Data Literacy and Artificial Intelligence in Higher Education

Data Literacy and Artificial Intelligence in Higher Education

Copyright: © 2024 |Pages: 19
DOI: 10.4018/979-8-3693-1054-0.ch007
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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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Data Literacy

When it comes to achieving success in higher education, having a strong command of quantitative literacy is absolutely necessary. Most literacy skills are acquired in a roundabout way through the practices involved in various school subjects rather than being taught in a formal setting (Prince et al., 2021). A distinct aptitude for quantitative and statistical literacy is the ability to analyze, interpret, and evaluate quantitative and statistical evidence in a proficient manner. The ability to understand data and statistics is one of the essential components of information literacy. The findings of the statistics offer a condensed summary of the data. Data literacy is enhanced by the ability to search for statistics. This entails understanding both the objective and the research site (Sabbati, 2022). When selecting, converting, and manipulating the data that underlies a statistic, the methods utilized significantly impact the numerical value of the statistic. Having the ability to convert and manipulate data is a specialized skill that requires a significant amount of knowledge and practice. For this reason, data literacy should be an essential component of both information and statistical literacy.

Key Terms in this Chapter

Data Management: The practice of acquiring, organizing, and retrieving data to enhance productivity, effectiveness, and the process of making sensible decisions.

Literacy: The capacity to identify, comprehend, analyze, generate, convey, and calculate, utilizing printed and written materials linked to diverse contexts.

Transliteracy: Proficiency in reading, writing, and engaging with various platforms, tools, and media, including signing, oral communication, handwriting, print, television, radio, film, and digital social networks.

Big Data: A massive and heterogeneous collection of structured: unstructured, and semi-structured data that exhibits exponential growth over time.

Artificial Intelligence: Computer systems possess the ability to execute intricate tasks that were traditionally exclusive to humans: such as logical thinking, decision-making, and problem-solving.

Data-Driven Culture: A setting in which the utilization of analytics: statistics, and factual insights serves as the foundation for enhancing processes and achieving organizational objectives.

Sustainable Development Goals: A comprehensive campaign to eradicate poverty: protect the environment, and guarantee that by 2030, every individual will experience tranquillity and prosperity.

Digital Literacy: The proficient and analytical utilization of a comprehensive array of digital technologies for the purposes of information retrieval, communication, and fundamental problem-solving across various fields.

Data Visualization: The visualization of data using prevalent visual aids, including charts, plots, infographics, and animations.

Data Literacy: The capacity of an individual to comprehend, interpret, and employ data in diverse manners.

Data: Quantitative or qualitative data gathered for the purpose of analysis, evaluation, and utilization in facilitating decision-making processes.

Critical Thinking: The process of intellectually systematically conceptualizing: applying, analyzing, synthesizing, and evaluating information obtained from, or generated by, observation, experience, reflection, reasoning, or communication serves as a guiding framework for individuals' beliefs and actions.

Higher Education: Education offered by a college or university beyond the secondary level.

Data Science: Knowledge obtained from structured and unstructured data is a multifaceted field that employs scientific methodologies: procedures, algorithms, and systems to derive knowledge and gain valuable insights.

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