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Data literacy is the ability to read, understand, create, and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data. [1] It is, however, not similar to the ability to read text since it requires certain skills involving reading and understanding data. [2]
As these technologies emerge, so have different attempts at defining AI literacy - the ability to understand the basic techniques and concepts behind AI in different products and services. [23] Many framings leverage existing digital literacy frameworks and apply an AI lens to the skills and competencies. Common elements of these frameworks ...
AI literacy or artificial intelligence literacy, is the ability to understand, use, monitor, and critically reflect on AI applications. [1] The term usually refers to teaching skills and knowledge to the general public, particularly those who are not adept in AI.
Understanding the differences between quantitative data vs. qualitative data, as well as formative assessment vs. summative assessment that tease out this data can be defined as assessment literacy. [5] Building assessment literacy also includes knowing when to use which type of assessment and the resulting data to use to inform instruction.
Literacy is the ability to read and write. Some researchers suggest that the study of "literacy" as a concept can be divided into two periods: the period before 1950, when literacy was understood solely as alphabetical literacy (word and letter recognition); and the period after 1950, when literacy slowly began to be considered as a wider concept and process, including the social and cultural ...
These days, the study of information literacy has been extended to include the study of media literacy in many countries like the UK, [7] Australia and New Zealand. [8] It is also referred to as information and communication technologies in the United States. [9] [10] Educators such as Gregory Ulmer have also defined the field as electracy.
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