Another new competence? Why 'data literacy' deserves a chance
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Text/Author of original article in German: Birgit Aschemann/CONEDU
In 2023, everyday life is permeated by processes of data collection, data analysis, and the application of their results—mostly without significant declaration. The global volume of data is increasing at an unimaginable scale; the volume of the 'global datasphere' is now measured in zettabytes. Data has become a key resource for the economy and society. The European Commission estimates that by 2025, the number of data experts in europe will double compared to 2018. At the same time, data-driven business processes make individuals the target for selective marketing efforts, information delivery, and algorithm-based decisions.
Is the level of information sufficient?
How is the population informed on these matters? Who precisely knows how today's weather data is generated, what happens with data from vehicle navigation, and whether adjusting cookie settings during internet searches is worthwhile? In daily media interactions, data-driven business models of social media platforms are often used without much reflection. User profiles are created during everyday internet activities often without users being consciously aware of it. And hardly anyone question the data basis of a glossy graphic or the training data behind an AI result. Is this (un)problematic? And can 'data literacy' provide a remedy?
Data literacy - What is it?
Data literacy is defined as the ability to collect, manage, evaluate, and apply data critically, according to a research synthesis from 2015. This definition brings to mind skills such as conducting internet research or surveys, creating Excel tables, using web analytics tools, or composing data-based reports.
A german-language competency framework for data literacy was introduced in 2019 in form of the Future Skills Framework for Data Literacy (att. in German language) for the higher education sector, addressing especially those skills needed in research situations. In 2020, two researchers from Hamburg published an article titled „Datenkompetenz – data literacy“ (att. in German language), where they designate data literacy as a key competence for the 21st century, encompassing data collection, data management, data evaluation, and data application (including data ethics).
All of this remains relevant but should be reconsidered in the face of rapidly advancing developments. Definitions like 'data literacy,' closely tied to technological advancements, are always to be understood as dynamic.
Data literacy revisited: What has changed
The business model of social media aims to generate interaction, and the algorithms behind it fuel this business model. Users are encouraged to share, like, comment, and leave data traces, and they are most likely to do so with emotionally charged content. This has an overall polarizing and destabilizing effect. Media education that enlightens about such dynamics contributes to media literacy and democracy. Social media users who are informed about this are less likely to perpetuate the arousal patterns of the online public (hopefully).
In 2023, additional awareness is needed due to the widespread availability of generative AI. Large language models like ChatGPT generate information based on training data that may contain biases, such as gender bias – this should be considered when interpreting results. Also, for the output of pure language models, the direct sources of the results cannot be traced and therefore cannot be contextualized. For AI products, the key is: it's not always what it seems. Therefore, functional understanding and data literacy are needed to handle AI tools like ChatGPT correctly – at least when the results are intended for publication or teaching situations. Additionally, data privacy must always be considered when using generative AI tools.
Finally, the application of AI is expected to bring forth a wealth of superficial or inaccurate information on the web – whether intentionally or generated out of ignorance. The reception of these contents also requires know-how, namely critical thinking or, in other words, specific data literacy. While the current proposal for the EU's AI Act calls for regulations for high-risk AI and a declaration for products like ChatGPT, the outcome remains uncertain (att. in German language).
If all these aspects are integrated into the concept of 'data literacy,' it closely aligns with 'critical media literacy,' as explained in a video by Mario Friedwagner from bifeb (att. in German language).
What does the concept of data literacy bring to adult learning?
While data literacy has long been defined, it is minimally applied in programs of Austrian adult education that are visible to the public.
In its newer interpretation ('revisited'), 'data literacy' exists at the intersection of critical media literacy. Advocating for more 'data literacy' is, therefore, a call for a shift in attention—specifically, more attention to the current mechanisms of social media and generative AI (and other data-based business models of the future).
Could this focus strategically contribute to better implementing the sociopolitically important goal of enlightenment? And could adult education benefit from it?
Both are entirely possible. Due to social media and generative AI, critical thinking and vigilant media literacy are more crucial than ever. While these are also topics in political education, terms like 'political education' and 'critical thinking' have traditionally faced marketing challenges in adult education—both are minority programs based on participation numbers. However, coping in a data-driven world could be a learning concern for many people—not only when biographically relevant decisions in their lives are made based on algorithms. Ideally, data literacy is of interest to everyone who wonders about the basis of their risk assessment in insurance, where the data from their vehicle's navigation system is being sent, and why it is often challenging to opt out of cookie requests.
Data literacy on the policy agenda
The European Framework for Digital Competence - known as DigComp - already included the competency area 'Information and data literacy' in version 2.1 from 2017. This competency area defined goals such as research, critical evaluation, and management of information and data. This was adopted into the Austrian model DigComp 2.2. AT and further developed in the DigComp 2.3 model, now explicitly including the term 'critical.
In 2021, the German federal government adopted an extensive data strategy, explicitly aiming to increase the data literacy of the German population. In Austria, the (former) Council for Research and Technology Development presented a position paper in 2022 titled 'Data Excellence: Strategies for Austria' (att. in German language), calling for a targeted improvement of data literacy in the Austrian population. The desired scenario of a data-literate population seems to be a consensus - both in Austria and beyond.
However, a certain tension with economic concerns is hard to overlook. In a publication on the Digital Action Plan Austria titled 'The Big Data Opportunity' (att. in German language), it is stated, 'Data sovereignty is only possible if there is sufficient knowledge about one's own data, the processing of this data, and the opportunities and risks of data usage throughout society.' Simultaneously, the concept of data sovereignty is complemented by the call for 'data solidarity.' Essentially, this urges the sharing of data for purposes with societal added value. The background is the European Data Strategy, aiming to establish a single market for data and facilitate shared data across different sectors and national borders within the EU. The national implementation of the European Data Strategy in Austria has been the responsibility of the Federal Ministry of Finance (BMF) since July 2023.
Further information:
- Future Skills: Ein Framework für Data Literacy – Kompetenzrahmen und Forschungsbericht (2019) (att. in German language)
- Ludwig, Th. / Tielmann, H.: Datenkompetenz – Data Literacy. Informatik Spektrum (2020) (att. in German language)
- Data Strategy of the Federal German Government
- Publikation zum Digitaler Aktionsplan Austria: Die große Daten-Chance (2020) (att. in German language)
- A self-test for data literacy in everyday life
- Das österreichische Kompetenzmodell DigComp 2.3 AT (att. in German language)
- Kritische Medienkompetenz: Noch relevanter durch neue KI-Anwendungen? (att. in German language)
Text/Author of original article in German: Birgit Aschemann/CONEDU
Redaktion/Editing of original article in German: Gunter Schüßler/CONEDU
Titelbild/Cover image: Unsplash-Lizenz, Kelvin Han, unsplash.com
Translated by: EPALE Austria
Data Literacy
The blog posts resonate strongly with me, as I’ve seen the gradual evolution of the data-driven society. In my opinion, we live in an era that is characterised by large data volumes and advanced technologies. Therefore, having data literacy skills is both necessary and imperative.
The author adequately highlights the nature and issues of media literacy, stressing the importance of understanding the mechanisms behind generative AI, social media, and other data-based models. I believe that direct discussions about the benefits and correct use of AI data models would benefit modern society, as topics such as plagiarism and a lack of creativity are at risk.
In conclusion, having data literacy skills and critical thinking is an important set for individuals to stay informed in a data-driven society.