A comparative analysis of data literacy competency frameworks

The field of critical data studies has been deconstructing datafication, pointing out privacy risks in online communication, and criticising the surveillance and the exploitation of internet users. Research on civic tech and data activism seeks to analyse the ways in which different actors face the challenges posed by datafication and try to empower people to make informed decisions about their data.Data literacy becomes a crucial competence today as citizens face the challenges of living in datafied societies. Through a discussion of different concepts of literacy, the chapter characterises data literacy along four specific criteria: (1) citizens possess knowledge of datafication, the ambivalences and challenges they are forced to confront, (2) people have access to their personal data, and (3) they have the skills which are required to engage with data’s specific materiality.The definition of data literacy that is used here was developed in a study presented in this chapter that analysed the ways in which actors encourage and support data literacy at CryptoParties. CryptoParties are events where people meet to share knowledge and learn about critical data practices that allow secure online communication. As the study demonstrates, the diverse group of people who organise these events aim to enable and develop laypersons’ data literacy in an increasingly datafied world.In its discussion about CryptoParties, this chapter contributes to the research field of critical data studies on a general level, but more specifically, its focus is data literacy to demonstrate the ways in which civil society initiatives critically reflect on datafication and the complex risks to privacy posed by online communication.

Data are increasingly important in central facets of modern life: academics, professions, and society at large. Educating aspiring minds to meet highest standards in these facets is the mandate of institutions of higher education. This, naturally, includes the preparation for excelling in today’s data-driven world. In recent years, an intensive academic discussion has resulted in the distinction between two different modes of data related education: data science and data literacy education. As a large number of study programs and offers is emerging around the world, data literacy in higher education is a particular focus of this paper. These programs, despite sharing the same name, differ substantially in their educational content, i.e., a high plurality can be observed. This paper explores this plurality, comments on the role it might play and suggests ways it can be dealt with by maintaining a high degree of adaptiveness and plurality while simultaneously establishing a consistent educational “essence”. It identifies a skill set, data self-empowerment, as a potential part of this essence. Data science and literacy education are still experiencing changeability in their emergence as fields of study, while additionally being stirred up by rapid developments, bringing about a need for flexibility and dialectic.

In today’s society, workplace, and scientific environment, data is increasingly regarded as a highly valuable often the most valuable resource. Decisions are progressively being made on the basis of data. Digitalisation and datafication are reshaping life and work in the 21st century: artificial intelligence, networked production, communicating machines, and autonomous vehicles all rely on data while simultaneously generating vast amounts of it. Data thus forms the foundation for knowledge creation, value generation, and better decision-making. Therefore, data literacy must be cultivated from the start of higher education and across all disciplines. The present study is aimed to find out the data literacy of the university students in Pakistan furthermore to compare the data literacy of applied and social science students at BS level. For the purpose, a total six academic departments three from the applied sciences (Biotechnology, Chemistry, and Zoology) and three from the social sciences (Education, Psychology, and Sociology). A sample size of 360 (180 students from each applied and social science) were selected. IDL was used as data collection tool. Results showed that overall data literacy levels are low across both groups, with social science students demonstrating relatively higher proficiency. From inferential data analysis no significant difference in data literacy between applied science and social science students at the BS level was found. It is recommended that universities should organize workshops and training sessions for teachers; facilitated Enhancing faculty proficiency in statistical tools will directly contribute to improving students’ understanding and use of data literacy software such as SPSS, STATA, and R. The Higher Education Commission of Pakistan (HEC) is encouraged to integrate a dedicated unit on data analysis software including SPSS, STATA, and R into relevant undergraduate programs.

Data literacy is a multidimensional concept that attracts the attention of a variety of communities of practice, from different angles. The authors grouped these communities of practice in three categories: education, fields and professions, and citizenship. The meaning of data literacy varies depending on who uses it, and its concept is often conveyed in terms other than data literacy. This paper addresses the problematization of data literacy as a term by examining academic literature around it. To this end, a desk study was carried out to gather sources where the term is used. After an extensive search in the main academic databases and a subsequent PRISMA selection process, automated content analysis was applied to the gathered sources. The findings suggest that the concept of data literacy has a different treatment in different communities of practice. For example, librarians and citizen scientists have a different understanding of the concept of data literacy.

Scientific writing serves as a platform for students to convey their knowledge, experiences, and information to readers. Data literacy is a crucial tool that aids students in developing, analyzing, and concluding the data they have gathered into well-constructed scientific writing. This research aims to describe students' ability to write scientific papers with respect to data literacy in higher education institutions. To gather research data, the researchers employed a qualitative descriptive method. The data in this study represent the challenges or difficulties faced by the students. Data collection techniques include careful observation and reading of these papers, and documentation techniques involve marking or noting significant parts of the students' scientific works. Data analysis involved mapping frequent errors and calculating the percentage of these errors based on the characteristics of scientific writing. The results indicated that students' ability to write scientific papers remains low, particularly in the areas of idea development and discussion. The following percentages illustrate the students' writing abilities and data literacy: 14.25% of students can develop ideas, with 19% data literacy at the analysis stage. The ability to write the results and discussion sections of scientific papers is at 13.40%, with 12.40% data literacy at the conclusion-making stage. In the concluding section, 71.20% of students accurately write conclusions, with 67.60% data literacy at the reading stage. This study's findings highlight persistent issues in idea development and the results and discussion sections in scientific writing at higher education institutions. Further research should focus on these sections to address the difficulties identified in this study.

Accessing, using and managing data is increasingly recognized as an important learning outcome in higher education. Approaches to data literacy have typically been informed by information literacy. New approaches to information literacy have emerged that address how information is used in the different disciplinary contexts in which people learn and work. Successful approaches to data literacy will also need to address contextual concerns. Informed learning is an approach to information literacy that purposefully addresses contextual concerns by suggesting pedagogic strategies for enabling students to use information in ways that support discipline‐focused learning outcomes. As part of an ongoing investigation, we advance data informed learning as a framework for data literacy in higher education that emphasizes how data are used to learn and communicate within disciplinary learning contexts. Drawing from informed learning, we outline principles and characteristics of data informed learning, and suggest future directions to investigate ways that data are used in real‐world environments.

In today’s data-driven world, it is crucial for students to be data literate; able to view, understand, and reason with data in multimodal forms representing real-world phenomena. Despite its importance, data literacy is rarely integrated into K-12 curricula, and its definition remains unclear for this age group. This paper reviews existing literature to define the competencies relevant to adolescent learners and highlights those crucial for middle-grade students. A literature review of theoretical and empirical discussions on data literacy concepts, instructional practices, and assessments revealed eight key competencies. Among these, two were identified as most critical for middle-grade students: interpreting data representations and evaluating claims based on data representations. This paper aims to serve as a conceptual and practical guide to enhance data literacy in educational settings, providing a foundation for educators and researchers to collaboratively support middle-grade learners.

In this contribution, Valentin Unger, Michael Beck, and Vera Husfeldt critically examine the construct of "data literacy" and provide a historical overview of the emergence of the term. They also suggest ways to promote data literacy in higher education.

ABSTRACTData visualizations are now commonplace in the public media. The ability to interpret and create such visualizations, as a form of data literacy, is increasingly important for democratic participation. Yet, the cross-disciplinary knowledge and skills needed to produce and use data visualizations and to develop data literacy are not fluidly integrated into traditional K–12 subject areas. In this article, we nuance and complicate the push for data literacy in STEM reform efforts targeting youth of color. We explore a curricular reform project that integrated explicit attention to issues pertaining to the collection, analysis, interpretation, representation, visualization, and communication of data in an introductory computer science class. While the study of data in this unit emphasized viewing and approaching data in context, neither the teacher nor the students were supported in negotiating the racialized context of data that emerged in classroom discussions. To better understand these dynamics, we detail the construct of racial literacy and develop an interpretative framework of racial-ideological micro-contestations. Through an in-depth analysis of a classroom interaction using this framework, we explore how contestations about race can emerge when data visualizations from the public media are incorporated into STEM learning precisely because the contexts of data are often racialized. We argue that access to learning about data visualization, without a deep interrogation of race and power, can be counterproductive and that efforts to develop authentic data literacy require the concomitant development of racial literacy.

Darkness, Datafication, and Provenance as an Illuminating Methodology

El objetivo principal del estudio es diseñar una propuesta de formación en alfabetización de datos, a modo de curso cero, en el que los estudiantes reciban una instrucción básica o inicial sobre identificación, localización, visualización y recuperación de conjuntos de datos, surgidos en el contexto de los datos abiertos y de la ciencia de datos. El trabajo se basa en la revisión de la literatura existente en materia de data literacy y en el análisis de prácticas basadas en datos en el ámbito de la Educación Superior. El estudio permitirá contar con una propuesta formativa en alfabetización de datos que contemple las competencias digitales y transversales que deben adquirir los estudiantes, los objetivos y contenidos de aprendizaje, así como las tareas, recursos y herramientas que lo hagan posible.

Decolonisation of the curriculum in higher education is a radical, transformative process of change that interrogates the enduring Eurocentric and racist narratives surrounding the production of academic ‘knowledge’. Our key argument is that it is essential for students of politics to understand the authorities and hierarchies exerted through quantitative data. In this article, we show that (1) quantitative methods and data literacy can be an explicit tool in the endeavour to challenge structures of oppression, and (2) there is a need to apply decolonial principles to the teaching of quantitative methods, prioritising the historical contextualisation and anti-racist critique of the ways in which statistics amplify existing micro and macro power relations. To explain how this can be done, we begin with a commentary on the ‘state of decolonisation’ in higher education, its relevance to the subdisciplines of politics, and its application to quantitative teaching in the United Kingdom. We then suggest some guiding principles for a decolonial approach to quantitative methods teaching and present substantive examples from political sociology, international political economy, and international development. These suggestions and examples show how a decolonial lens advances critical and emancipatory thinking in undergraduate students of politics when it is used with quantitative methods.

Traditional online learning environments generally use a one-size-fits-all approach, neglecting individual learner needs, goals, and cognitive abilities. Adaptive Educational Hypermedia (AEH) systems emerged to address this by personalizing learning content based on user profiles. The Adaptive Learning System is a computerized system developed to enhance digital interactive educational content teaching and learning. The system’s Adaptive algorithm automatically detects learners’ strengths and weaknesses through the adaptation process and provides personalized learning materials, feedback, and guidance during their learning. Adaptive content allows learners to learn the material at their own pace while providing additional support, evaluation, and resources tailored to each learner. In traditional educational systems, every student is given access to the same learning content regardless of their individual learning needs. Higher education institutions face various challenges when testing and implementing adaptive learning systems for their domain of knowledge. Issues related to technical challenges include processing real-time data, difficulties consolidating learning concepts and solutions into existing learning management systems, and the complexity, ease of use, and usability of adaptive learning systems. However, despite the advancement and technical sophistication in adaptive algorithms, most AEH systems still face significant usability challenges. Learners often struggle with complex interfaces, limited system responsiveness, and poor adaptation to diverse demographics and subject backgrounds. Consequently, the current adaptive systems are underutilized in higher education, with limited empirical evidence on their effectiveness in enhancing student engagement, engagement, quality of experience (QoE), and academic performance. While foundational research work (Brusilovsky, 2001; De Bra et al., 2013) and recent studies (Liu & Lim, 2020; colMOOC project) have shown the importance of adaptive educational hypermedia, yet ongoing issues persist regarding usability, learner control (Paramythis & Loidl-Reisinger, 2004), and empirical validation across varied higher education settings (Latham et al., 2004; Educause, 2018). Systematic investigations into user-controllable personalization and its effects on inclusivity and academic achievement remain limited (Xie et al., 2019; Lundqvist; Warburton, 2019). Furthermore, Li et al. (2021) highlighted that existing systems lack comprehensive cognitive modelling and fail to adequately adapt to individual learner dispositions, which restricts their intelligence and adaptability. This complexity in adaptive systems continues to pose challenges for education overall, as emphasized by Fengying Li, Yifeng He, and Qingshui Xue (2021). Addressing these issues, Dubiel et al. (2022) propose a framework for adaptive user interfaces that integrates the interaction environment to enhance system responsiveness. Therefore, there remains a significant gap in the literature regarding the lack of empirical evaluation of user-controlled personalization, particularly in real-world, multi-disciplinary higher education contexts. Existing research has focused mainly on adaptive algorithmic and technical development, but has not given sufficient attention to practical usability (the lack of effective, usable AEH systems, learner diversity, and actual learner-centered outcomes). Moreover, there is a lack of comprehensive frameworks for evaluating the usability of adaptive educational hypermedia (AEH), and there is limited research on how these adaptive systems can foster inclusivity and fairness among diverse learner populations. Therefore, there is an urgent need to address the usability and complexity problems in AEH systems and to suggest the creation of usable, effective, personalised, learner-centric, and user-controllable AEH systems for higher education environments. This research study addresses these gaps by introducing a personalized, user-controllable Adaptive Educational Hypermedia (AEH) framework specially designed for a Higher Education setting. It evaluates multiple versions of adaptive learning systems across multiple disciplines (English, Malay, Computer Science) revealing significant enhancements in both student achievement and satisfaction. The study not only evaluates multiple versions of adaptive systems but also offers practical recommendations for software developers and educators seeking to implement effective, more intuitive, and learner-centric adaptive systems within real higher educational environments. By exploring the relationships between learner demographics, usability, and academic outcomes, the study promotes inclusivity and equity in digital education. More specifically, it introduces a novel user-controllable personalization feature that empowers learners to customize the learning content to improve their adaptive learning experience, addressing a major limitation found in existing systems. The findings demonstrate significant improvements in student performance and satisfaction with the system’s interface, thereby enhancing subject retention, confirming that adaptive learning systems can effectively improve the academic experience in higher education environments.

Digital Literacy

The present study aimed to develop and validate an instrument to measure educational research literacy. An exploratory sequential mixed method design was used with conveniently sampled six participants for interviews, five content experts for instrument content validation, and eighty-nine faculty members for the pilot testing of the instrument. Confidentiality of data was assured and informed consent from respondents was secured. Qualitative data were thematically analyzed. Data from content experts were analyzed to obtain Content Validity Indices (CVIs) of the items (I-CVIs) and the scale (S-CVI). Quantitative data from pilot testingwere subjected to reliability and factor analyses. A 24-item Educational Research Literacy Scale (ERLS) measuring four dimensions, namely knowledge of research concepts, data literacy, information literacy, and evidence-based reasoning was developed and validated. Furthermore, ERLS had sound psychometric properties with established evidence of validity and reliability. Moreover, this study recommended that other evidences of instrument validity should be established.