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

Data Literacy Conceptions and Pedagogies

Professor Sheila Corrall Centre for Information Literacy Research

Redefining Information Literacy Frameworks for the 21st Century

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 players 20/04/11 © University of Sheffield / Information School / Sheila Corrall

Libraries, librarians and data ‘Providing data services is a natural fit for the academic library's core mission of helping users find information in a variety of formats’ (Read, 2007: 72)

‘Datasets are heavier, more feral, and require more resources than, say, monograph shipments or e-journal subscriptions, but managing and improving the organization of and access to them is still the obligation of the library and 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

What is Data Literacy?


Who should be developing knowledge and skills in dealing with data?

Conceptions of data literacy (1) A social science perspective Data literacy almost synonymous with statistical literacy, quantitative literacy and numeracy – but involving more than 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 stats

Data literacy is an essential and critical component of information competence in social sciences

(e.g. Read, 2007; Schield, 1999; Stephenson & Caravello, 2007)

© University of Sheffield / Information School / Sheila Corrall 20/04/11

© University of Sheffield / Information School / Sheila Corrall

Conceptions of data literacy (2)

Analysis, Interpretation, Evaluation Analysis, Interpretation, Evaluation

Information Literacy

Information Literacy

Statistical Literacy Statistical Literacy

Data Literacy

Data Literacy

CRITICAL THINKING SOCIAL SCIENCE DATA

Critical thinking perspective Discipline perspective

Alternative (hierarchical) social science perspectives

(Schield, 2004) 20/04/11

Conceptions of data literacy (3) A science (STEM/information science) perspective Science data literacy shares aspects of social science conceptions, 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 data

SDL goes beyond ‘pushing’ the data to students by developing abilities and skills in ‘pulling’ data

(Qin & D’Ignazio, 2010) © University of Sheffield / Information School / Sheila Corrall 20/04/11

Lead on local (Univ) data

policy Develop local data curation capacity

Identify required data skills with LIS

schools

Bring data into UG research-

based learning

Influence national data

policy

Teach data literacy to post-

graduates Develop library

workforce data

confidence

Provide researcher data advice

Develop researcher

data awareness

Research data management pyramid for libraries

Strategic and operational roles

for research libraries

(Lewis, 2010: 154) © University of Sheffield / Information School / Sheila Corrall 20/04/11

Examples of tactical adaptation of existing LIS 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)


‘Scientific datasets may be thought of as the ‘special collections’ of the digital age’ (Choudhury, 2008: 218)

20/04/11

Pedagogies for data literacy (1) McGill Libraries Electronic Data Resources Service Supporting 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) © University of Sheffield / Information School / Sheila Corrall 20/04/11


Pedagogies for data literacy (2) UCLA 105 Sociology Information Literacy Lab Developing 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)


Pedagogies for data literacy (3) Calgary 311 Biology Information Literacy Lab Incorporating 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) © University of Sheffield / Information School / Sheila Corrall 20/04/11


Pedagogies for data literacy (4) Purdue Libraries GIS Librarian Raising 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) © University of Sheffield / Information School / Sheila Corrall 20/04/11


Pedagogies for data literacy (5) Syracuse Science Data Management Course Learning 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) © University of Sheffield / Information School / Sheila Corrall 20/04/11


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)



Redefining frameworks Should we develop more subject-specific models?

20/04/11

Redefining frameworks for the 21C •  Should we update our

literacy definitions: −  add scope notes?

−  insert ‘data’ into the text as appropriate?

−  produce statements to supplement existing definitions?

Plain English definition? ‘Data literacy is knowing when and why you need data, where to find them, what their attributes are, and how to evaluate, process, use, manage and communicate them in an ethical manner’

(Adapted from CILIP, 2004 and Qin & D’Ignazio, 2010)


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?


Education

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