Next Step: Data Literacy Measurement

Marketa Smolnikova

Department of Information Technology, University of Economics, W. Churchill Sq. 4, Prague, Czech Republic

Keywords: Data, Data Literacy, Data Literacy Indicator, Data Literacy Measurement, Knowledge Management.

Abstract: As data became a new business commodity, affecting our everyday lives from shopping to voting, it smoothed

the way for data literacy as a tool for full participation in a modern society. This paper argues for data literacy

development and accelerated research of its measurement which has been lagging behind countless studies

on teaching data skills. Data literacy is in this paper approached as an ability to understand and use data and

differentiates itself from information or statistical literacy. As a prerequisite of information literacy, data

literacy is inevitable part of knowledge development. While the term of data literacy has been well established

and used for developing best practices and methodologies to teach data skills, measurement of data literacy

seems to be still in its infancy. As a result, this paper includes research plan for developing a data literacy

indicator based on quantitative methods.

1 INTRODUCTION

Data is the new oil. It has become a common phrase

and easily accepted fact in the recent years. Why?

This “call for action” of Big Data and education

specialists (ODI 2015) briefly summarizes why we

label the present day as “a golden era of data” and

introduces the business case for data literacy:

“1. Our world economy and our jobs are

increasingly defined by data and by the knowledge

and skills required to use them effectively.

2. We are all perpetually producing streams of

data, which we need to shape and manage to ensure

our privacy and personal security.

3. Effective use of data empowers us to make

objective, evidence-based inferences and

fundamental decisions affecting our lives, both as

individuals and among societies.”

No wonder, Gartner also recognizes data as the

new core capability of business along with people,

processes, and technology. (Gartner 2018a)

Grillenberger and Romeike (2018) argue that

“knowing about the possibilities offered by data and

data analysis plays an increasing role for developing

an understanding of the world.” We manipulate data

in everyday processes regardless of sectors or

domains. That supports Ridsdale’s et al. (2015) view

https://orcid.org/0000-0002-2631-920X

that data literacy “is an essential ability required in

the knowledge-based economy”.

2 DATA LITERACY

However, to go “data-mindful” at full scale and to

enhance organization’s lead in the fierce competition

or to expand individual’s understanding and its future

options in employability, businesses, institutions,

schools, and its members require a certain level of

data literacy.

2.1 What Is Data Literacy

Ridsdale et al. (2015) define this type of literacy as

“the ability to collect, manage, evaluate and apply

data, in a critical manner”. Gartner (2018b) further

elaborates on the definition of data literacy by

articulation of four key barriers of data literate society

– the individual as well as organizational incapacity

to derive insights from their data, to understand the

analytical methods, to use analytical services to get

the insights or the incompetence to comprehend and

to integrate company’s data sources.

Yet we shouldn’t get intimidated with the above

definition and we should rather expound data literacy

as “the ability of non-specialists to make use of data”.

234

Smolnikova, M.

Next Step: Data Literacy Measurement.

DOI: 10.5220/0010146402340240

In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 3: KMIS, pages 234-240

ISBN: 978-989-758-474-9

Figure 1: The phases of data processing (Wolff et al. 2016).

(Frank et al. 2016) As Wolff et al. (2016) emphasizes,

a data literate person follows the same phases of data

processing as data scientists and knows how to make

use of them for its objectives. Nevertheless, the devil

is (obviously) in the detail as proposed in the Figure

1 in which the leftmost vertical arrow pointing

downwards suggests the level of expertise. While a

data literate person gets along with basic

understanding of the process and methods, data

scientists are professionals with profound knowledge

and skills in data management and advanced

statistical methods. In other words, data literacy

translates into being able to “read and speak data”, to

understand data and being able to make use of them,

in order to take a full part in society affected by the

availability and accessibility of vast volume of data.

2.2 It Does Not Equate with

Information or Statistical Literacy

In “the increasingly pervasive nature of data”

(Gartner, 2018b) first we need to learn to handle the

volume and characteristics of data (discrete, objective

facts) before we can draw information from it (make

data useful, enrich them with meaning). That is where

we demarcate a line between data literacy and

information literacy which ACRL (1989) specifies as:

“To be information literate, a person must be able to

recognize when information is needed and have the

ability to locate, evaluate, and use effectively the

needed information”. The difference arises from the

relation between data and information in which

information plays a role of a result of data processing

during which meaning is assigned to data and which

is explained in detail in the data-information-

knowledge-wisdom (DIKW) hierarchy (Rowley

2007). Thus in the world brimming over with data,

data literacy is a prerequisite of information literacy.

Even though data literacy inevitably draws on

statistical methods, it differentiates itself from

statistical literacy. According to Gould (2017), goal

of statistical literacy is “developing critical

consumers of statistics”. Gal (2002) calls it people’s

“ability to interpret and critically evaluate” statistical

products, as well as their ability to “discuss or

communicate their reactions” to statistical products.

Either way, both definitions anticipate statistical

literate persons to be only consumers of statistical

products which is in opposition to the view of a data

literate individual who is both consumer and producer

of data. It resonates with the view of Frank et al.

(2016) who argues that data literacy adds to statistical

literacy which developed first – in the era of limited

access to data when people had to rely on

intermediaries like press to access and interpret data

for them.

2.3 Its “Fit For” Knowledge

Management

The direct relationship between data literacy and

knowledge management does not seem to be in the

“research spotlight” yet. However, thanks to its

position in the data-information-knowledge-wisdom

pyramid, data literacy is evidently well-connected to

successful knowledge acquisition. As data literacy

serves as a precondition to become information

literate, the relation of data literacy to knowledge

management can be derived through its middleman –

the information literacy.

Proceeding from his analysis, O’Farrill (2008)

excellently names “the state of the relationship”

between knowledge management and information

Next Step: Data Literacy Measurement

235

literacy as “preparation for arranged marriage”. The

profound link between those fields is obvious, but it

is still waiting for its interconnection by academics

from two camps. O’Farrill emphasizes the learning

processes as the main meeting point of information

and knowledge and along with Marcum (2002)

criticizes that the “reception of information is equated

with knowledge acquisition in a rather unproblematic

way”. According to O’Farrill (2008) knowledge

management also “lack a robust understanding of

effective information use in the organization“ which

is supported by Oman (2001), Cheuk (2008) who

examined information literacy in company

environment and realised that the failure of many

knowledge management projects was caused by

inadequate information literacy skills.

In 2016 Virkus conducted a content analysis of

the literature about knowledge management and

information literacy published in the period of 1990-

2016. His study confirmed a strong link between

these fields which was supported by Whitworth

(2014) who believed that information literacy was

“an essential and integral competency for both

knowledge worker and effective knowledge

management“ or van Rooi and Snyman (2006) who

acknowledged corporate information literacy as one

of main knowledge management areas where library

and information science professionals can contribute.

Saito (2007) in his doctoral thesis even claims that

“knowledge management seemed to be a natural

extension to the field of library and information

sciences“. Nevertheless, Virkus concluded that

research of information literacy in the context of

knowledge management was insufficient and short of

empirical studies and he naturally followed the call

for further research in this topic of several authors

before him (e.g. Thompson 2003, De Saulles 2007).

The presented research of O’Farrill (2008) and

Virkus (2016) implies that not only data literacy, but

information literacy as well deserve more attention in

the context of knowledge management and require

further research.

2.4 Current State of the Data Literacy

Research

The term of data literacy is well established which

resulted in many different approaches to its

definition. Van der Wal et al. (2017) strengthen the

importance of data literacy as one of the techno-

mathematical skills necessary for graduates of

technical universities. Koltay (2015) circumscribes

data literacy in relation to other types of literacies like

information or statistical literacy; on the other hand,

Gould (2017) emphasizes data literacy as a part of

statistical literacy. Wang, Wu, Huang (2019), Burns,

Matthews (2018) or Halliday (2019) underline data

literacy in context of a specific field like safety

management or journalism while Prado and Marzal

(2013) call for complex approach to data literacy

definition. Gray, Gerlitz, Bounegru (2018) and

D’Ignazio, Bhargava (2015) also ask for expansion of

the term of data literacy (e.g. Big Data literacy or data

infrastructure literacy) to emphasize obvious aspects.

Pedersen and Caviglia (2019) perceive data literacy

as a compound competence and what is more, the

authors explore data literacy as a group competence.

Research of Grillenberger and Romeike (2018)

enriches the topic with a model of data literacy

competences which is clearly inspired by Ridsdale et

al. (2015).

Nevertheless, the measurement of data literacy

seems to be still in its infancy. Pratama and his team

(2020) has published a preliminary study of their

assessment instrument tested on 94 junior high

students which is according to their conclusions ready

to test initial level of data literacy. Another initiative

to measure data literacy also originates in south-east

Asia where a team of Lusiyana (2020) aims to prove

effectiveness of MIRECAL learning model.

Furthermore, there are also business initiatives

like QlikTech’s (2018) Data Literacy Project which

focuses on corporate data literacy whose

measurement has three components: employees’

individual data literacy skills, the accessibility of the

right data for decision-making in a given job position

and the widespread use of data across the company.

Based on the scores of corporate data literacy

QlikTech also came up with Data Literacy Index

which correlates data literacy levels to measures of

corporate performance and thus points out what

business value can company attain with a given level

of corporate date literacy.

In the field of teaching data literacy, the research

has been richer and more fragmented. In 2015

Maybee and Zilinski came up with a framework for

teaching data literacy based on a method of informed

learning, while D’Ignazio, Bhargava (2016) set

pedagogical principles to keep in mind when

developing tools or interactive applications for

teaching data literacy. The conceptual approach to

teaching data literacy was extended by Wolff,

Wermelinger and Petre (2019) who pilot a method for

teaching data literacy at middle schools on complex

data. Moreover, the social benefits of data literacy

and current educational models were examined in

Pangrazio, Sefton-Green (2019).

KMIS 2020 - 12th International Conference on Knowledge Management and Information Systems

236

More specific methods or approaches are brought by

the research of Wallner, Kriglstein (2011) or Gäbler

et a.l (2019) who focused on design of interactive

application and games. On the other hand Nolan and

Perret (2016) come up with “ideas and assignments”

how to effectively involve statistical visualizations

into undergraduate courses or Halliday (2019) who

developed set of exercises for economic students.

3 PLANNED RESEARCH

To improve anyone’s data literacy, first of all, it is

necessary to determine what the start line is and what

is to be achieved. That is why my research aims to

define Data Literacy Indicator to measure data

literacy as a level of maturity. Thus creating a

Capability Maturity Model (CMM) for Data Literacy

is an essential part of it.

The second part of the research is focused on the

design of methodology for teaching data literacy for

different levels of Data Literacy Indicator. As the

Data Literacy Indicator will generally define sets of

abilities needed to achieve a certain level of data

literacy, the indicator then will naturally serve as a

prescript of what skills need to be taught to move up

to a higher level.

3.1 Expected Contributions

The contribution of my research resides mostly in the

research artefacts. The Data Literacy Indicator as a

product of a maturity model brings two beneficial

aspects – while it naturally measures the level of data

literacy at a given moment, it also allows to generally

define sets of abilities needed to achieve a certain

level of data literacy. From this assumption, I derive

the usage of the Data Literacy Indicator.

I expect that the constructed indicator will be

suitable to measure individuals’ as well as

organizations’ data literacy. In companies and state

institutions, it can serve as a tool for specification of

data literacy competencies linked to different job

positions and of how to gain the required knowledge

and skills and eventually for the development of

analytical culture. In schools, it should be used to

define appropriate capabilities of data literacy for

different grades, to continuously and critically

measure its students’ progress in data literacy along

their studies and most importantly to prepare

adequate educational programs to acquire these

capabilities.

While the Data Literacy Indicator serves as a

critical assessment where we are and where we need

to go, the methodology for teaching data literacy

appropriately translated into tailor-made educational

programs brings solution to the knowledge and skills

gap in data literacy. Based on the measured level of

indicator it will offer a path of concrete steps to follow

in order to reach the targeted level of data literacy. As

the measurement of data literacy should take into

account differences of subjects’ domains or students’

highest level of education acquired, the methodology

aims to be tested and tailored to these specifics as

well. However, the main objective and a stepping

stone is to create a methodology for teaching data

literacy at schools (from middle schools to

universities).

3.2 Selected Research Methods

The proposed research is clearly design-oriented and

intends to contribute to the academic world as well as

to the public with two artefacts – the Data Literacy

Indicator (CMM) and methodology for teaching data

literacy in relation to the measured level of the

indicator.

The development of the Data Literacy Indicator is

based on the People Capability Maturity Model

(PCMM) as maturity assessment models are used as

“an instrument for systematically documenting and

guiding the development and transformation of

organizations” (Paul et al. 1993). Its offshoot, the

People Capability Maturity Model, then focuses on

the development of people competences and the

measurement of the competences maturity. We

generally understand maturity as a level of

sophistication, here it clearly serves as a measure for

capability evaluation (De Bruin et al. 2005). The

capabilities are characterized by specific areas, so

called dimensions, which encompass “different

aspects of the maturity assessment’s object“ and are

“further specified by a number of characteristics

(practices, measures or activities) at each level”

(Raber, Winter, Wortmann 2012).

I plan to approach the maturity assessment with

quantitative methods as used by Lahrmann et al.

(2011) or Raber, Winter, Wortmann (2012). Their

quantitative analysis is based on Item Response

Theory (IRT) which is “a collection of mathematical

models and statistical methods used for two primary

purposes: item analysis and test scoring“ and is “used

with data arising from educational tests of ability,

proficiency or achievement“ (Millsap, Maydeu-

Olivares 2009). The theory builds on the hypothesis

that the probability of correct answer to an item (the

question) is a mathematical function of the

respondent and the characteristics of the item.

Next Step: Data Literacy Measurement

237

The IRT will be applied to a test/questionnaire to

measure data literacy. The test questions aim to assess

all dimensions of data literacy which represent areas

of knowledge concepts and skills required to be able

to “read and speak data”. By expanding Ridsdale’s et

al.’s (2015) and Grillenberger and Romeike’s (2018)

models of data literacy competencies, I derived five

dimensions of data literacy: (1) Data concepts, ethics

and protection; (2) Analytical principles and models;

(3) Data collection and preparation; (4) Data analysis

and evaluation; (5) Data communication and

decision-making. Every dimension comprises of a

specific set of competencies which will be measured

by the test (e.g. an ability to assess relevant data

sources or an ability to access data are competencies

covered in Data collection and preparation

dimension). By clustering method the respondents’

test results which indicate respondents’ level of

sophistication in different areas of data literacy will

be used to establish the maturity levels.

Regarding the second artefact, the methodology

for teaching data literacy, I would like to base its

design on the use of various case studies. More

precisely I would like to follow the approach in

Wolff, Wermelinger and Petre (2019) which for the

design of data literacy activities applied method

called research through design in which “design

practice is applied to the creation of artefacts as a

way of exploring solutions to problems”. The method

comes from interaction design research in human-

computer interaction and as stated in Wolff,

Wermelinger and Petre (2019), “new knowledge is

constructed by undertaking activities associated with

design, such as iteratively creating and testing

prototypes to understand and solve a problem and to

act as a focal point for discussion by making

interactions observable“.

At the moment I am at the beginning of the first

case study preparation. Its main objective is to verify

the pilot version of a questionnaire measuring the data

literacy maturity of the freshmen students at

university. The test would be included in the first

lesson of a new subject introducing application of

data analysis by means of an interactive online game

which promises high number of respondents with

several classes each semester and allows to measure

the entry level of the high school graduates (without

the interference of higher education).

4 CONCLUSIONS

With the accessibility of vast volumes of data

everywhere, data literacy is a must in order to fully

participate in a modern society. Nevertheless, if we

want to be effective in enhancement of our

knowledge and skills in the domain, we have to be

able to mark our start line and to specify what level of

data literacy we want to achieve. Thus appropriate

measurement of data literacy is required and should

swiftly complement the recent boom in data literacy

teaching initiatives and research.

REFERENCES

Association of College & Research Libraries (ACRL).

(2006). Presidential Committee on Information

Literacy: Final Report. Association of College &

Research Libraries. http://www.ala.org/acrl/

publications/whitepapers/presidential

Burns, L. S., & Matthews, B. J. (2018). First Things First:

Teaching Data Journalism as a Core Skill: Asia Pacific

Media Educator. https://doi.org/10.1177/1326365X1

8765530

Cheuk, B. (2008). Delivering Business Value through

Information Literacy in the Workplace. Libri, 58(3),

137–143. https://doi.org/10.1515/libr.2008.015

De Bruin, T., Rosemann, M., Freeze, R., & Kaulkarni, U.

(2005). Understanding the Main Phases of Developing

a Maturity Assessment Model. In D. Bunker, B.

Campbell, & J. Underwood (Eds.), Australasian

Conference on Information Systems (ACIS) (pp. 8–19).

Australasian Chapter of the Association for Information

Systems. https://eprints.qut.edu.au/25152/

D’Ignazio, C., & Bhargava, R. (2016). DataBasic: Design

Principles, Tools and Activities for Data Literacy

Learners. The Journal of Community Informatics,

12(3), 83–107. http://ci-journal.net/index.php/ciej/

article/view/1294

De Saulles, M., 2007. Information literacy amongst UK

SMEs: an information policy gap. Aslib Proceedings

59, 68–79. https://doi.org/10.1108/00012530710725

214

Frank, M., Walker, J., Attard, J., & Tygel, A. F. (2016).

Data Literacy—What is it and how can we make it

happen? The Journal of Community Informatics, 12(3).

http://ci-journal.net/index.php/ciej/article/view/1347

Gäbler, J., Winkler, C., Lengyel, N., Aigner, W., Stoiber,

C., Wallner, G., & Kriglstein, S. (2019). Diagram

safari: A visualization literacy game for young

children. 389–396. Scopus. https://doi.org/10.1145/

3341215.3356283

Gal, I. (2002). Adults’ Statistical Literacy: Meanings,

Components, Responsibilities. International Statistical

Review, 70(1), 1–25. https://doi.org/10.1111/j.1751-

5823.2002.tb00336.x

Gartner. (2018a). Fostering Data Literacy and Information

as a Second Language: A Gartner Trend Insight

Report. https://www.gartner.com/en/doc/3860965-

fostering-data-literacy-and-information-as-a-second-

language-a-gartner-trend-insight-report

KMIS 2020 - 12th International Conference on Knowledge Management and Information Systems

238

Gartner. (2018b). Getting Started With Data Literacy

and Information as a Second Language: A Gartner

Trend Insight Report. https://www.gartner.com/en/

documents/3892877/getting-started-with-data-literacy-

and-information-as-a-

Gould, R. (2017). Data literacy is statistical literacy.

Statistics Education Research Journal, 16(1), 22–25.

Scopus.

Gray, J., Gerlitz, C., & Bounegru, L. (2018). Data

infrastructure literacy. Big Data and Society, 5(2).

Scopus. https://doi.org/10.1177/2053951718786316

Grillenberger, A., & Romeike, R. (2018). Developing a

theoretically founded data literacy competency model.

ACM International Conference Proceeding Series.

Scopus. https://doi.org/10.1145/3265757.3265766

Halliday, S. D. (2019). Data literacy in economic

development. Journal of Economic Education, 50(3),

284–298. Scopus. https://doi.org/10.1080/00220485.

2019.1618762

Koltay, T. (2015). Data literacy: In search of a name and

identity. Journal of Documentation, 71(2), 401–415.

https://doi.org/10.1108/JD-02-2014-0026

Lahrmann, G., Marx, F., Mettler, T., Winter, R., &

Wortmann, F. (2011). Inductive Design of Maturity

Models: Applying the Rasch Algorithm for Design

Science Research. In H. Jain, A. P. Sinha, & P.

Vitharana (Eds.), Service-Oriented Perspectives in

Design Science Research (pp. 176–191). Springer.

https://doi.org/10.1007/978-3-642-20633-7_13

Lusiyana, A., Festiyed, & Yulkifli. (2020). Measuring the

physics students’ data literacy skill in the era of industry

4.0 by using mirecal learning model. International

Journal of Scientific and Technology Research, 9(1),

1203–1205. Scopus.

Marcum, J.W., 2002. Rethinking Information Literacy. The

Library Quarterly 72, 1–26. https://doi.org/10.1086/

603335

Maybee, C., & Zilinski, L. (2015). Data informed learning:

A next phase data literacy framework for higher

education. Proceedings of the Association for

Information Science and Technology, 52(1), 1–4.

Scopus. https://doi.org/10.1002/pra2.2015.1450520100

108

Millsap, R., & Maydeu-Olivares, A. (2009). The SAGE

Handbook of Quantitative Methods in Psychology.

SAGE Publications Ltd. https://doi.org/10.4135/

9780857020994

Nolan, D., & Perrett, J. (2016). Teaching and Learning Data

Visualization: Ideas and Assignments. The American

Statistician, 70(3), 260–269. https://doi.org/10.1080/

00031305.2015.1123651

Oceans of Data Institute. (2015). Building Global Interest

in Data Literacy: A Dialogue. Education Development

Center. http://oceansofdata.org/sites/oceansofdata.org/

files/ODI%20Data%20Literacy%20Report_0.pdf

O’Farrill, R.T., 2008. Information Literacy and Knowledge

Management: Preparations for an Arranged Marriage.

Libri 58, 155–171. https://doi.org/10.1515/libr.2008.

017

Oman, J.N., 2001. Information Literacy in the Workplace.

Information Outlook 5, 32.

QlikTech. (2018). The Data Literacy Index: Results

Summary. https://thedataliteracyproject.org/files/

documents/Qlik%20-%20The_Data_Literacy_Index_

October_2018.pdf

Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V.

(1993). Capability maturity model, version 1.1. IEEE

Software, 10(4), 18–27. https://doi.org/10.1109/

52.219617

Prado, J. C., & Marzal, M. Á. (2013). Incorporating Data

Literacy into Information Literacy Programs: Core

Competencies and Contents. International Journal of

Libraries and Information Studies, 63(2), 123–134.

https://doi.org/10.1515/libri-2013-0010

Pangrazio, L., & Sefton-Green, J. (2019). The social utility

of ‘data literacy’. Learning, Media and Technology.

Scopus. https://doi.org/10.1080/17439884.2020.1707

223

Pedersen, A. Y., & Caviglia, F. (2019). Data literacy as a

compound competence. Advances in Intelligent

Systems and Computing, 850, 166–173. Scopus.

https://doi.org/10.1007/978-3-030-02351-5_21

Pratama, M. A., Supahar, Lestari, D. P., Sari, W. K.,

Putri, T. S. Y., & Adiatmah, V. A. K. (2020). Data

literacy assessment instrument for preparing 21 Cs

literacy: Preliminary study. 1440. Scopus.

https://doi.org/10.1088/1742-6596/1440/1/012085

Raber, D., Winter, R., & Wortmann, F. (2012). Using

quantitative analyses to construct a capability maturity

model for Business Intelligence. 4219–4228. Scopus.

https://doi.org/10.1109/HICSS.2012.630

Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H.,

Bliemel, M., Irvine, D., Kelley, D., Matwin, S., &

Wuetherick, B. (2015). Strategies and Best Practices

for Data Literacy Education: Knowledge Synthesis

Report. Dalhouse University. https://dalspace.

library.dal.ca/handle/10222/64578

Rowley, J. (2007). The wisdom hierarchy: Representations

of the DIKW hierarchy. Journal of Information

Science. https://doi.org/10.1177/0165551506070706

Saito, A., 2007. Educating Knowledge Managers: A

Competence-Based Approach.

Thompson, S., 2003. Information Literacy Meeting of

Experts: Prague, the Czech Republic, September 20-23,

2003. United States Commission on Libraries and

Information Science.

Wallner, G., & Kriglstein, S. (2011). Design and evaluation

of the educational game DOGeometry—A case study.

ACM International Conference Proceeding Series.

Scopus. https://doi.org/10.1145/2071423.2071441

van der Wal, N. J., Bakker, A., & Drijvers, P. (2017). Which

Techno-mathematical Literacies Are Essential for

Future Engineers? International Journal of Science and

Mathematics Education, 15, 87–104. Scopus.

https://doi.org/10.1007/s10763-017-9810-x

van Rooi, H., Snyman, R., 2006. A content analysis of

literature regarding knowledge management

opportunities for librarians. Aslib Proceedings 58, 261–

271. https://doi.org/10.1108/00012530610677255

Next Step: Data Literacy Measurement

239

Virkus, S., 2016. Knowledge Management and Information

Literacy: An Exploratory Analysis, in: Information

Literacy: Key to an Inclusive Society, Communications

in Computer and Information Science. Springer

International Publishing, Cham, pp. 119–129.

https://doi.org/10.1007/978-3-319-52162-6_12

Wang, B., Wu, C., & Huang, L. (2019). Data literacy for

safety professionals in safety management: A

theoretical perspective on basic questions and answers.

Safety Science, 117, 15–22. Scopus.

https://doi.org/10.1016/j.ssci.2019.04.002

Wendler, R. (2012). The maturity of maturity model

research: A systematic mapping study. Information and

Software Technology, 54(12), 1317–1339.

https://doi.org/10.1016/j.infsof.2012.07.007

Whithworth, A., 2014. Radical Information Literacy:

Reclaiming the Political Heart of the IL Movement,

Chandos Information Professional Series. Chandos

Publishing.

Wolff, A., Gooch, D., Cavero Montaner, J. J., Rashid, U.,

& Kortuem, G. (2016). Creating an Understanding of

Data Literacy for a Data-driven Society. The Journal of

Community Informatics, 12(3). http://ci-journal.net/

index.php/ciej/article/view/1286

Wolff, A., Wermelinger, M., & Petre, M. (2019). Exploring

design principles for data literacy activities to support

children’s inquiries from complex data. International

Journal of Human Computer Studies, 129, 41–54.

Scopus. https://doi.org/10.1016/j.ijhcs.2019.03.006

KMIS 2020 - 12th International Conference on Knowledge Management and Information Systems

240