Review of eLearning Knowledge Quality

Dimensions: Concepts and

Measurements

Researcher: Mehwish Waheed

PhD Candidate

University of Malaya, Kuala Lumpur

mahwish.phd@gmail.com

Introduction

“Where is the knowledge we have lost in information?” Eliot (1963)

The computing ‘garbage in, garbage out’ mantra succinctly

express this problem. Which leads to the difficulty of identifying the

‘quality’ information that helps in increasing the user knowledge,

from a bulk of information (Stvilia et al., 2008).

In educational perspective, quality is a critical issue in general, and

more sensitive for Open Educational Resources (OER) (Alkhattabi et

al., 2011) due to the demand of high quality learning content and it needs to be measure through multidimensional criteria (Aladwania

et al., 2002; Alkhattabi et al., 2010).

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Cont…

College and universities are rapidly adopting eLearning platforms

and OER resources for their course delivery and training (Lim et al.,

2006), (e.g. (WOU), (SPeCTRUM), (OLIVE), MIT Open Courseware.

OER resources are facilitated by the use of new multimedia

technologies and the Internet to improve the quality of learning

(Commission of the European Communities, 2001).

This study is concerned about the OER content quality, in terms of knowledge gained from the content available on OER.

users’ concern is about the quality knowledge gain from the

available content in OER 7/14/2014

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Problem Statement

Information quality of online content is discussed (Alkhattabi et al., 2011; Masoumi et al., 2012). However, area of ‘Quality’ research,

has been hampered by the lack of consensus on quality of

knowledge in OER.

knowledge quality is a vaguely defined concept. In literature,

researchers (Chiu et al, 2006; Halawi et al., 2008; Jennex et al., 2006;

Liu et al., 2010) used the Data quality (DQ) information quality

(IQ)dimensions to measure the knowledge quality (KQ), which is unjustifiable. Measurement of KQ is the key identified issue of this

study.

Lack of research discuss the KQ considering theory of knowledge

(Artemov et al, 2005; Lehrer, 1990; Lehrer et al., 1969) and

knowledge hierarchy (Braganza, 2004; Brodie et al., 2009; Rowley,

2007)

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Purpose of the Study

This study aims to accomplish the following objectives

Examine the essence of knowledge.

Identify the elusive use of IQ dimensions to measure KQ.

Investigate conceptual and operational measurement of Data

Quality, Information Quality and Knowledge Quality through review

of literature.

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Significance of Study

This review helps in exploring required dimensions for measuring OER knowledge quality which is the key contribution to the body of OER

literature.

Additionally, the dimensions can be utilized for measuring knowledge quality instrument.

The instrument would be useful to measure the OER knowledge

quality in terms of the knowledge gained by the user from OER

content.

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Literature Review

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Theory of Knowledge and Knowledge

Hierarchy

The tripartite definition discusses the theory of knowledge,

which argues that knowledge must encompass ‘justified,

true, belief’; the three indispensable conditions to fulfil the

essence of knowledge (Lehrer et al., 1969; Plato,

1921,1967).

Knowledge hierarchy functionally relates the data,

information and knowledge. These terms hold separate

dimensions at each stage that collectively lead to

knowledge starting from the data (Braganza, 2004).

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Elusive use of “knowledge quality”

Table 1. Elusive use of the information quality items for knowledge quality in different domains

Item Extraction

From IQ

Domain

Adapted By KQ Domain of

Measure

Sample Item Used Methodology

Bailey and

Pearson (1983)

Kulkarni et

al. (2007)

Validating the KM

Success Model

150 midlevel managers

enrolled in executive &

part-time MBA

Presentation format and Usefulness of the

content

EFA

Halawi et al.

(2007)

Investigating the

KMS Success

99 members from

Companies

Convenience of Access, Accuracy, Timeliness,

Precision, Reliability, Currency, Completeness,

Language, Volume of Output, Relevancy, and

Error Recovery

EFA

McKinney et al.

(2002)

Chiu et al.

(2006)

knowledge sharing

in virtual

communities

310 member from

virtual community

Relevance, Ease of Understanding, Accuracy,

Completeness, Reliability, and Timeliness

CFA

Liu et al.

(2010)

knowledge sharing

in Libraries

204 professional

librarians

Relevance, Ease of Understanding, Accuracy,

Completeness, Reliability, and Timeliness

SEM

Delone (2003) Jennex and

Olfman

(2006)

Knowledge

Management

Success Model

Content Analysis knowledge strategy/process, richness, and

linkages between knowledge components

Updated, relevance, accuracy, completeness,

reliability, and timeliness

Theoretical

Paper

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Comparison of DQ, IQ, and KQ Dimensions

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Knowledge Quality Pyramid

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Critique

Previous researchers have used the modified version of either Wang

and Strong’s (1996) DQ dimensions or Delone’s (2003) IQ dimensions

to measure KQ (Chiu et al., 2006; Halawi et al., 2007; Jennex and

Olfman, 2006; Liu et al., 2010; Rao and Osei-Bryson, 2007).

DQ dimensions that are Access Security, Accessibility, Accuracy, Appropriate Amount of Data, Believability, Completeness,

Conciseness, Consistency, Current, Interpretability, Level of Detail,

Objectivity, Relevancy, Reliability, Representation Consistency, Reputation, Timeliness, Understandability, Usefulness, and Value

Added, are adapted for measuring IQ by introducing two further dimensions, i.e. Updated and Verifiability.

However, KQ construct has faced serious negligence; none of the

researchers proposed any new dimensions

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Conceptualization and Operational

Measurement of KQ Hierarchy

Quality knowledge encompass truthiness, justification, newness and

actionable nature (adaptable, applicable, and expandable)

Collectively three attributes of tripartite definition (JTB) meet the

essence and quality of knowledge.

Knowledge that is new, innovative, and useful for the

organisation/institution/system fulfils the requirements of quality

knowledge (Chan et al., 2008).

Knowledge is about action and must be used to some end’

(Nonaka et al., 1995; Yoo et al., 2011: 331). 7/14/2014

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Methodology

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Research Design

The exploratory research design is opted.

Exploratory sequential Mixed-method design will be used (Creswell

et al., 2010). Also referred to as instrument development design or

the quantitative follow-up design (Creswell et al., 2003; Morgan,

1998).

Qualitative to Quantitative sequence

Population

Students who use OER recourses. Specifically, undergraduate and

postgraduate students of.

Sampling

For Qualitative Data: Purposive sampling. Participants should be

information rich as suggested by Creswell et al. (2007). Small sample

For Quantitative Data: Proportional stratified sampling. “to produce

a sample that is as representative of the population as possible

Mark (1996).

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Thank You..

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