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Connected Learning in STEAM Classrooms: Opportunities for Engaging Youth in Science and Math Classrooms.

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Title: Connected Learning in STEAM Classrooms: Opportunities for Engaging Youth in Science and Math Classrooms.
Authors: Quigley, Cassie F.1,2 (AUTHOR) CQUIGLEY@pitt.edu, Herro, Dani3 (AUTHOR), Shekell, Calli1 (AUTHOR), Cian, Heidi4 (AUTHOR), Jacques, Lori5 (AUTHOR)
Source: International Journal of Science & Mathematics Education. Dec2020, Vol. 18 Issue 8, p1441-1463. 23p.
Subject Terms: *Science classrooms, *STEAM education, *Learning, *Classrooms, *Kindergarten children, Conceptual models
Abstract: STEAM education evolved to address the critical demand for creative transdisciplinary teaching that was under-realized in STEM programs. However, this novel concept has not been clearly conceptualized; this is likely attributed to the lack of a grounding theory to frame STEAM. We propose using connected learning theory to examine a previously developed STEAM conceptual model. This work explores the potential of connected learning theory to understand specific STEAM instructional practices. Using observations of 43 middle-grade teachers from 14 schools enacting STEAM practices in their classrooms, we examined what connected learning looked like in STEAM classrooms and how the STEAM conceptual model could be enhanced by analyzing implementation practices through the principles of connected learning. The qualitative data analysis of observations, video recorded data, and debriefing sessions with teachers after the observations included two rounds of analysis. This found significant overlap in ideas of connected learning and STEAM, notably a shared emphasis on design, collaboration, and contextualized learning. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Science & Mathematics Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Science+%26+Mathematics+Education%22">International Journal of Science & Mathematics Education</searchLink>. Dec2020, Vol. 18 Issue 8, p1441-1463. 23p.
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  Data: STEAM education evolved to address the critical demand for creative transdisciplinary teaching that was under-realized in STEM programs. However, this novel concept has not been clearly conceptualized; this is likely attributed to the lack of a grounding theory to frame STEAM. We propose using connected learning theory to examine a previously developed STEAM conceptual model. This work explores the potential of connected learning theory to understand specific STEAM instructional practices. Using observations of 43 middle-grade teachers from 14 schools enacting STEAM practices in their classrooms, we examined what connected learning looked like in STEAM classrooms and how the STEAM conceptual model could be enhanced by analyzing implementation practices through the principles of connected learning. The qualitative data analysis of observations, video recorded data, and debriefing sessions with teachers after the observations included two rounds of analysis. This found significant overlap in ideas of connected learning and STEAM, notably a shared emphasis on design, collaboration, and contextualized learning. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Science & Mathematics Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10763-019-10034-z
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              Text: Dec2020
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