Strategies and Best Practices for Data Literacy Education Knowledge Synthesis Report

Technical Report (PDF Available) · January 2015with 1,891 Reads 
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DOI: 10.13140/RG.2.1.1922.5044
Affiliation: SSHRC
Cite this publication
Abstract
Key Messages BACKGROUND: We begin with a definition, synthesized from existing literature and refined based on expert input: Data literacy is the ability to collect, manage, evaluate, and apply data, in a critical manner. It is an essential ability required in the global knowledge-­‐‑ based economy; the manipulation of data occurs in daily processes across all sectors and disciplines. An understanding of how decisions are informed by data, and how to collect, manage, evaluate, and apply this data in support of evidence-­‐‑ based decision-­‐‑ making, will benefit Canadian citizens, and will increasingly be required in knowledge economy jobs. Data literacy education is currently inconsistent across the public, private, and academic sectors, and data literacy training has not been approached systematically or formally at Canada'ʹs post-­‐‑ secondary institutions. There are also per-­‐‑ sector capability gaps, which makes it difficult to set realistic expectations of data-­‐‑ based skills.
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Strategies and Best Practices
for Data Literacy Education
Knowledge Synthesis Report
Chantel Ridsdale, James Rothwell, Mike Smit,
Hossam Ali-Hassan, Michael Bliemel, Dean Irvine,
Daniel Kelley, Stan Matwin, and Brad Wuetherick
Key$Messages$
BACKGROUND:$!"#$"%&'#(&)*#+#,"-&'&)&.'/#01')*"0&2",#-3.4#"5&0)&'%#6&)"3+)73"#+',#3"-&'",#$+0",#
.'#"58"3)#&'87)9#Data$literacy$is$the$ability$to$collect,$manage,$evaluate,$and$apply$data,$in$a$
critical$manner.$:)#&0#+'#"00"')&+6#+$&6&)1#3";7&3",#&'#)*"#%6.$+6#<'.(6",%"=$+0",#">.'.41?#)*"#
4+'&876+)&.'#.-#,+)+#.>>730#&'#,+&61#83.>"00"0#+>3.00#+66#0">).30#+',#,&0>&86&'"0@#A'#7',"30)+',&'%#
.-#*.(#,">&0&.'0#+3"#&'-.34",#$1#,+)+/#+',#*.(#).#>.66">)/#4+'+%"/#"B+67+)"/#+',#+8861#)*&0#,+)+#&'#
0788.3)#.-#"B&,"'>"=$+0",#,">&0&.'=4+<&'%/#(&66#$"'"-&)#C+'+,&+'#>&)&2"'0/#+',#(&66#&'>3"+0&'%61#$"#
3";7&3",#&'#<'.(6",%"#">.'.41#D.$0@#E+)+#6&)"3+>1#",7>+)&.'#&0#>733"')61#&'>.'0&0)"')#+>3.00#)*"#
87$6&>/#83&B+)"/#+',#+>+,"4&>#0">).30/#+',#,+)+#6&)"3+>1#)3+&'&'%#*+0#'.)#$""'#+883.+>*",#
010)"4+)&>+661#.3#-.34+661#+)#C+'+,+F0#8.0)=0">.',+31#&'0)&)7)&.'0@#G*"3"#+3"#+60.#8"3=0">).3#
>+8+$&6&)1#%+80/#(*&>*#4+<"0#&)#,&--&>76)#).#0")#3"+6&0)&>#"58">)+)&.'0#.-#,+)+=$+0",#0<&660@##
CONSIDERATIONS:$E"B"6.8&'%#)*"#0.6&,#-.7',+)&.'+6#<'.(6",%"#.-#,+)+#6&)"3+>1#&0#&')"%3+6#).#
$7&6,&'%#,&0>&86&'"=H,.4+&'=08">&-&>#<'.(6",%"#+',#"'073&'%#)*+)#>&)&2"'0#+3"#+$6"#).#70"#+',#+8861#
)*"0"#0<&660#+883.83&+)"61#+',#,&B"30"61#)*3.7%*.7)#)*"&3#8"30.'+6#+',#83.-"00&.'+6#6&B"0@#G*"#$"0)#
86+>"#).#$"%&'#)*&0#&'&)&+)&B"#&0#)*"#7',"3%3+,7+)"#>733&>7674#&'#8.0)=0">.',+31#&'0)&)7)&.'0/#,7"#&'#
8+3)#).#)*"&3#.B"3+3>*&'%#%.+6#.-#83.,7>&'%#%6.$+661#>.48")&)&B"/#>3&)&>+661#)*&'<&'%/#("66=";7&88",#
%3+,7+)"0@#I.0)=0">.',+31#>733&>76+#+63"+,1#&')3.,7>"#0)7,"')0#).#'"(#)*".3&"0#+',#83+>)&>"0/#+',#).#
'"(#-.340#.-#6&)"3+>1#07>*#+0#&'-.34+)&.'#6&)"3+>1#+',#>.487)+)&.'+6#)*&'<&'%@#G("')1=-&30)#>"')731#
83.$6"40#3";7&3"#)("')1=-&30)#>"')731#0<&660#JI"')6+',/#KLMNO?#+,,&'%#,+)+#6&)"3+>1#"586&>&)61#).#
7',"3%3+,7+)"#>733&>76+#(&66#*"68#"'073"#%3+,7+)"0#(&66#$"#$"))"3#";7&88",#).#4"")#)*"#,+)+#0<&660#
%+8#&'#C+'+,+F0#J+',#)*"#%6.$+6O#(.3<-.3>"@##
FINDINGS$AND$BEST$PRACTICES:$E+)+#6&)"3+>1#",7>+)&.'#3";7&3"0#4")*.,0#)*+)#"'%+%"#+',#
4.)&B+)"#0)7,"')0/#+0#("66#+0#"'>.73+%"#)+0<#>.44&)4"')@#P"0)#83+>)&>"0#-.3#)"+>*&'%#,+)+#6&)"3+>1#
",7>+)&.'#&'>67,"#>.66+$.3+)&.'#$")(""'#",7>+).30/#.3%+'&2+)&.'0/#+',#&'0)&)7)&.'0#).#"'073"#%.+60#
+3"#$"&'%#4")#$1#+66#0)+<"*.6,"30?#,&B"30"#+',#>3"+)&B"#)"+>*&'%#+883.+>*"0#+',#"'B&3.'4"')#
&'>67,&'%#)*"#"--">)&B"#70"#.-#)">*'.6.%1?#07>>"00&B"H&)"3+)&B"#6"+3'&'%#(&)*#>.486"4"')+31#0<&660#
&')"%3+)",#J"@%@#83.D">)=$+0",#6"+3'&'%O?#"48*+0&2&'%#4">*+'&>0#&'#+,,&)&.'#).#>.'>"8)0#J&@"@#
83+>)&>+6/#*+',0#.'#6"+3'&'%O?#+',#&'>3"+0&'%#"'%+%"4"')#(&)*#)*"#>.')"')#$1#70&'%#3"+6#(.36,#,+)+@#
C.730"0#$7&6)#.'#)*&0#4.,"6#(&66#>.''">)#6"+3'&'%#(&)*#>.')3&$7)&'%#).#0.>&")1#.3#8"30.'+6#&')"3"0)0/#
+',#"'>.73+%"0#$.)*#&'=0>*..6#+',#6&-"6.'%#6"+3'&'%@#!"#*+B"#+60.#&,"')&-&",#%+80#&'#.73#>.66">)&B"#
7',"30)+',&'%#.-#,+)+#6&)"3+>1#",7>+)&.'/#(*&>*#(&66#3";7&3"#-73)*"3#3"0"+3>*@#
DATA$LITERACY$COMPETENCIES:#!"#*+B"#01')*"0&2",#+#0")#.-#0<&660#+',#+$&6&)&"0#)*+)#).%")*"3#
>.483&0"#B+3&.70#6"B"60#.-#,+)+#6&)"3+>1/#(*&>*#("#83"0"')#&'#+#data$literacy$competencies$matrix,#
.3%+'&2",#$1#)*"#-&B"#>.3"#+08">)0#.-#.73#,+)+#6&)"3+>1#,"-&'&)&.'#J,+)+/#>.66">)&.'/#4+'+%"4"')/#
"B+67+)&.'/#+886&>+)&.'O@#G*&0#4+)3&5#&0#&')"',",#).#-.34#)*"#$+0&0#.-#.'%.&'%#>.'B"30+)&.'0#+$.7)#
0)+',+3,0#-.3#+00"00&'%#+',#"B+67+)&'%#6"B"60#.-#,+)+#6&)"3+>1/#+',#).#&'-.34#)*"#>3"+)&.'#.-#6"+3'&'%#
.7)>.4"0#&'#,+)+#6&)"3+>1#",7>+)&.'@##
CONCLUSION:$Q.3#)*"#$"'"-&)#.-#0)7,"')0/#"486.1"30/#+',#0.>&")1/#,+)+#6&)"3+>1#470)#$"#
3">.%'&2",#+0#+#'">"00+31#>&B&>#0<&66#JR(+'#")#+6@/#KLLSO@#G*&0#3">.%'&)&.'#0*.76,#>.4"#-3.4#+66#6"B"60#
.-#%.B"3'4"')/#+',#-3.4#8.0)=0">.',+31#&'0)&)7)&.'0@#G*"3"#'"",0#).#$"#+%3""4"')#.'#(*+)#
"6"4"')0#.-#,+)+#6&)"3+>1#+3"#'">"00+31#&'#+'#7',"3%3+,7+)"#>.3"#>733&>7674/#&'#.3,"3#).#83.B&,"#+#
>.'0&0)"')#-.7',+)&.'+6#",7>+)&.'#-.3#)*.0"#"')"3&'%#+'#&'>3"+0&'%61#,+)+=,"8"',"')#(.3<-.3>"@#
2
Executive)Summary)
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?8@#-A#'+"#B-%5&C,#&$'$#+$&#*->"#)2'-#"D),'"2*"#B)'+)2#'+"#0%"1)-E,#7#."$%,#3F",,"'#"'#$5<6#789G;<#
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#
Conceptual
Framework
Introduction to Data
Evaluating and
Ensuring Quality of
Data and Sources
Data
Organization
Data
Manipulation
Data Conversion
(from format to format)
Metadata
Creation
and Use
Data Curation,
Security, and
Re-Use
Data
Preservation
Data Collection
Data Management
Data Tools
Basic Data
Analysis
Data Interpretation
(Understanding Data)
Identifying
Problems
Using Data
Data
Visualization
Presenting
Data
(Verbally)
Data Driven Decisions
Making (DDDM) (Making
decisions based on data)
Critical
Thinking
Data Culture Data Ethics Data Citation Data Sharing
Evaluating
Decisions
Based on Data
Data Evaluation
Data Application
3
Context)and)Strategic)Value)of)Data)Literacy)Education)
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'+)2M)2I<#4-,'(,"*-2&$%.#)2,')'E')-2,#>E,'#*-2,)&"%#&$'$#5)'"%$*.#)2#)',#2$')-2$5#*-2'"D'6#)&"2')A.#+-B#
$2&#B+"%"#"5">"2',#-A#&$'$#5)'"%$*.#$%"#L")2I#'$EI+'#)2#'+")%#"D),')2I#*-E%,",#$2&#0%-I%$>,6#
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4
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,"*-2&$%.#)2,')'E')-2,<##
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A%->#'+"#L"I)22)2I<#K+),#),#0$%')*E5$%5.#'%E"#A-%#>)&(*$%""%#5"$%2"%,6#B+-#B)55#L"#>-%"#B)55)2I#'-#
)21",'#'+")%#5)>)'"&#')>"#$2&#"AA-%'#)A#'+".#,""#'+"#-00-%'E2)'.#'-#+"50#'+")%#*->>E2)'.6#)2&E,'%.6#
A$>)5.6#-%#-'+"%,<##
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2""&"&#'-#AE55.#E2&"%,'$2&#$#'"*+2)*$5#,M)55/#,'E&"2',#2""&#'+"#*+$2*"#'-#A)IE%"#-E'#0%-*",,",#$2&#
>"'+-&,#-2#'+")%#-B2#$2&#>$M"#>),'$M",#'-#%"$&SE,'#'+")%#-B2#E2&"%,'$2&)2I<#N"*+$2)*,#$%"#1"%.#
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)2*5E&"#$#B)&"#%$2I"#-A#)21",')I$')-2#$2&#+$1"#%"$5(B-%5&#$005)*$L)5)'.#B)55#,-5)&)A.#'+"#*-22"*')-2#
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,'E&"2',#'+"#-00-%'E2)'.#'-#I-#AE%'+"%#'+$2#.-E#"D0"*'<#
=2'"I%$')2I#&$'$#5)'"%$*.#'"$*+)2I#)2'-#"D),')2I#,ELS"*',#'+$'#>$M"#E,"#-A#,->"#"5">"2'#-A#&$'$#
5)'"%$*.#),#$#B$.#'-#)2'"I%$'"#'+"#,.,'">$')*#$2&#A-%>$5#'"$*+)2I#-A#&$'$#5)'"%$*.#)2'-#$5%"$&.(AE55#
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Research)Gaps)and)Further)Work)
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L.#"D),')2I#B-%M<#K+","#)2*5E&"#I"-,0$')$5#&$'$#5)'"%$*.#$2&#P=R/#,"*'-%(,0"*)A)*#$2&#)2&E,'%.(&%)1"2#
&$'$#5)'"%$*.#%"^E)%">"2',#B)'+#)20E'#A%->#-E',)&"#-A#$*$&">)*#)2,')'E')-2,/#2-#,'$2&$%&#A-%#
$,,",,)2I#-%#"1$5E$')2I#&$'$#5)'"%$*.#5"1"5,/#&$'$#,"*E%)'.#'%$)2)2I#A-%#,'E&"2',#B)'+-E'#$#*->0E'"%#
,*)"2*"#L$*MI%-E2&/#'+"#"'+)*,#-A#&$'$#$2&#&$'$(&%)1"2#&"*),)-2(>$M)2I/#$2&#+-B#'-#0%-1)&"#&$'$#
5)'"%$*.#'%$)2)2I#'-#'+"#"D),')2I#B-%MA-%*"#)2#$&&)')-2#'-#2"B#I%$&E$'",<##HE%#'"$>#B)55#*-2')2E"#
B-%M#)2#'+),#$%"$/#B"#$%"#&"1"5-0)2I#$#&$'$#5)'"%$*.#$,,",,>"2'#'--56#B"#+$1"#$005)"&#A-%#$*$&">)*#
)22-1$')-2#AE2&)2I#'-#0%-&E*"#*-E%,"#>$'"%)$5,#L$,"&#-2#'+"#%",E5',#-A#'+),#,.2'+",),6#$2&#B"#B)55#
,+$%"#'+"#M2-B5"&I"#B"`1"#,.2'+",)J"&#)2#$00%-0%)$'"#1"2E",<#K+),#%"0-%'#$2&#-'+"%#%",-E%*",#
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5
Table&of&Contents&
!
Key!Messages!......................................................................................................................................................!1!
Executive!Summary!...........................................................................................................................................!2!
Context!and!Strategic!Value!of!Data!Literacy!Education!.........................................................................!3!
Best!Practices!for!Data!Literacy!Education!.................................................................................................!4!
Research!Gaps!and!Further!Work!................................................................................................................!4!
Table!of!Contents!................................................................................................................................................!5!
Implications!.........................................................................................................................................................!7!
Results!................................................................................................................................................................!10!
Toward!a!Shared!Understanding!of!Data!Literacy!..................................................................................!10!
Context!and!Strategic!Value!of!Data!Literacy!Education!.......................................................................!11!
Building!a!21st!Century!Citizenry!..........................................................................................................!11!
Barriers!and!Challenges!.......................................................................................................................!12!
Canadian!Employers!and!Economy!.......................................................................................................!14!
Right!Tools,!Right!Job:!Data!Literacy!Skills!for!the!Workforce!......................................................!14!
Go!Fish:!Matching!Skills!with!Requirements!....................................................................................!15!
Making!the!Call:!Data!Management!and!Decision!Making!............................................................!15!
Barriers!and!Challenges!.......................................................................................................................!16!
Canadian!Universities!and!Graduates!...................................................................................................!17!
Team!Effort:!Collaborating!to!Deliver!Data!Literacy!Education!....................................................!17!
Barriers!and!Challenges!.......................................................................................................................!18!
Best!Practices!for!Teaching!Data!Literacy!.................................................................................................!18!
Appropriate!Timing!of!Data!Literacy!Education!.................................................................................!19!
Delivery:!From!Courses!to!Workshops!.................................................................................................!19!
Integration!of!Data!Literacy!into!Curricula!..........................................................................................!20!
Emerging!Teaching!Approaches!and!Learning!Environments!.........................................................!21!
Engaging!content!with!real!world!data!to!foster!innovation!.............................................................!21!
Successive/Iterative,!Practical,!Hands]on!Learning!.............................................................................!21!
6
Assessment!and!Evaluation!....................................................................................................................!22!
Additional!Resources!.......................................................................................................................................!23!
Further!Research/Research!Gaps!...................................................................................................................!26!
Geospatial/Temporal!Data!Management!..............................................................................................!26!
Lack!of!Agreed!Upon!Standards!and!Best!Practices!...........................................................................!26!
Data!Security!.............................................................................................................................................!27!
Data!Ethics!.................................................................................................................................................!27!
Data!Literacy!for!the!Existing!Workforce!..............................................................................................!27!
Knowledge!Mobilization!.................................................................................................................................!27!
References!..........................................................................................................................................................!29!
Appendices!........................................................................................................................................................!36!
!
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!
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7
Context'
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+,%('#'%"*%#$&%<+.1(%"3%-+*#"*0"*2%#+%2.+<%'#%'*%"*-.&(":1&%.'#&@%5#%<'3%&3#"/'#&(%"*%CDEC%#$'#%ZD[%+,%
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19
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20
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Further'Research/Research'Gaps''
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Geospatial/Temporal'Data'Management''
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'
Data'Literacy'Requirements'informed'by'Employer'Need'
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<$'#%"*(03#.A%&/>1+A&.3%'.&%'-#0'11A%1++="*2%,+.%<$&*%$"."*2%*&<%2.'(0'#&3@%%
Lack'of'Agreed'Upon'Standards'and'Best'Practices'
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27
Data'Security'
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Data'Ethics'
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Data'Literacy'for'the'Existing'Workforce'
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Knowledge'Mobilization'
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36
Appendices)
!
Appendix!1:!Data!Literacy!Competencies!Matrix!
Appendix!2:!Data!Literacy!Definitions!Word!Cloud!
Appendix!3:!Key!Themes!in!Data!Literacy!Literature!
Appendix!4:!Annotated!Bibliography!
37
Appendix(1(*(Data(Literacy(Competencies(Matrix
This%matrix%consists%of%key%data%literacy%ability/knowledge%areas,%and%the%corresponding%competencies%and%tasks%required%for%each.%
Definition:%Data%literacy%is%the%ability%to%collect,%manage,%evaluate,%and%apply%data;%in%a%critical%manner.%
Key Ability/Knowledge Area Competency Knowledge/Tasks LEGEND: Conceptual Competencies Core Competencies Advanced Competencies
Conceptual Framework Introduction to Data Knowledge and understanding of data
Knowledge and understanding of the uses and
applications of data
Data Collection
Data Discovery and
Collection
Performs data exploration Identifies useful data Collects data
Evaluating and Ensuring
Quality of Data and Sources
Crtically assesses sources of data for
trustworthiness
Critically evaluates quality of datasets for errors
or problems
Data Organization
Knowledge of basic data organization
methods and tools
Asesses data organization requirements Organizes data
Data Manipulation Asesses methods to clean data Identifies outliers and anomalies Cleans data
Data Management
Data Conversion (from
format to format)
Knowledge of different data types and
conversion methods
Converts data from one format or file type to
another
Metadata Creation and Use Creates metadata descriptors
Assigns appropriate metadata descriptors to
original data sets
Data Curation, Security, and
Re-Use
Assesses data curation requirements
(e.g. retention schedule, storage,
accessibility, sharing requirements, etc.)
Assess data security requirements (e.g.
restricted access, protected drives, etc.)
Curates data
Data Preservation Assesses requirements for preservation Asseses methods and tools for data preservation Preserves data
Data Tools
Knowledge of data analysis tools and
techniques
Selects appropriate data analysis tool or
technique
Applies data analysis tools and
techniques
Basic Data Analysis Develops analysis plans Applies analysis methods and tools Conducts exploratory analysis Evaluates results of analysis
Compares results of analysis with
other findings
Data Interpretation
(Understanding Data)
Reads and understands charts, tables,
and graphs
Identifies key take-away points, and integrates
this with other important information
Identifies discrepancies within
the data
Data Evaluation
Identifying Problems Using
Data
Uses data to identify problems in
practical situations (e.g. workplace
efficiency)
Uses data to identify higher level problems (e.g.
policy, environment, scientific experimentation,
marketing, economics, etc.)
Data Visualization
Creates meaningful tables to organize
and visually present data
Creates meaningful graphical representations of
data
Evaluates effectiveness of
graphical representations
Critically assesses graphical
representations for accuracy and
misrepresentation of data
Presenting Data (Verbally)
Asssess the desired outcome(s) for
presenting the data
Assesses audience needs and familiarity with
subject(s)
Plans the appropriate meeting or
presentation type
Utilizes meaningful tables and
visualizationsto communicate data
Presents arguments and/or outcomes
clealy and coherently
Data Driven Decisions
Making (DDDM) (Making
decisions based on data)
Prioritizes information garnered from
data
Converts data into actionable information
Weighs the merit and impacts of
possible solutions/decisions
Implements decisions/solutions
Critical Thinking
Aware of high level issues and
challlenges associated with data
Thinks critically when working with data
Data Culture
Recognizes the importance of data
Supports an environment that fosters critical use
of data for learning, research, and decision-
making
Data Application
Data Ethics
Aware of legal and ethical issues
associated with data
Applies and works with data in an ethical manner
Data Citation
Knowledge of widely-accepted data
citation methods
Creates correct citations for secondary data sets
Data Sharing
Assesses methods and platforms for
sharing data
Shares data legally, and ethically
Evaluating Decisions Based
on Data
Collects follow-up data to assess
effectiveness of decisions or solutions
based upon data
Conducts analysis of follow-up data
Compares results of analysis
with other findings
Evaluates decisions or solutions
based on data
Retains original conclusions or
decisions, or implements new
decisions/solutions
38
Competency LEGEND:
Conceptual
Competencies
Core
Competencies
Advanced
Competencies
Authors/Source
Introduction to Data Critical Thinking Data Culture Data Ethics Data Tools
Data Discovery
and Collection
Data Management and
Organization
Data
Manipulation
Evaluating and
Ensuring Quality of
Data and Sources
Carlson, Fosmire, Miller, and
Sapp Nelson (2011; Carlson,
Johnston, Westra, and
Nichols, 2012)
* * * * * *
Koltay (2014) * * *
Prado and Marzal (2013) * * * *
Gummer and Mandinach
(2015)
Qin and D'Ignazio (2010) * * * *
Gray (2004) * *
Haendel, Vasilevsky, and Wirz
(2012)
* *
Reeves and Honig (2015) * *
Schield (2004) * *
Ikemoto and Marsh (2008) * *
MacMillan (2010) * *
Shorish (2015) * * * * *
Stephenson an Caravello
(2007)
* * * * *
Teal, Cranston, Lapp, White,
Wilson, Ram, and Pawlik
(2015)
* *
Wright, Fosmire, Jeffryes,
Bracke, and Westra (2012)
* * *
Cowan, Alencar, and McGarry
(2014)
* * *
Gunter (2007) * * * * *
MacMillen (2014) * *
McAuley, Rahemtulla,
Goulding, and Souch (2010)
* * *
Mooney and Carlson (2014) * * * * * * *
Sapp Nelson, Zilinski, and Van
Epps (2014)
* *
Scheitle (2006) * * *
Stout and Graham (2007) * * *
Swan and Brown (2008) * * *
Twidale, Blake, and Grant
(2013)
*
*
Womack (2014) * *
Association of College and
Research Libraries (2013)
* * * *
Association of College and
Research Libraries (2014)
* * *
Doucette and Fyfe (2013) *
McKendrick (2015) * * *
Swan, Vahey, Kratcoski, van
t`Hooft, Rafanan, Stanford
(2009)
* *
* *
Zalles (2005) *
Totals (across) 7 6 2 7 14 18 20 516
39
Authors/Source
Carlson, Fosmire, Miller, and
Sapp Nelson (2011; Carlson,
Johnston, Westra, and
Nichols, 2012)
Koltay (2014)
Prado and Marzal (2013)
Gummer and Mandinach
(2015)
Qin and D'Ignazio (2010)
Gray (2004)
Haendel, Vasilevsky, and Wirz
(2012)
Reeves and Honig (2015)
Schield (2004)
Ikemoto and Marsh (2008)
MacMillan (2010)
Shorish (2015)
Stephenson an Caravello
(2007)
Teal, Cranston, Lapp, White,
Wilson, Ram, and Pawlik
(2015)
Wright, Fosmire, Jeffryes,
Bracke, and Westra (2012)
Cowan, Alencar, and McGarry
(2014)
Gunter (2007)
MacMillen (2014)
McAuley, Rahemtulla,
Goulding, and Souch (2010)
Mooney and Carlson (2014)
Sapp Nelson, Zilinski, and Van
Epps (2014)
Scheitle (2006)
Stout and Graham (2007)
Swan and Brown (2008)
Twidale, Blake, and Grant
(2013)
Womack (2014)
Association of College and
Research Libraries (2013)
Association of College and
Research Libraries (2014)
Doucette and Fyfe (2013)
McKendrick (2015)
Swan, Vahey, Kratcoski, van
t`Hooft, Rafanan, Stanford
(2009)
Zalles (2005)
Totals (across)
Data Citation
Basic Data
Analysis
Data
Visualization
Presenting Data
(Verbally)
Data Interpratation
(Understanding Data)
Identifying
Problems Using
Data
Data Driven Decision
Making (DDDM)
Evaluating
Decisions/Conclusions
Based on Data
Metadata
Creation and Use
* * *
*
* *
* * * *
* *
*
* * * *
*
* * * *
*
*
*
*
*
* *
* *
*
*
*
* *
* * * * *
*
*
* * *
* *
*
*
*
* * * * * *
* * *
* * * * *
410 9 4 13 214 4 6
40
Authors/Source
Carlson, Fosmire, Miller, and
Sapp Nelson (2011; Carlson,
Johnston, Westra, and
Nichols, 2012)
Koltay (2014)
Prado and Marzal (2013)
Gummer and Mandinach
(2015)
Qin and D'Ignazio (2010)
Gray (2004)
Haendel, Vasilevsky, and Wirz
(2012)
Reeves and Honig (2015)
Schield (2004)
Ikemoto and Marsh (2008)
MacMillan (2010)
Shorish (2015)
Stephenson an Caravello
(2007)
Teal, Cranston, Lapp, White,
Wilson, Ram, and Pawlik
(2015)
Wright, Fosmire, Jeffryes,
Bracke, and Westra (2012)
Cowan, Alencar, and McGarry
(2014)
Gunter (2007)
MacMillen (2014)
McAuley, Rahemtulla,
Goulding, and Souch (2010)
Mooney and Carlson (2014)
Sapp Nelson, Zilinski, and Van
Epps (2014)
Scheitle (2006)
Stout and Graham (2007)
Swan and Brown (2008)
Twidale, Blake, and Grant
(2013)
Womack (2014)
Association of College and
Research Libraries (2013)
Association of College and
Research Libraries (2014)
Doucette and Fyfe (2013)
McKendrick (2015)
Swan, Vahey, Kratcoski, van
t`Hooft, Rafanan, Stanford
(2009)
Zalles (2005)
Totals (across)
Data Curation
and Re-Use
Data Sharing
Data
Preservation
Data Conversion (from
format to format)
* * *
* * *
*
*
*
* *
* *
*
* * *
*
* *
*
* *
* *
*
* *
*
*
*
9 5 9 8
41
Appendix 2 - Data Literacy Word Cloud
The following is a word cloud generated from the major definitions of data literacy in the reviewed literature.
!
42
Appendix 3 Key Themes
The following are key themes derived from the literature reviewed (i.e. emergent
themes). Although the implications of the knowledge synthesis are based on practical,
actionable implications of data literacy, these themes are present through the results of
the knowledge synthesis.
1. Delivery and Assessment
In terms of top-level delivery, the literature has identified three main learning environments for
teaching data literacy at the postsecondary level: in-class, through workshops, and online
courses. Classes and workshops often take a module-based approach. This allows instructors to
focus on specific aspects of data literacy as needed, and for students to build competencies
successively (MIT, 2014 ;Qin & D’Ignazio, 2010; Schneider, 2013; Shorish, 2015; Stephenson &
Caravello, 2007; Wright, Fosmire, Jeffryes, Bracke & Westra, 2012). There are multiple
techniques and tools for evaluating data literacy competencies, and most courses or workshops
include a pre and post-assessment.
Teaching Environments
In-class courses
Courses embedded in the curriculum can be an effective way to teach
data literacy to students (Martin & Leger-Hornby, 2012),
Due to institutions struggling to provide a wide variety of courses,
integrating data literacy into existing information literacy courses could
be the most feasible option (Carlson, Johnston, Westra, & Nichols, 2013;
Hattwig, Bussert, Medaille, & Burgess, 2013; Schneider, 2013; Teal,
Cranston, Lapp, White, Wilson, Ram, & Pawlik, 2015; Zilinski, Scherer, &
Maybee, 2013)
Data literacy could also be embedded into subject-specific courses as
targeted training (Hunt, 2004; Schneider, 2013; Teal, Cranston, Lapp,
White, Wilson, Ram, & Pawlik, 2015; Wright, Fosmire, Jeffryes, Bracke,
& Westra, 2012)
Developing foundational skills in-class is important, but encouraging
students to improve these skills through experimentation at home is
essential to the level of skill they develop (Romani, 2009).
Workshops
Workshops can be used to fill the inevitable gaps in knowledge for
students (Carlson, Johnston, Westra, & Nichols, 2013; Hattwig, Bussert,
Medaille, & Burgess, 2013; MacMillen, 2014).
Academic libraries and librarians (e.g. data librarians) have a key role to
play. Can provide workshops, in-class support, and/or act as liaisons to
faculty members (Prado & Marzal, 2013; Schield, 2004; Stout & Graham,
2007).
Online courses
Online courses are helpful in developing skills, but students require
interaction and targeted teaching methods, according to skill
development (Gray, 2004; MacMillen, 2014).
43
MIT (2014) offers a comprehensive online course entitled Tackling the
Challenges of Big Data which has been maximized into a versatile,
focused, skill developing class.
Assessment
A pre-course inventory of technological and information skills is useful to
determine pre-existing skillsets, as well as gaps that require specific attention
(Carlson, Fosmire, Miller, & Sapp Nelson, 2011; Chinien & Boutin, 2011; Teal,
Cranston, Lapp, White, Wilson, Ram, Pawlik, 2015).
Ongoing, iterative evaluation through informal assessment (e.g. conversations,
office hours), and critical assessment (e.g. tests, assignments) is important in
order to build confidence and mastery (Carlson, Fosmire, Miller, & Sapp Nelson
2011; Hattwig, Bussert, Medaille, & Burgess, 2013; Koltay, 2014).
Post-course assessment of students for perceived relevancy and mastery is also
important (Carlson, Fosmire, Miller, & Sapp Nelson, 2011).
Assessment tools should encompass both process, as well as end results
(Liquete, 2012).
Methods include pre-screening and testing, self-assessment, demonstration of
data tools, online assessment tools, practical case study assignments,
performance assessment as an indicator, test of workplace essential skills
(TOWES), and academic-style tests (Carlson, Fosmire, Miller, & Sapp Nelson,
2011; Chinien & Boutin, 2011; Romani, 2009; Teal, Cranston, Lapp, White,
Wilson, Ram, Pawlik, 2015)
Assessment of the course and professors from students is also crucial, so as to
improve delivery of content and instruction (Johnson & Jeffryes, 2014)
2. Barriers to Effective Data Literacy Instruction
Barriers to teaching data literacy at the postsecondary level can be both tangible and intangible.
These barriers can be further broken into three areas: cultural, operational, and technical.
Cultural:
Lack of support/conditions for innovative learning, e.g. creativity, risk-taking
(Johnson, Adams Becker, Estrada, & Freeman, 2015).
Lack of university faculty trust in benefits of data literacy (Ikemoto & Marsh, 2008;
Johnson, et al 2015).
Mixed support for different data literacy competencies, e.g. teaching some
aspects of data literacy, but not others (Carlson, Johnston, Westra, & Nichols,
2013)
Abstract nature and perceived complexity of data literacy and related concepts
(Qin & D’Ignazio, 2010; Twidale, Blake, & Grant, 2013).
Misconception that the Net Generation/Digital Natives are inherently more
knowledgeable technically than past students (Thompson, 2012).
Operational:
Modifying existing curriculum to include room for course(s) on data literacy (Hunt,
2004; Teal, Cranston, Lapp, White, Wilson, Ram, & Pawik, 2015)
Not enough skilled professors, or not enough time to learn the required skills to
effectively teach a data literacy course (Boyles, 2012; Carlson, et al, 2013; Teal,
et al, 2015)
Poor communication regarding student learning goals, as well as a lack of
communication/collaboration between professors relating to what has been
44
covered in courses, and what should be developed for future courses (Cowan,
Alencar, & McGarry, 2014 ; Teal, et al,, 2015)
Differing levels of comfort and experience with technology and/or data, differing
levels of education (graduate vs. undergraduate), and age/generational
differences between students (Jones, Ramanau, Cross, & Healing, 2009;
Shorish, 2015)
Technical
Lack of proper storage and organizational tools for data, lack of lab space, lack of
access to databases, and lack of computers/other hardware. Cost is also a factor
(Stout & Graham, 2007)
3. Data Literacy Best Taught At The Commencement of Post-Secondary Studies
It has been found that teaching data literacy to new (i.e. first year) students is more effective, as
students have not yet learned advanced research methodologies.
This makes it is easier to ingrain good data literacy practices and competencies into
workflows and study habits (Hunt, 2004; Sapp Nelson, Zilinski, & Van Epps, 2014;
Shorish, 2015; Stephenson & Caravello, 2007; Womack, 2014).
Although still useful, teaching data literacy and related competencies late in a student’s
educational career limits their skills to very specific, discipline-focused areas (as opposed
to broader, more transferable areas) (Womack, 2014).
It was also found that workshops and courses open to all levels (e.g. undergraduate and
post-graduate) were more challenging to teach due to the inequality of students’ technical
skills and educational backgrounds (Qin & D’Ignazio, 2010; Scheitle, 2006; Womack,
2014).
Case studies and prac