2nd Regional Symposium on Open Educational Resources: Beyond Advocacy, Research and Policy 24 – 27 June 2014 Sub-theme 5: Quality Concepts and Measurements Mehwish Waheed, Kiran Kaur

1. Review of eLearning Knowledge Quality
Dimensions: Concepts and
Measurements
Researcher: Mehwish Waheed
PhD Candidate
University of Malaya, Kuala Lumpur
mahwish.phd@gmail.com

2. 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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3. 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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4. 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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5. 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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6. 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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7. Literature Review
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8. 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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9. 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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10. Comparison of DQ, IQ, and KQ Dimensions
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11. Knowledge Quality Pyramid
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12. 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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13. 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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14. Methodology
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15. 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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16. Thank You..
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