Data literacy

from Wikipedia, the free encyclopedia

Data competence and Data Literacy includes the ability to collect data on critical way to manage, evaluate and apply.

The term itself developed around the turn of the millennium and has recently been further strengthened by frameworks of the European Commission and the University Forum Digitization in cooperation with the Stifterverband, and also differentiates data literacy from information literacy, digital literacy, data management skills and data science literacy when content and descriptions partially overlap depending on the author.

Data literacy comprises five areas of competence and defines typical tasks for each competence:

  1. Conceptual framework
    • Introduction to data
  2. Data collection
    • Data discovery and collection
    • Evaluation and assurance of the quality of the data sources
  3. Data management
    • Data organization
    • Data manipulation
    • Data conversion (from format to format)
    • Metadata generation and use
    • Data healing, security and reuse
    • Data retention
  4. Data evaluation
    • Data tools
    • Basic data analysis
    • Data interpretation (understanding of data)
    • Use data to identify problems
    • Data visualization
    • Data presentation (verbal)
    • Data-driven decision making
  5. Data application
    • Critical thinking
    • Data ethics
    • Data citation
    • Data sharing
    • Evaluate decisions based on data

The Future Skills Data Literacy framework, published in September 2019, is intended to prepare a competence framework for university education and the requirements for measuring the quality and impact of teaching.

With the conceptual development of data (science) literacy and statistical literacy, the perspective shifts to a cyclical representation of the process (“data informed decision making cycle”). This presentation emphasizes the integration of data analysis into a specific research question or decision-making situation, while the established statistics training at universities focuses on the acquisition of specialist knowledge and the learning of methods.

With this perspective, a fundamental distinction is made between the skills of the coding and the decoding actors in data literacy.

Overall, the data competency catalog includes the following skills:

Framework Future Skills Data Literacy
Encode
A data culture established A1: Identify data application Identifies gaps in knowledge and background information, identifies a specific task on this basis that can be solved with the help of data, has an idea of ​​the possible value contribution of the data
A2: Specify data application Defines minimum and optional requirements, defines demarcations from other tasks, structures the process flow in objects and their relationships, derives measurable objects and hypotheses about their relationships, communicates the requirements to experts
A3: Coordinate data application Planning and coordination of a data project, possibly with the participation of other people (from interdisciplinary areas)
B Provide data B1.1: Modeling the data application Depicts the measurable objects in variables with definable properties and their relationships in a model structure
B1.2: Comply with data protection and security Observes guidelines for secure and ethically sound data processing and implements them accordingly where no clear guidelines are defined
B.2.1: Identify data sources Identifies various common and novel data sources (internal, external) and evaluates their accessibility, relevance and usability
B.2.2: Integrate data Automatically reads data in various formats, integrates them and documents the integration
B.3.1: Verify data Checks the data quality with regard to various criteria (correctness, relevance, representativeness, completeness)

Systematically documents the test

B.3.2: Prepare data Cleans data, corrects errors, imputes missing values, standardizes and transforms data, filters relevant data for a particular question, links data
C Evaluate data C.1: Analyze data Uses static and dynamic visualizations with the help of the appropriate tools in a factual and purpose-oriented manner
C.2: Visualize data Verbalizes the results of data analyzes in various text forms in a factual and purpose-oriented manner
C.3: Verbalizing data Interprets data products (statistics, model results) in verbal form or critically checks the explicitly or implicitly provided interpretation
Decode
D Interpret the results D.1: Interpreting data analyzes Interprets data products (statistics, model results) in verbal form or critically checks the explicitly or implicitly provided interpretation
D.2: Interpreting data visualizations Interprets graphics and draws conclusions about essential elements and relationships or critically checks the explicitly or implicitly provided interpretation
D.3: Interpret data verbalizations Interprets statistical parameters and models in such a way that conclusions are drawn about underlying data points and relationships or forecasts are made
E interpret data E.1: Decipher standardization Recognize, assess and interpret statistical methods used; Recognition of the transformation of the data
E.2: Track data acquisition Based on the analysis and the information provided, it can be traced back how the data was obtained, from which source it came and what level of trust the data can be placed on
E.3: Reconstruct data concept Conclusions about the data basis as well as potential false conclusions can be drawn
F Derive action Q.1: Identify options for action Identifies specific options for action, the assessment and evaluation of which can be evaluated with data; has an idea of ​​the possible value contribution of the data when deriving options for action
F.2: Data-driven action Describes integrating results into the decision-making process and basing action on these results
Q.3: Evaluate impact Describes the evaluation of data-based trading based on its effectiveness

literature

  • European Commission: European e-Competence Framework 3.0. 2016 ( PDF ).
  • Harald Gapski, Thomas Tekster, Monika Elias: Education for and about Big Data. Expert opinion as part of ABIDA - Assessing Big Data. Grimme Institute, Marl 2018 ( PDF ).
  • Andreas Grillenberger, Ralf Romeike: Preliminary study: Cross- university concepts for acquiring 21st century skills using the example of data literacy. In: Hochschulforum Digitisierung , working paper No. 43, doi: 10.5281 / zenodo.2633091 ( PDF ).
  • Jens Heidrich, Pascal Bauer, Daniel Krupka: Approaches to imparting data literacy skills. In: Hochschulform Digitisierung , No. 47, September 2018 ( PDF ).
  • Maren Lübcke, Klaus Wannemacher : Teaching data skills at universities: courses in the field of data science . HIS-HE, Hanover 2018 (Forum University Development 1 | 2018). ISBN 978-3-9818817-1-4 ( PDF ).
  • Evelyn Münster: Help, why does nobody understand my data visualization? Designation, 2019 ( PDF ).
  • C. Ridsdale, J. Rothwell, M. Smit, H. Ali-Hassan, M. Bliemel, D. Irvine et al .: Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report. 2015, doi: 10.13140 / RG.2.1.1922.5044 .
  • Katharina Schüller, Pauline Busch, Carina Hindinger: Future Skills: A Framework for Data Literacy. University Forum Digitization No. 47/2019 ( PDF ).

Individual evidence

  1. ↑ C. Ridsdale, J. Rothwell, M. Smit, H. Ali-Hassan, M. Bliemel, D. Irvine et al .: Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report. 2015, doi: 10.13140 / RG.2.1.1922.5044
  2. ↑ European Commission: European e-Competence Framework 3.0. 2016, http://www.ecompetences.eu/wp-content/uploads/2014/02/European-e-Competence-Framework-3.0_DE.pdf
  3. ↑ Katharina Schüller, Pauline Busch, Carina Hindinger: Hochschulforum Digitisierung NR. 47 / August 2019 Future Skills: A Framework for Data Literacy. https://hochschulforumdigitalisierung.de/sites/default/files/daten/HFD_AP_Nr_47_DALI_Kompetenzrahmen_WEB.pdf
  4. ^ Andreas Grillenberger, Ralf Romeike: Preliminary study: Cross- university concepts for the acquisition of 21st Century Skills using the example of data literacy. Working paper No. 43. Berlin: University Forum Digitization. doi: 10.5281 / zenodo.2633091 .
  5. ↑ Jens Heidrich, Pascal Bauer, Daniel Krupka: Approaches to imparting data literacy skills , Hochschulform Digitisierung, No. 47, September 2018, p. 25ff.
  6. ↑ Katharina Schüller, Pauline Busch, Carina Hindinger: Hochschulforum Digitisierung NR. 47 / August 2019 Future Skills: A Framework for Data Literacy. P. 15, https://hochschulforumdigitalisierung.de/sites/default/files/daten/HFD_AP_Nr_47_DALI_Kompetenzrahmen_WEB.pdf
  7. ↑ Katharina Schüller, Pauline Busch, Carina Hindinger: Hochschulforum Digitisierung NR. 47 / August 2019 Future Skills: A Framework for Data Literacy. P. 22, https://hochschulforumdigitalisierung.de/sites/default/files/daten/HFD_AP_Nr_47_DALI_Kompetenzrahmen_WEB.pdf
  8. ↑ Katharina Schüller, Pauline Busch, Carina Hindinger: Hochschulforum Digitisierung NR. 47 / August 2019 Future Skills: A Framework for Data Literacy. P. 23, https://hochschulforumdigitalisierung.de/sites/default/files/daten/HFD_AP_Nr_47_DALI_Kompetenzrahmen_WEB.pdf
  9. ↑ Evelyn Münster: Help, why does nobody understand my data visualization? Designation, 2019.
  10. ↑ Katharina Schüller, Pauline Busch, Carina Hindinger: Hochschulforum Digitisierung NR. 47 / August 2019 Future Skills: A Framework for Data Literacy. P. 90ff., Https://hochschulforumdigitalisierung.de/sites/default/files/daten/HFD_AP_Nr_47_DALI_Kompetenzrahmen_WEB.pdf