Data Literacy Conceptionsand PedagogiesRedefining Information Literacy Frameworks forthe 21st CenturyProfessor Sheila Corr...
Context for data literacy development•  History of library involvement with print and electronic   statistical sources and...
Libraries, librarians and data                ‘Providing data services is a natural fit for the                academic li...
What is Data Literacy?Who should be developingknowledge and skills indealing with data?20/04/11   © University of Sheffiel...
Conceptions of data literacy (1)A social science perspectiveData literacy almost synonymous with statistical literacy,quan...
Conceptions of data literacy (2)Alternative (hierarchical) social science perspectives        CRITICAL THINKING           ...
Conceptions of data literacy (3)A science (STEM/information science) perspectiveScience data literacy shares aspects of so...
Strategic and                                                                                   operational rolesResearch ...
‘Scientific datasets may be thought                                       of as the ‘special collections’ of the          ...
Pedagogies for data literacy (1)McGill Libraries Electronic Data Resources ServiceSupporting multidisciplinary research an...
20/04/11   © University of Sheffield / Information School / Sheila Corrall
Pedagogies for data literacy (2)UCLA 105 Sociology Information Literacy LabDeveloping students’ skills in searching for, r...
Pedagogies for data literacy (3)Calgary 311 Biology Information Literacy LabIncorporating genetic data resources in IL ins...
20/04/11   © University of Sheffield / Information School / Sheila Corrall
Pedagogies for data literacy (4)Purdue Libraries GIS LibrarianRaising awareness of the importance of data among   students...
20/04/11   © University of Sheffield / Information School / Sheila Corrall
Pedagogies for data literacy (5)Syracuse Science Data Management CourseLearning how data management solutions support scie...
20/04/11   © University of Sheffield / Information School / Sheila Corrall
Redefining frameworks for the 21C•  Work in progress on revising   the Seven Pillars Model to   meet researcher needs•  Ca...
Redefining frameworks                                                                             Should we develop more  ...
Redefining frameworks for the 21C•  Should we update our                                                      Plain Englis...
Points for reflection and discussion•  How should we incorporate data literacy into   information literacy frameworks?    ...
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Corrall - Data literacy conceptions and pedagogies: Redefining information literacy frameworks for the 21st Century

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Corrall - Data literacy conceptions and pedagogies: Redefining information literacy frameworks for the 21st Century

  1. 1. Data Literacy Conceptionsand PedagogiesRedefining Information Literacy Frameworks forthe 21st CenturyProfessor Sheila CorrallCentre for Information Literacy Research
  2. 2. Context for data literacy development•  History of library involvement with print and electronic statistical sources and data archives in social sciences −  social science librarians and specialist data librarians/archivists•  Growth of computer/network-enabled scientific research −  need to raise data literacy of science students and develop workforce of data managers able to contribute to e-research•  Current interest among information literacy practitioners in strengthening support for research students and staff −  revision of Seven Pillars Model to improve relevance to research•  Debate on roles and responsibilities in data management −  including questions about library capacity, institutional mandates and the education, training and development of key players20/04/11 © University of Sheffield / Information School / Sheila Corrall
  3. 3. Libraries, librarians and data ‘Providing data services is a natural fit for the academic librarys core mission of helping users find information in a variety of formats’ (Read, 2007: 72)‘Datasets are heavier, more feral, and require moreresources than, say, monograph shipments or e-journalsubscriptions, but managing and improving the organizationof and access to them is still the obligation of the libraryand information scientist.’(Miller, 2010) ‘…we also advocate the integration of pedagogies for data literacy and information literacy’ (Stephenson & Caravello, 2007: 535)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  4. 4. What is Data Literacy?Who should be developingknowledge and skills indealing with data?20/04/11 © University of Sheffield / Information School / Sheila Corrall
  5. 5. Conceptions of data literacy (1)A social science perspectiveData literacy almost synonymous with statistical literacy,quantitative literacy and numeracy – but involving morethan basic statistics and mathematical functions•  understanding data and its tabular and graphical representations, including statistical concepts and terms•  finding, evaluating and using statistical information effectively and ethically as evidence for social inquiries•  reading, interpreting and thinking critically about statsData literacy is an essential and critical componentof information competence in social sciences (e.g. Read, 2007; Schield, 1999; Stephenson & Caravello, 2007)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  6. 6. Conceptions of data literacy (2)Alternative (hierarchical) social science perspectives CRITICAL THINKING SOCIAL SCIENCE DATA Analysis, Interpretation, Evaluation Analysis, Interpretation, Evaluation Information Literacy Data Literacy Statistical Literacy Statistical Literacy Data Literacy Information LiteracyCritical thinking perspective Discipline perspective (Schield, 2004) 20/04/11 © University of Sheffield / Information School / Sheila Corrall
  7. 7. Conceptions of data literacy (3)A science (STEM/information science) perspectiveScience data literacy shares aspects of social scienceconceptions, but requires awareness of the data life cycle,metadata issues, data tools and collaboration mechanisms•  managing the data generated from experiments, surveys and observations by using sensors and other devices•  understanding the attributes, quality and history of data to produce valid, reliable answers to scientific inquiries•  accessing, collecting, processing, manipulating, converting, transforming, evaluating and using dataSDL goes beyond ‘pushing’ the data to students bydeveloping abilities and skills in ‘pulling’ data (Qin & D’Ignazio, 2010)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  8. 8. Strategic and operational rolesResearch data Influence national data for researchmanagement policy librariespyramid for Lead on local (Univ) datalibraries Develop policy Identify local data required data curation skills with LIS capacity schools Bring data into Teach data UG research- literacy to post- Develop based graduates Develop library learning Provide researcher workforce researcher data data data advice awareness confidence 20/04/11 © University of Sheffield / Information School / Sheila Corrall (Lewis, 2010: 154)
  9. 9. ‘Scientific datasets may be thought of as the ‘special collections’ of the digital age’ (Choudhury, 2008: 218)Examples of tactical adaptation of existingLIS practices to managing research data•  Conducting data interviews with researchers•  Adding data sets to institutional repositories•  Developing subject librarians into data liaisons•  Including data literacy in information instruction (classroom sessions, teachable moments at the reference desk, drop-in research consultations) (e.g. Delserone, 2008; Gabridge, 2009; MacMillan, 2010; Miller, 2010; Witt & Carlson, 2007)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  10. 10. Pedagogies for data literacy (1)McGill Libraries Electronic Data Resources ServiceSupporting multidisciplinary research and instruction with historical, socio-economic and GIS data•  preparing web pages tailored to particular courses, highlighting appropriate data sources −  and offering class presentations based on the pages•  providing computer facilities for student use and technical assistance for work involving digital data•  scheduling departmental orientations for grad students to demonstrate the wide array of research resources•  delivering training sessions and workshops on software (e.g. Excel, SPSS, Stata and SAS) (Czarnocki & Khouri, 2004)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  11. 11. 20/04/11 © University of Sheffield / Information School / Sheila Corrall
  12. 12. Pedagogies for data literacy (2)UCLA 105 Sociology Information Literacy LabDeveloping students’ skills in searching for, retrieving, customising and critically evaluating statistical resources•  standalone unit taught by librarian and data archivist −  10 weeks, 7 credit-bearing assignments + credit for attendance•  aim not to teach statistics, but to use statistical resources•  intended learning outcomes −  able to read and critically evaluate simple 2 x 2- or 3-way tables −  produce accurate bibliographic citations for data tables −  use American Factfinder to create a table, which they could describe and cite correctly −  read an article containing a graphical representation of data and discuss it in relation to the article content (Stephenson & Caravello, 2007)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  13. 13. Pedagogies for data literacy (3)Calgary 311 Biology Information Literacy LabIncorporating genetic data resources in IL instruction by simulating pathways of experienced researchers•  integrated unit taught by librarian(s) and lab instructors −  90 minutes (workshop, structured exercise and credit-bearing poster assignment, supported by workbook and online resource)•  authentic workflow designed with academic collaborator −  step-by-step exercise based on tool-specific modules, providing demonstration, practice and discussion of each resource −  progressing from online encyclopedias and journal dbases through Google Patents to gene and protein databanks and tools −  highlighting synergies and relationships between key resources•  value added by infolit expertise and student perspective −  contextualising sources in disciplinary information environment and identifying where extra scaffolding needed (Macmillan, 2010)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  14. 14. 20/04/11 © University of Sheffield / Information School / Sheila Corrall
  15. 15. Pedagogies for data literacy (4)Purdue Libraries GIS LibrarianRaising awareness of the importance of data among students and faculty ‘the technological barrier between libraries and geospatial research is surprisingly low’•  inserting single-session drop-ins into existing courses•  exploiting reference and consultation sessions ‘the librarian lays a heavy rap about data access and reuse on the unsuspecting student that has stopped by for some help with this or that’•  delivering multidisciplinary credit-bearing courses −  applying geoinformatics technologies to diverse subject fields −  3 weeks (credits for labs, project, participation and quizzes) (Miller, 2010)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  16. 16. 20/04/11 © University of Sheffield / Information School / Sheila Corrall
  17. 17. Pedagogies for data literacy (5)Syracuse Science Data Management CourseLearning how data management solutions support scientific practice, balancing info, tech, social and policy issues•  elective unit, taught by iSchool academic and PhD −  14 weeks (aimed at STEM UGs, taken by iSchool UGs and PGs)•  intended learning outcomes −  understand the fundamental concepts in scientific data −  use the data for scientific inquiry•  teaching strategies deployed −  clearly differentiated modules/sub-units, tiered skill development −  extensive treatment of metadata through wide set of readings −  real-world cases studies (e.g. geography as accessible example) −  authentic project involvement (pairing UG and PG students) (Qin & D’Ignazio, 2010)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  18. 18. 20/04/11 © University of Sheffield / Information School / Sheila Corrall
  19. 19. Redefining frameworks for the 21C•  Work in progress on revising the Seven Pillars Model to meet researcher needs•  Can the ‘skills’ be expanded sufficiently to provide the necessary focus on: −  the attributes and life cycle of data resources? −  the management and processing of data? (See Qin & D’Ignazio, 2010)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  20. 20. Redefining frameworks Should we develop more subject-specific models?20/04/11 © University of Sheffield / Information School / Sheila Corrall
  21. 21. Redefining frameworks for the 21C•  Should we update our Plain English definition? literacy definitions: ‘Data literacy is knowing −  add scope notes? when and why you need data, where to find them, −  insert ‘data’ into the what their attributes are, text as appropriate? and how to evaluate, −  produce statements to process, use, manage supplement existing and communicate them in definitions? an ethical manner’ (Adapted from CILIP, 2004 and Qin & D’Ignazio, 2010)20/04/11 © University of Sheffield / Information School / Sheila Corrall
  22. 22. Points for reflection and discussion•  How should we incorporate data literacy into information literacy frameworks? −  Amend current definitions, models and standards? −  Produce expanded versions of existing statements? −  Develop discipline-based frameworks for information and data literacy?•  How should we provide data literacy education? −  Standalone or integrated? −  Part of research methods, theory course or integrated across curricula?•  Who should teach and support learners? −  Librarians, academic domain experts, LIS academics?20/04/11 © University of Sheffield / Information School / Sheila Corrall

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