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A team of literacy, science, and theatre educators have been working to engage children in an urban public school system in the United States through embodied performances, where students embody and dramatise science ideas. This study focuses on one fourth‐grade classroom when instruction was done remotely due to Covid‐19. Children in the class were asked to compose videos of themselves acting out and/or exploring science phenomena and concepts, and we analysed the affordances of these multimodal compositions. We situate the need for this study in claims from the Next Generation Science Standards that literacy skills are necessary to build and communicate science knowledge. In doing so, we center social semiotics perspectives that conceive of composition broadly as production‐oriented processes drawing from various semiotic resources. The multimodal compositions in Mr. M's science class included both primarily embodied compositions and primarily digital compositions, and we elaborate on one focal example of each in the findings. Intertwined affordances of the focal children and their classmates' multimodal science compositions include opportunities to creatively engage with and negotiate science ideas, to draw from personal and social knowledge during meaning‐making, and to intentionally make rhetorical choices.
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AI-ready data in space science and solar physics: problems, mitigation and action plan
In the domain of space science, numerous ground-based and space-borne data of various phenomena have been accumulating rapidly, making analysis and scientific interpretation challenging. However, recent trends in the application of artificial intelligence (AI) have been shown to be promising in the extraction of information or knowledge discovery from these extensive data sets. Coincidentally, preparing these data for use as inputs to the AI algorithms, referred to as AI-readiness, is one of the outstanding challenges in leveraging AI in space science. Preparation of AI-ready data includes, among other aspects: 1) collection (accessing and downloading) of appropriate data representing the various physical parameters associated with the phenomena under study from different repositories; 2) addressing data formats such as conversion from one format to another, data gaps, quality flags and labeling; 3) standardizing metadata and keywords in accordance with NASA archive requirements or other defined standards; 4) processing of raw data such as data normalization, detrending, and data modeling; and 5) documentation of technical aspects such as processing steps, operational assumptions, uncertainties, and instrument profiles. Making all existing data AI-ready within a decade is impractical and data from future missions and investigations exacerbates this. This reveals the urgency to set the standards and start implementing them now. This article presents our perspective on the AI-readiness of space science data and mitigation strategies including definition of AI-readiness for AI applications; prioritization of data sets, storage, and accessibility; and identifying the responsible entity (agencies, private sector, or funded individuals) to undertake the task.
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- Award ID(s):
- 2026579
- PAR ID:
- 10661255
- Publisher / Repository:
- Frontiers in Astronomy and Space Sciences
- Date Published:
- Journal Name:
- Frontiers in Astronomy and Space Sciences
- Volume:
- 10
- ISSN:
- 2296-987X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.3389/fspas.2023.1203598
