Artificial Intelligence and Machine Learning in the STEAM classroom
Analysis of performance data and reflections of international high school students.
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Keywords:Artificial intelligence education, machine learning education, machine perception education, high school, minimum spanning trees, artificial neural networks
This research aims in shedding light on international high school students’ perceptions, awareness, and prior knowledge about Artificial Intelligence and Machine Learning, as well as to investigate the effect of taking a relevant learning unit within a STEAM course on students’ dispositions toward and understanding about Artificial intelligence. The analysis of performance and reflection data from 62 individuals revealed low prior student engagement with Artificial Intelligence and Machine Learning content, a positive shift in the anticipated societal impact of Artificial Intelligence and an active engagement with online Artificial Intelligence applications during the unit, as well as no correlation between student performance and gender. It is suggested that the development and implementation of learning designs that focus on conceptual understanding of Artificial Intelligence and Machine Learning could benefit all students.
Carney, M., Webster, B., Alvarado, I., Phillips, K., Howell, N., Griffith, J., Jongejan, J., Pitaru, A., & Chen, A. (2020). Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification. CHI '20: CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery
Croes, E. A. J., & Antheunis, M. L. (2021). Can we be friends with Mitsuku? A longitudinal study on the process of relationship formation between humans and a social chatbot. Journal of Social and Personal Relationships, 38 (1) 279-300, SAGE
Experiments with Google. (2020). Retrieved from https://experiments.withgoogle.com/
Jing, M. (2018). China looks to school kids to win the global AI race. Published May 3, 2018. Retrieved from https://www.scmp.com/tech/china-tech/article/2144396/china-looks-school-kids-win-global-ai-race
Karampelas, A. (2021). Building a design-centered STEAM course. In Avgerinou, M.D., & Pelonis, P. (Eds.), Handbook of research on K-12 blended and virtual learning through the i²Flex classroom model. IGI Global
Karampelas, A. (2020). Developing and delivering a high school Artificial Intelligence course in blended and online learning environments. European Distance and E-Learning Network (EDEN) Proceedings, European Distance and E-Learning Network, pp. 255-261.
Karampelas, A. (2018). Introducing Artificial Intelligence and Machine Learning in Secondary Education. Astrolavos. Journal of New Technologies, Hellenic Mathematical Society, Issue 29-30
Schwab, S. (2016). The Fourth Industrial Revolution: what it means and how to respond. Published Jan. 14, 2016. Retrieved from https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/
Synced (2018). Chinese publisher introduces AI textbooks for preschoolers. Published Dec. 5, 2018. Retrieved from https://medium.com/syncedreview/chinese-publisher-introduces-ai-textbooks-for-preschoolers-b95e1a89cfa0
Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What Should Every Child Know about AI? Proceedings of the AAAI Conference on Artificial Intelligence, 33, 9795–9799. https://doi.org/10.1609/aaai.v33i01.33019795
World Economic Forum. (2020). Schools of the Future. Defining New Models of Education for the Fourth Industrial Revolution (REF 09012020)
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