Defining Data Literacy Communities by Their Objectives: A Text Mining Analysis

Yousef, Ahmed Mohamed Fahmy; Walker, Johanna; Urrutia, Manuel León

doi:10.1145/3462741.3466663

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Wolff et al , 2016; Schüller et al , 2019). This technical understanding of data literacy often originates from data science tradition and clearly dominates current academic discourses (Seymoens et al , 2020; Pangrazio and Sefton-Green, 2022), as demonstrated by several recent literature review studies (Maybee and Zilinski, 2015; Raffaghelli and Stewart, 2020; Yousef et al , 2021). In addition, overlaps with numerical literacy, statistical literacy and IT literacy occur (Frank et al , 2016; Gould, 2017).…”

Section: Review Of Existing (Critical) Data Literaciesmentioning

confidence: 99%

Critical datafication literacy – a framework for educating about datafication

Sander

2023

ILS

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Purpose In light of a need for more critical education about datafication, this paper aims to develop a framework for critical datafication literacy that is grounded in theoretical and empirical research. The framework draws upon existing critical data literacies, an in-depth analysis of three well-established educational approaches – media literacy, the German “(politische) Bildung” and Freirean “critical pedagogy” – and empirical analyses of online educational resources about datafication. Design/methodology/approach The study interconnects theoretical analyses with an empirical mixed methods investigation that includes expert interviews with creators of online educational resources about datafication and a qualitative survey with educators interested in teaching about data technologies. Findings The research identified novel findings on the goals of resource creators and educators, such as a focus on empowering and emancipatory approaches, fostering systemic understanding of datafication and encouraging collective action. Such perspectives are rare in existing critical data literacy conceptualisations but show resemblance to traditional education scholarship. This highlights how much can be learnt from practitioners and from these more established educational approaches. Based on these findings, a framework for critical datafication literacy is suggested that aims for systemic understanding of datafication, encouraging critical thinking and enabling learners to make enlightened choices and take different forms of action. Originality/value The study is unique in its interconnection of theoretical and empirical research, and it advances previous research by suggesting a grounded framework for critical datafication literacy.

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Section: Review Of Existing (Critical) Data Literaciesmentioning

confidence: 99%

Critical datafication literacy – a framework for educating about datafication

Sander

2023

ILS

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“…In the other words, this text mining also gives new information about the market. For research purposes, this analytical process has been used to examine large-size information (Yousef et al 2021). In the present time, the business process has used various types of data to develop their business performance.…”

Section: Concept Of Text Miningmentioning

confidence: 99%

Adaptation of Text Mining as Text Data Mining, Similar to Text Analytics for the Process of Driving High-Quality Information from Text

Chhajer¹,

Satpathy²

2022

TTIDMKD

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This research study describes the text mining adaptation of the business organization. In the present time, text mining helps the business organization to analyze the vast text document and extract valuable data from this text. This study has adopted the secondary qualitative data collection methods and from this secondary data, this study has conducted the thematic analysis to find the authentic result. This text mining process has used several types of tools and technologies that can help to increase the capacity of the organization. The business organization can get several benefits from this text mining process. Keyword : Text mining, Text analytics, automated process

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“…It enables researchers to make decisions based on facts, contribute to the body of existing knowledge, and advance scientific knowledge in their specialized fields. In other words, it is a multifaceted idea that draws interest from numerous communities of practice from various perspectives [ 7 ]. The authors divided these communities of practice into four groups, digital activism, artificial intelligence and machine learning, social network analysis, and social media.…”

Section: Introductionmentioning

confidence: 99%

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

Butt

Malik

Qamar

et al. 2023

Sensors

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Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

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Defining Data Literacy Communities by Their Objectives: A Text Mining Analysis

Cited by 5 publications

References 15 publications

Critical datafication literacy – a framework for educating about datafication

Critical datafication literacy – a framework for educating about datafication

Adaptation of Text Mining as Text Data Mining, Similar to Text Analytics for the Process of Driving High-Quality Information from Text

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

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