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E-learning quality assurance:
a process-oriented lifecycle model
M’hammed Abdous
Center for Learning Technologies, Old Dominion University,
Norfolk, Virginia, USA
Abstract
Purpose – The purpose of this paper is to propose a process-oriented lifecycle model for ensuring
quality in e-learning development and delivery. As a dynamic and iterative process, quality assurance
(QA) is intertwined with the e-learning development process.
Design/methodology/approach – After reviewing the existing literature, particularly focusing on
QA frameworks, procedures, and methodology, a process-oriented model structured around three
sequential non-linear phases is presented: before: planning and analysis; during: design, prototype and
production; and after: post-production and delivery. This model is supported by an advancedinformation
system used to organize, track, collect, and generate reports regarding QA changes and needed updates.
Findings – Following a process-oriented lifecycle approach, the paper emphasises that QA requires a
supportive environment that explicitly recognizes quality as a work value and as an enabler for
reaching organizational goals.
Practical implications – The paper proposes a practical QA model which follows e-learning
development phases. For each development phase, practical steps, including sample checklists, are
recommended.
Originality/value – The proposed model has the potential to transform QA from a static,
after-the-fact state to a more iterative and dynamic state, thus promoting a culture of ongoing
self-improvement, rather than of circumstantial compliance, within the e-learning community.
Keywords Quality, Quality assurance, E-learning, Higher education
Paper type Conceptual paper
Introduction
In the midst of the technological paradigm shift reshaping institutions of higher education
(HE), the question of quality assurance (QA) is at the forefront of university leadership
concerns worldwide (Newton, 2007; van Damme, 2002). The resurgence of this old/new
quality debate is driven by the confluence of contextual factors, such as the competitive
global economy, external pressures for greater accountability and responsiveness,
financial constraints and massification effects. Indeed, these factors are pressuring HE
institutions to implement QA procedures in order to improve teaching, learning, research,
and services, particularly for e-learning[1] which sometimes still suffers from the stigmas
(occasionally founded) of poor quality and low standards (Chua and Lam, 2007).
This increased attention has resulted in a proliferation of guidelines,
recommendations, procedures and checklists aiming at compliance, accountability,
and improvement (Hodson and Thomas, 1999), but also at regaining stakeholders’
confidence by reassuring them that e-learning courses are equally as rigorous as
courses delivered in a traditional face-to-face format (Hope, 2001).
While most universities have implemented some form of internal self-regulated
QA procedures, it is hard to find a comprehensive and practical QA framework that
systematically covers HE inputs, processes and outputs (Inglis, 2005). These efforts are
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0968-4883.htm
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Quality Assurance in Education
Vol. 17 No. 3, 2009
pp. 281-295
qEmerald Group Publishing Limited
0968-4883
DOI 10.1108/09684880910970678
occasionally initiated in response to external requirements and are often limited to the
administrative-side operations of HE institutions (Aly and Akpovi, 2001).
Self-implemented QA procedures are sometimes unintegrated and can be too
narrowly focused on student learning outcomes (Welsh and Dey, 2002), consequently
omitting critical input variables and the processes leading to those outcomes. As for
external evaluations, in addition to their being poorly integrated with existing
institutional strategic planning processes (van der Westhuizen, 2007), their focus is on
compliance and on accountability, resulting in a limited effect on the student learning
experience (Harvey and Newton, 2004).
While recognizing that the holistic nature of HE entails a systemic QA approach
covering inputs, processes and outputs[2] this paper’s focus is on the foundational
process of e-learning development and delivery. A process-oriented lifecycle QA model,
one in which QA is dynamically and iteratively intertwined with the e-learning
development process, is proposed. This approach recognizes, along with Oliver and
Herrington (2003) that the most influential factors affecting the quality of the e-learning
experience are the purposeful and informed design of appropriate learning tasks and
the inclusion of resources to scaffold and support interaction and learning. From this
perspective, this paper joins Ellis et al. (2007) in their choice of a process-oriented
approach in the institutional implementation of QA in e-learning.
By acting on the design of the production and the delivery of e-learning courses,
QA is transformed from a static, after-the-fact state to a more iterative and dynamic
state, one which promotes a culture of ongoing self-improvement instead of one of
circumstantial compliance. In this regard, the proposed model targets the practices of
those “front-line academics” (Newton, 2002) who are involved in shaping the e-learning
experience: professors, instructional designers and instructional technologists.
Ultimately, it is hoped that this model will both contribute to and enrich the debate
about e-learning quality by providing a practical QA model potentially capable of
addressing some of the scepticism surrounding e-learning.
As it attempts to articulate these goals, this paper begins by clarifying the concept
and context of quality and QA. Next, it examines the literature pertaining to QA
frameworks, procedures and methodology. Based on this clarification and review,
a process-oriented lifecycle QA model structured around three sequential non-linear
phases is presented, which includes:
(1) before: planning and analysis;
(2) during: design, prototype and production; and
(3) after: post-production and delivery.
The main goal of this paper is to propose a systematic and practical model capable of
enabling institutions of HE to integrate QA into e-learning development and delivery
and to share lessons learned from a trial implementation of this model.
What is quality?
The definitional challenge associated with the concept of quality, along with its
derivatives (including QA, quality control, quality audit, total quality management, and
quality enhancement), stems from the juxtaposition of internal and external university
stakeholders’ interests, expectations, and requirements. These requirements and
expectations, occasionally contradictory, are complex and contribute to the conceptual
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and operational imprecision which surrounds any attempt to define quality. In that vein,
Harvey and Green (1993) view quality as stakeholder-relative; hence, it is not a unitary
concept, but a rather elusive (Green, 1994), slippery and multi-dimensional (Giertz, 2001)
concept. In this regard, van Damme (2002) points out that, despite 20 years of operational
experience in assuring quality in HE, no consensual definition of this rather vague and
controversial concept is universally accepted (Cheng and Tam, 1997). Similarly, Scott
(1994) asserts that there is no authoritative definition of quality in HE (Newton, 2007).
Quality is a rather notoriously ambiguous term (Pounder, 1999). However, one of the
earlier categorizations of quality proposed by Harvey and Green (1993) seems to
generate some consensus that quality can be perceived as added value, fitness for
purpose, customer satisfaction, or positive transformation.
What is quality assurance?
As a subset of the semantic cluster of the word “quality,” “quality assurance” seems to
be less ambiguous. As an approach focusing on processes, QA is defined by the
International Organization for Standardisation (2005) as “that part of quality
management focused on providing the confidence that quality requirements are being
fulfilled.” Harman and Meek (2000, p. vi) define QA as the “systematic management
and assessment procedures used to ensure achievement of quality outputs or improved
quality.” Close to this meaning is the proposed definition from the Council for Higher
Education Accreditation (2002a) of QA as a “planned and systematic review process of
an institution or program to determine that acceptable standards of education,
scholarship and infrastructure are being maintained and enhanced.”
From this quick review of quality and QA definitions, the lack of clarity
surrounding the concept of quality is mainly nurtured by its stakeholders’ perspective.
Paradoxically, this lack of clarity carries a double-edged potential: on one hand, it is
conducive to meeting the needs and interests of various internal and external
stakeholders; on the other, it renders the concept of quality difficult to operationalize,
because of its vagueness and imprecision. In contrast, while quality is subjected to
multiple narratives and perspectives, QA seems to be both achievable and
implementable as a methodology used to judge the achievement of organizational
aims and objectives (Doherty, 2008).
Keeping this in mind, Newton (2007) advocates for a practical and “relativist”
approach and acknowledges the relative nature of quality to stakeholders, to context
and to the particular assurance mechanisms associated with quality, such as
assessment, audit, and accreditation. Contrary to the traditional perspective which
perceives QA as a mechanism used to avoid and anticipate faults or mistakes, the use
of this pragmatic stakeholders’ approach is more likely to foster a culture of QA and
ongoing improvement, particularly within the dynamic, diverse, and unpredictable
context of HE. This leads to the next question: how to understand the contextual
factors which affect QA?
Quality assurance in context
Although this paper argues for a QA model structured around e-learning development
and delivery phases, it is hard to isolate this process from the contextual factors
influencing it. Understanding the context is of paramount significance, not only
because it frames the QA process, but also because it dictates the standards used
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during that process, particularly when translating the existing standards into
operational checklists. According to Newton (1999, cited in Newton, 2007, p. 19), “any
quality assurance model, method or system will always be affected by situational
factors and context.” To understand the dynamic of the contextual factors affecting
QA, a metaphor using threefold-layered circles is proposed (Figure 1).
From this figure, it is clear that, at the centric level, QA is a core value. It is
perceived as a dynamic, iterative and ongoing process. Rather than serving as an
after-the-fact approach, it can be embedded into the daily practices of the front-line
academics shaping the e-learning experience.
At the concentric circle level, variables such as technology, accreditation,
accountability, financial pressures, and student mobility shape the QA process.
In particular, hardware and software innovations are constantly pressuring existing
teaching and learning strategies. The pressures exerted by the burgeoning of technology
permeate the culture of HE, with consequential implications on the QA narrative,
particularly in determining standards and guidelines for communication and collaboration
on one side and for accessibility, usability and interface design on the other side.
At the outer circle level, market forces, employers, transnational education and
diploma mills are among the most common issues shaping QA. A new generation of HE
providers encompassing for-profit institutions, textbook publishing corporations and
transnational providers is pressuring traditional universities to distinguish themselves
by offering quality programs. As a marketing and recruiting tool, QA is becoming a seal
of distinction in the midst of the competitive and growing e-learning market.
Following this line of thinking, the QA context is a rather a complex web ofinteractions
and dynamics amid several interconnected variables. The inability to acknowledge and
Figure 1.
Situational factors
affecting QA
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comprehend these dynamics and the forces shaping the debate and narrative about what
constitutes quality is likely to hinder the implantation of QA into e-learning.
From accountability to improvement
Before undertaking the complex task of reviewing some of the abundant literature on
QA, it is noteworthy to point out the evolution of the concept of QA from accountability
to improvement. In his ethnographic study, Newton (2002, p. 8) distinguishes two
successive phases of quality implementation. The first phase, during the early 1990s,
was burdened with bureaucracy and accountability and was particularly interested in
looking for a replicable blueprint. During this phase, the concept of quality was
(and still is, to some extent) loaded, both politically and ideologically. In the second
phase, which occurred in the mid-1990s, the awareness of quality was structured
around what Newton calls “an alternative understanding and perspective on ‘quality’
and ‘quality policy’ as implemented, based on the situated perceptions of quality of
front-line academics.” This evolution implicitly expresses the irreconcilable tension
between accountability and improvement (Newton, 2002), if not the inability of
accountability alone to provide a sufficient basis for delivering quality improvement.
In addition to accountability and improvement, quality is often used as a marketing
and recruiting tool (Boyle and Bowden, 1997). Brennan and Shah (2000) note that
quality is used to assign institutional status, to facilitate student transfer and mobility,
and to enable international comparisons.
From a procedural stance, Jeliazkova and Westerheijden (2001) point out that, with
minor variation, external QA systems follow a four-stage model which starts with a
request from an external coordinating agency and is followed by the submission of a
self-evaluation report, a peer visit and a public report.
From a methodological standpoint, Reichert (2007) proposes a matrix with three
columns (accreditation, evaluation and benchmarking) structured around
accountability, control, and improvement. At the institutional level, accreditation is
widely used as a seal of approval and distinction, particularly in light of the growing
presence of for-profit institutions in the HE sector. Similarly, evaluation is intended to
help both with making strategic decisions and with steering resources under the
pressures of budget constraints. Finally, benchmarking is used as a self-evaluation
process (Jackson, 2001), and is used to increase awareness of competition, while
allowing for the sharing and exchanging of experiences.
From an operational perspective, Bogue (1998) discerns the four contemporary QA
categories listed as follows:
(1) Traditional peer review evaluations are structured around three subcategories:
accreditation, program review and ranking, and rating. Accreditation is the oldest
non-governmental seal of quality in HE (Baker, 2002). According to Dodd (2004),
accreditation is the most prominent of all accountability efforts, fostering
improvement as well as accountability, and enabling participating institutions to
receive governmental financial aid. For their part, Stensakera and Harvey (2006)
note that accreditation, while criticized for overlooking the challenge of quality
improvement, still provides internal and external legitimacy to institutions of HE.
Academic program review, the second sub-category, features self-study,
and external peer review at the discipline, department, or program level. According
to Bogue (1998), academics perceive this approach as futile, as it offers little
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relationship to resource allocation or to decision making. Finally, the third
category, ranking and rating studies, encompasses studies which, although widely
publicized in the news, have been underutilized by students and accrediting
agencies.
(2) The assessment-and-outcomes movement focuses mainly on results, rather
than on reputation. This movement’s underlying policies mainly focus on
increasing access to HE and enhancing public accountability for quality.
(3) Total quality management is an approach borrowed from business practices which
focuses oncontinuous improvement and student satisfaction. According to Becket
and Brookes (2005), total quality management has the potential to capture both the
internal and the external stakeholders’ perspectives, enabling a comprehensive
approach capable of assuring quality while facilitating change and innovation.
However, because of the perceived disconnect between total quality management
techniques and the education process (Srikanthan and Dalrymple, 2003), this
approach is considered more applicable to administrative and student services.
(4) Accountability and performance indicator reporting is requested, and is used,
as a quality indicator. In this regard, enrolment trends, retention and graduation
rates, job placement rates, and student and alumni satisfaction are used to
measure HE’s effectiveness and impact. According to Baker (2002), judgment of
quality in HE has shifted from a traditional and implicit perception based
on institutional reputation and characteristics to an explicit perception based on
evidence of outcomes and achievements.
These categories and sub-categories depict common practices in traditional HE
settings. In the early days of distance learning, QA frameworks, and methodologies
were inspired by traditional on-campus education, with accreditation agencies using
almost the same criteria for both distance education and on-campus education.
This approach was justified by the perception that distance learning was merely an
alternate delivery system (Council for Higher Education Accreditation, 2002b).
However, pressures exerted by the contextual factors mentioned above (accreditation,
technology, and competitiveness) have forced accrediting agencies and HE institutions
to proactively implement rigorous and transparent QA procedures and guidelines.
Indeed, several agencies, such as the Quality Assurance Agency for Higher
Education (1999), the Institute for Higher Education Policy (1999), the Council for Higher
Education Accreditation (2002b), the Western Cooperative for Educational
Telecommunications (2002), the European Association for Quality Assurance in
Higher Education (2005), the Commonwealth of Learning (2005), and the United Nations
Educational, Scientific and Cultural Organization (2005) have played a key role in
shaping distance learning QA approaches. With a high level of similarities and
comparability, these agencies have promoted the implementation of QA frameworks
nationally and internationally, focusing mainly on improving the student learning
experience (Belawati and Zuhairi, 2007). In her survey of QA practices in distance
learning mega-universities, Jung (2004) highlights the emergence of a culture of quality,
strengthening capacity-building to promote and implement QA systems while focusing
on learning rather than teaching (Koul and Kanwar, 2006). In addition, Jung (2004) points
out the variation observed in QA systems integration within existing university policy
frameworks which is particularly reflected in the standards and criteria used at
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different universities. At institutions that place utmost importance on accountability,
predetermined standards and criteria are generally followed. At other institutions
striving for self-improvement, less prescriptive general guidelines are often followed.
According to a report published by the Council for Higher Education Accreditation
(1998), QA is defined in e-learning as “the means by which the institutions or providers set
their program goals and measure resultsagainst those goals.” Indeed, as stated above, the
resurgence of the quality debate, particularly inside e-learning, has generated a
considerable body of knowledge reflecting various stakeholders’ perspectives regarding
their understanding of qualityand QA (Rekkedal, 2006). Following Hirumi’s (2005) matrix,
which compares educational quality guidelines, as well as Wang’s (2006) review of
existing QA best practices, five common categories are noted: institutional commitment,
curriculum and instructional development, academic staff support, student support, and
learning outcomes assessment. Overall, similarities tend to cover inputs and outputs,
while dissimilarities tend to be process-related. According to Parker (2003), quality
measures still rely on inputs such as the qualificationof instructors and on outputs such as
satisfaction ratings, and therefore still lack the ability to capture the “fundamental
integrity of the online learning environment.” With this in mind, a process-oriented
QA model based on development and delivery phases is proposed, following upon a belief
that embedding QA within e-learning development is likely to improve the student
e-learning experience, assuming that enabling conditions are provided.
Process-oriented lifecycle model for quality assurance in e-learning
Drawing upon the materials described above, a process-oriented model aimed at
helping organizations to implement a QA process structured around the foundational
process of e-learning development and delivery is proposed. In this regard, it is
noteworthy to point out that the design, production, and delivery of e-learning require
both a streamlined workflow and the collaboration of several specialists
(subject-matter, instructional, and technical) working together in a team environment
(Phillips, 2005). With this consideration in mind, the proposed QA model mirrors a
centralized institutional framework for planning, designing, producing, and delivering
e-learning courses. In this centralized model, content, design and technology are
synergistically integrated, using a series of templates and checklists built on key ideas
from research and practice (Abdous and He, 2008). The model (Figure 2) is structured
around three sequential non-linear phases:
(1) before: planning and analysis;
(2) during: design, prototype and production; and
(3) after: post-production and delivery.
In the planning and analysis phase, a project plan and a workflow diagram are used as
QA tools to flowchart the development process and to clarify the timeline,
assumptions, and expectations. This phase is critical in setting the stage for the
proposed QA model, particularly in refining and updating course development
templates and checklists. Indeed, this phase defines and sets the quality standards
underpinning content collection templates and production checklists. In other words,
it defines the constructs underlying the proposed QA model.
Subsequently, during the design/production phase, pre-designed content collection
templates are used to ensure the appropriateness, comprehensiveness and consistency of
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the content. These templates include the critical elements of effective e-learning
environments, such as the development of student-centred syllabi, the alignment of
objectives with the content matrix, the use of engaging and diversified learning activities,
the offering of opportunities for interaction and collaboration and, of course, the
opportunity for meaningful assessment and feedback (Hosiea et al., 2005). During this
phase, tailored QA checklists are used by team members to ensure the application of the
standards and guidelines identified in the first phase. Used as a screening device (Hosiea
et al., 2005), these checklists are designed and adapted by drawing on evidence from
research-based standards and by studying best practices in instructional and web design.
These templates are also intended to be used as self-assessment and improvement tools by
professors, by instructional designers and by instructional technologists, without
hindering creativity or freedom. Indeed, these templates and checklists are used throughout
the process to ensure consistency and efficiency and to foster a culture of quality.
In parallel, to these content-related templates, several production templates and
tools using PHP as an automation/scripting language and Adobe CS 3 as a production
Figure 2.
Process-oriented lifecycle
model for QA in e-learning
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toolbox are used. These ensure uniformity and consistency across the production
process by providing pre-built-in features based on the standards identified in the first
phase. For each production tool (text, images, audio, and video and animations),
a detailed QA checklist is used by team members during the production phase. A total
of 118 QA checklist items are used throughout the process (Figure 3).
To streamline this process, an advanced information system was developed, to
organize, track, collect, and generate reports about QA changes and needed updates.
By incorporating all of the checklists used during the production process, this system
facilitates QA implementation, tracking, and reporting, while it reduces the number of
clerical tasks associated with the process.
This information system enables designers to track and update the checklists
associated with their roles in the production process. The system also generates
QA summary reports by module, course, or degree program (Figure 4).
Finally, during the post-production and first offering phase of e-learning courses,
interface usability and student feedback surveys are administered and collected. Data
collected during this phase feeds back into the system to encourage the improvement of
course, content, and activities (and, occasionally, templates, and checklists).
By mirroring the production process and by using pre-defined content and
production templates, the model provides an operational framework that promotes
QA as a daily practice. Supported by an advanced information system, this
process-oriented model provides opportunities for continuous improvement and
refinement in order to ensure an effective learning experience.
Lessons learned
As an operational QA framework, the proposed model requires a supportive
environment that explicitly recognizes quality as a work value and as an enabler for
reaching organizational goals, while it documents and provides support, guidance,
Figure 3.
QA checklist example:
course module
content review
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reinforcement, and continuous improvement (Silimperi et al., 2002). In accord with this
important enabling condition, early trial implementation of the model offers some
valuable lessons:
(1) During the planning phase, clarifying quality expectations helps to set the
QA implementation path. It is crucial to provide a clear picture of the overall
quality requirements, expectations and process. In this regard, it is critical to
keep three production considerations in mind:
.Gain the buy-in of the academic staff by clarifying the overall process of
content collection and by explaining the importance of each content
collection template. Academic staff resistance often stems from a lack of
understanding of the purpose of the process and from unwillingness to
embrace new ways of developing courses.
.Be sure that the academic staff understand the meaning of the different
checklist items.
.Help the production team members to reach a common understanding of the
checklist items, to ensure that they will be applied systematically. As
Newton (2007) argues, quality is contingent upon how it is used and
experienced in practice by academics.
(2) During the design/production phase, burdening the production team with
additional checklists is likely to be counterproductive, unless roles and
responsibilities are clearly identified and understood. In addition, the QA
implementation must be supported by an information system which tracks and
facilitates clerical tasks. Having a flexible and efficient information system is
critical to the success of the QA model implementation.
(3) During the delivery phase, a double consideration is required:
.On the academic staff side, readiness and online teaching abilities have a
significant impact on how e-learning content is delivered. Thus, providing
both development opportunities and ongoing technical support is critical for
an effective e-learning experience.
Figure 4.
QA information system
environment
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.On the student side, students’ readiness, technical literacy, study strategies
and tactics influence their level of interaction with the content. The author
believes, with Ehlers (2004), that the outcome of any learning process does
not hinge exclusively on the production process, but rather on student
enablement and empowerment, by providing online orientations, ongoing
support, and regular and systematic feedback collection.
With these considerations in mind, it is important to reiterate that the proposed model is
an operational tool, a kind of roadmap which helps organizations implement efficient
and systemic QA procedures. However, the success of its implementation hinges on key
enabling variables including the clarification of quality requirements, a common
understanding of QA checklists and the support of both professors and students.
Suggestions for future research
This paper began by highlighting the inefficacy of existing QA frameworks and
procedures and by justifying the choice to structure a model around e-learning
development and delivery. After shedding some light on quality and QA definitional
issues, it reviewed the existing literature on QA mechanisms and frameworks. It is
against this backdrop that a three-phase model which parallels the course development
process has been proposed. This model is aimed both at moving quality from a static,
after-the-fact state to a more iterative and dynamic state and at promoting a culture of
ongoing self-improvement, instead of one of compliance. In other words, it is a model in
which QA becomes embedded into the daily processes of “front-line academics,”
enabling it to penetrate the core activities of course, development operation (Hodson and
Thomas, 2003). Ultimately, this approach has the potential to raise the overall quality of
the e-learning experience, increase organizational efficiency for the units developing
e-learning and address some of the scepticism that surrounds e-learning.
In recommending future research avenues, this paper supports the need for
comprehensive research to examine both the effectiveness of the proposed model and its
impact on students’ e-learning experiences. More specifically, how and to what extent do
e-learning courses which follow this model contribute to higher student outcomes? From
a procedural perspective, further research is needed to examine the effectiveness of the
information system designed to facilitate QA implementation, particularly by
examining how the system contributes to the promoting and embedding of QA
practices into daily routines. In other words, how does the proposed model contribute to
the promotion of a culture of QA within units developing e-learning courses?
As a concluding comment, it is should be said that although quality can be
contested and challenged, and although its implementation varies contextually and
requires enabling conditions, it remains clear that the QA debate will remain alive and
will continue to drive competition within the HE sector.
Notes
1. As a generic concept, e-learning refers to any learning experience which mixes three
interdependent dimensions: delivery and interaction tools (hardware and software); remote
or/and face-to-face location; and synchronous or asynchronous meeting time. With minor
nuances, this concept of “e-learning” can subsume other common concepts, including distance
education, distance learning, distributed learning, online learning, e-education, virtual
education, web-based learning, computer-based training, blended, and hybrid learning.
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2. Typically, HE inputs include students, staff and resources; processes include curriculum
design, teaching and learning methods, support, assessment and administration; and
outputs include student results, graduation, employment, and satisfaction.
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About the author
M’hammed Abdous is a Director for the Center for Learning Technologies at Old Dominion
University. His research interests include distributed learning trends, e-learning and quality
assurance, process reengineering and curriculum planning and development. M’hammed Abdous
can be contacted at: mabdous@odu.edu
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