Integrating Data Science and the Internet of Things Into Science, Technology, Engineering, Arts, and Mathematics Education Through the Use of New and Emerging Technologies

Abstract

This paper reports on the implementation of a collaborative approach to STEAM (Science, Technology, Engineering, Arts, and Mathematics) education within the context of a college of education and an elementary school in Ireland. The project is novel in that it explores a transdisciplinary approach of supporting STEAM education in a school, using data science and an Internet of Things (IoT) based Environment Monitoring System. This case study contributes to an emerging field of research within STEAM that is informed by practice with emphasis placed on the value of collaboration and transdisciplinary pedagogical approaches. The multi collaborators included lecturers from a college of education with expertise in STEM and Visual Arts education, creative professionals (artists, scientists, and architects), teachers, students, and children. Encompassing all the mentioned stakeholders was important and placed transdisciplinarity at the core of the learning. Qualitative data was collected using questionnaires, focus groups and interviews to research how the participants interpreted their experiences and what meaning they attributed to their experiences. It was found that a program of STEAM education integrating new and emerging technologies with data science and the IoT can promote and encourage the delivery of a transdisciplinary model of STEAM education and the overall reconceptualization of how individual subject areas are taught at elementary level. Innovative data capturing technologies, exploring real-life data within their local educational contexts can positively impact children and teachers’ knowledge and skills in STEAM supporting the development of artistic concepts, engineering habits of mind, imagination, and creativity. The success of the project can be attributed to the collaboration between various stakeholders in the design and implementation of the project. This paper has provided valuable insights into teachers’, scientists’, and artists’ perspectives, children’s experiences, and the role of innovative technology in STEAM education.

Engaging elementary students in data science practices

“…Both connected learning (Ito et al , 2013) and results of recent studies (Liston et al , 2022; Sulaeman et al , 2021) emphasize the importance of designing data science instruction congruent with students' context and experiences. However, our study's findings also illuminate a nuanced dynamic.…”

“…With limited research on ways elementary teachers might incorporate engaging data science instruction in their everyday practices, we observed and listened to children’s perspectives to address this important research gap. Grounded in theoretical (Ito et al , 2013) and conceptual frameworks (Weintrop et al , 2016) that support using problem-based activities and heightening CT instruction (Kim, 2021), and literature promoting inclusive and relevant data science instruction (Liston et al , 2022; NASEM, 2023), this work provides a roadmap for elementary educators to consider pedagogical approaches towards data science instruction for young children. We assist in answering the call to build a body of research and practical models for data science pedagogies; research that is still in its infancy (Pangrazio and Sefton-Green, 2020).…”

“…While these initiatives signify important steps towards advancing data science education in K-12, recent studies have emphasized the need for more research on how data science education can be effectively integrated in K-12 in ways that are inclusive and relevant to students’ diverse environments (Kim, 2021; Liston et al , 2022; NASEM, 2023; Weiland and Engledowl, 2022). For example, in a collaborative case study with researchers, teachers and elementary students, Liston et al (2022) explored the impact of integrating data science and an internet of things (IoT) weather station on elementary students' (aged 11–12) STEAM (science, technology, engineering, arts and mathematics) practices. Using IoT-connected sensors and an Arduino board, students collected real-time environmental data (light, temperature, humidity and sound) from their surroundings.…”

“…Data science is the interdisciplinary practice of learning from and making decisions with data using modern computational tools (Ow-Yeong et al , 2023). STEM and data science share a close relationship (Liston et al , 2022), as data science leverages the analytical and problem-solving skills fostered by STEM education to make sense of big data, build models, optimize processes and drive innovation across industries. In turn, data science practices like collection, manipulation, analysis and visualization of data are integral for understanding the nature of problems and decision-making across many STEM professions (Roehrig et al , 2021).…”

Trends in Educational Research for Sustainable Development in Postgraduate Education Programs at a University in Peru

“…As aspects of concreteness, virtual, remote, or hybrid education, educational innovation processes with ICT, digital transformation, and technological innovation find dynamizing aspects in the pedagogical models of STEAM from the recognition of interdisciplinary skills and the commitment of educational actors [82][83][84][85], education 4.0 [86][87][88][89][90][91][92][93], Big Data [94][95][96][97][98][99][100][101], Data Mining [102,103], Data Science [104][105][106][107][108][109][110][111], and Artificial Intelligence [112][113][114][115][116][117]. However, one should constantly reflect on the ethics, potentialities, and challenges of artificial intelligence in the framework of quality education [118].…”