approach”. Pearson.
Sanusi, I. T. e Oyelere, S. S. (2020) “Pedagogies of Machine Learning in K-12 Context”, In Proc. of the IEEE Frontiers in Education Conference, Uppsala, Sweden, 1–8.
Squicciarini, M. e Nachtigall, H. (2021). “Demand for AI skills in jobs: Evidence from online job posts". OECD Science, Technology and Industry Working Papers, No. 2021/03, OECD Publishing, Paris.
Torrance, H. (1995). Evaluating Authentic Assessment: Problems and Possibilities in New Approaches to Assessment. Buckingham: Open University Press.
Touretzky, D. S., Gardner-McCune, C., Martin, F., Seehorn, D. (2019) “Envisioning AI for K-12: What Should Every Child Know about AI?”. In Proc. of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA
UNDESA. (2021) “Departamento de Assuntos Econômicos e Sociais das Nações Unidas”, https://sdgs.un.org.
Yin, R. K. (2017) “Case Study Research and Applications: Design and Methods”, Los Angeles: SAGE Publications.
Zhu, M. (2020) “Effective Pedagogical Strategies for STEM Education from Instructors’ Perspective: OER for Educators “. Open Praxis, vol 12, 257–270.
Published
2023-04-24
How to Cite
MARTINS, Ramon Mayor; GRESSE VON WANGENHEIM, Christiane; RAUBER, Marcelo Fernando; HAUCK, Jean Carlo Rossa.
Effectiveness of Active Methodologies and Technological Support in Teaching Machine Learning in K-12.. In: BRAZILIAN SYMPOSIUM ON COMPUTING EDUCATION (EDUCOMP), 3. , 2023, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 154-162.
ISSN 3086-0733.
DOI: https://doi.org/10.5753/educomp.2023.228162.
