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What is Data Science: Knowledge obtained from structured and unstructured data is a multifaceted field that employs scientific methodologies

AI Approaches to Literacy in Higher Education
procedures, algorithms, and systems to derive knowledge and gain valuable insights.
Published in Chapter:
Data Literacy and Artificial Intelligence in Higher Education
Aulia Puspaning Galih (Eötvös Loránd University, Budapest, Hungary) and Ágnes Hajdu Barát (Eötvös Loránd University, Budapest, Hungary)
Copyright: © 2024 |Pages: 19
DOI: 10.4018/979-8-3693-1054-0.ch007
Abstract
In the big data era, we generate, use, and share data from many sources. Quantitative survey or experiment results are no longer the only data in academia. Data collection using artificial intelligence is common in academic and research settings, especially in meta-analysis. Data literacy involves understanding, analyzing, and communicating data. Everyone in higher education needs data literacy. Students must understand statistics to correctly interpret data, communicate research findings, and build evidence-based arguments. Artificial intelligence could help solve complex practical and academic problems in sustainable development research. Data literacy must be taught to stakeholders to help them analyze research data for sustainable higher education research. Additionally, higher education institutions must teach artificial intelligence to sustain their research. Transliteracy is another data literacy and AI education future concept. Transliteracy offers a new perspective on how higher education stakeholders with knowledge of education and academic communication can collaborate to better serve future generations.
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