Content uploaded by Lei Shi
Author content
All content in this area was uploaded by Lei Shi on Nov 28, 2015
Content may be subject to copyright.
The Evolution of the
Internet in the Business
Sector:
Web 1.0 to Web 3.0
Pedro Isaías
Universidade Aberta (Portuguese Open University), Portugal
Piet Kommers
University of Twente, The Netherlands
Tomayess Issa
Curtin University, Australia
A volume in the Advances in E-Business Research
(AEBR) Book Series
Published in the United States of America by
Business Science Reference (an imprint of IGI Global)
701 E. Chocolate Avenue
Hershey PA, USA 17033
Tel: 717-533-8845
Fax: 717-533-8661
E-mail: cust@igi-global.com
Web site: http://www.igi-global.com
Copyright © 2015 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in
any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher.
Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or
companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark.
Library of Congress Cataloging-in-Publication Data
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the
authors, but not necessarily of the publisher.
For electronic access to this publication, please contact: eresources@igi-global.com.
CIP Data
The evolution of the internet in the business sector : Web 1.0 to Web 3.0 /
Pedro Isaias, Piet Kommers, and Tomayess Issa, editors.
pages cm
Includes bibliographical references and index.
ISBN 978-1-4666-7262-8 (hardcover : alk. paper) -- ISBN 978-1-4666-7263-5 (ebook) -- ISBN 978-1-4666-7265-9 (print
& perpetual access) 1. Business enterprises--Computer network resources. 2. Information technology--Economic aspects.
I. Isaias, Pedro, editor. II. Kommers, Piet A. M., editor.
HD30.37.E96 2015
384.3’1--dc23
2014036396
This book is published in the IGI Global book series Advances in E-Business Research (AEBR) (ISSN: 1935-2700; eISSN:
1935-2719)
Managing Director:
Managing Editor:
Director of Intellectual Property & Contracts:
Acquisitions Editor:
Production Editor:
Development Editor:
Typesetter:
Cover Design:
Lindsay Johnston
Austin DeMarco
Jan Travers
Kayla Wolfe
Christina Henning
Allison McGinniss
Cody Page
Jason Mull
306
Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter 14
Students as Customers:
Participatory Design for Adaptive Web 3.0
ABSTRACT
The World Wide Web is changing, from the early Web 1.0 to the Social Web 2.0 and beyond to Web 3.0
interfaces, but more importantly, the users of the Web are also changing, and their numbers are increasing
rapidly in line with this evolution. In e-Learning, it is essential to be able to keep up with these trends and
provide personalized social interaction. Here, our main customers are our students, but these customers
do not come unprepared: they already have a great deal of Web experience, especially in the areas of
Social Networking Sites (SNS) and online interaction. Thus, it is essential to improve approaches used
in the past, where learners were only involved in the receiving part of the delivery process. This chapter
therefore proposes and explores applying participatory design methodologies in the early stages of the
social adaptive educational hypermedia system design process, showing also its benefits for further
design, implementation, and usage.
1. INTRODUCTION
The Web of today looks totally different from
that of the past. Its main driving forces are less
the technologies and mechanisms, but its thriv-
ing user communities. There are over 2.4 billion
Web users in the world, according to KPCB Web
Trends (Meeker & Wu, 2013). Moreover, younger
generations have embraced the Web as a normal
part of their lives, on which they spend a great
amount of time. For instance, according to Everfi
(Everfi, 2013), 13% of the 5500 American young
teens surveyed admitted to spending more than
five hours a day online, 16% of them admitted to
spending 3-5 hours, and 40% of them admitted
to spending 1-3 hours.
Lei Shi
University of Warwick, UK
Alexandra I. Cristea
University of Warwick, UK
Craig Stewart
Coventry University, UK
DOI: 10.4018/978-1-4666-7262-8.ch014
307
Students as Customers
In education, e-Learning is flourishing, with
most universities and even schools having a clear
e-presence and a varying proportion of online ma-
terials, including usage of e-Learning systems and
learning management systems (such as MOOCS,
Moodle, or older systems such as Blackboard,
WebCT, etc.). However, e-Learning lags some-
what behind in embracing the new technologies,
techniques and interaction models, for instance
e-Learning in the business (through lifelong
learning) or mobile sectors (ubiquitous learning).
In this global context, there is already a good
body of research available to support the benefits
of personalized education, both offline and online.
Targeting the latter, the research area of Adaptive
Hypermedia (AH) and Adaptive Educational
Hypermedia (AEH) (Brusilovsky, 2001) has been
growing rapidly during the past 20 years. It has
resulted in a plethora of AEH systems (AEHS) to
support, verify and evaluate the newly proposed
models, system architectures and methodologies.
Researchers in this area have been focusing on
posing and answering the six major questions
that define the core of adaptation, initially intro-
duced by Brusilovsky (1996), namely, 1) what
can we adapt? 2) what can we adapt to? 3) why
do we need adaptation? 4) where can we apply
adaptation? 5) when can we apply adaptation?
and 6) how do we adapt?. Asking (and answer-
ing) these questions enables researchers to define
adaptation process, in order to design an AEHS
that better identifies a learner’s knowledge level,
learning goal, preferences, stereotypes, cognitive
and learning styles, etc. (Brusilovsky, 2004) to
provide adaptive and adaptable learning content,
navigation, presentation and interaction. Whilst
researchers (and system designers) are of impor-
tance during the AEHS design process, the other
crucial role that has often been neglected is that
of the customer of an AEHS (such as the learner
or end-user).
Indeed, with the ever-increasing commoditiza-
tion of learning, and the rise in fees (especially
for higher education), students tend to act more
like customers than passive recipients of knowl-
edge, as they have often been considered in the
past. They also come normally with a very good
background on Web 2.0 (as in social) and some
Web 3.0 (as in both personalized and social) sys-
tems and platforms, albeit with less knowledge in
the area of e-Learning (including pedagogy and
meta-cognition of life skills such as Learning to
Learn). Indeed with the rise of this ‘student-as-
client’ paradigm, the business of higher learning
has broken the bounds of the traditional university
structures and ‘exploded’ onto the Web. MOOCs
are an excellent example of this, with vast num-
bers of students (often 100,000+) being able
to access courses designed by leading teachers
and researchers. These courses, like all previous
non-AEH courses, fall into the ‘one-size-fits-all’
trap (Brusilovsky, 2012), in that delivery of these
learning materials are not personalized to the
learner in anything other than a superficial man-
ner. Therefore AEH research and development
has a great deal to offer the business of educa-
tion, especially in using MOOCs (and Learning
Management Systems (LMS) such as Moodle)
as a vehicle for delivering a personalized lesson
to a large scale audience over the course of their
working life.
Furthermore, in the Web 2.0 era, a growing
number of researchers have been exploring the
ways to facilitate adaptive e-Learning by introduc-
ing a social dimension and integrating various Web
2.0 technologies. This identifies the advantages
of providing social media tools and supporting
linking learners, e.g., inquiry-based collaboration
(McLoughlin, 2007). Learners have been found to
also be more motivated to contribute to creating
an effective learning environment and enriching
learning experiences, supported by collaboration
and feedback from their peers (Dabbagh, 2011),
which brings the benefits of not only engaging
creating and sharing information and knowledge
within a collaborative learning context, but also
enhancing adaptation by monitoring and analyzing
learners’ social learning behaviors and interactions
308
Students as Customers
with each other (Brusilovsky & Henze, 2007;
Krause, et al., 2009; Magnisalis, et al., 2011; Shi,
et al., 2013).
Accordingly, the research focus has shifted
from an individual orientation, on a student and
his cognitive processes (Werner, 1986), to a so-
cial orientation. In comparison with AEHSs, the
social-AEHSs have been pushing the research area
of AH and AEH towards fostering diversification
of (explicit and implicit) user modeling (Barla,
2011), as well as richer user experience. Due to the
wide use and popularity of major social network
sites (SNS) such as Facebook, Renren, Weibo,
Tumblr, Pinterest, the new generation of learn-
ers have already been frequently using Web 2.0
functionalities and social apps, which makes the
social-AEH learning environment more familiar
to them, and subsequently increases the usability
of such an e-Learning environment (Shi, et al.,
2013b). The significant features of social-AEHS
make it more important than ever before to ensure
the learners’ participation during the AEHS design
process (Shi, et al., 2012a).
In the conventional research process of the
AH and AEH area, researchers normally took a
researcher-centered (or designer-centered) ap-
proach, while the learners were usually involved
only in the evaluation stage (Lohnes & Kinzer,
2007; Seale, 2009; Mulwa, et al., 2011). For in-
stance, the researchers firstly built an AHES with
their hypothesis and several new features, and then
conducted experiments to collect learners’ usage
data and/or distributed questionnaires, in order to
evaluate the system’s usefulness, ease of use, ease
of learning, satisfaction, privacy and data sharing,
and so on. However, the researcher-centered ap-
proach has limited ability to cater for the learners’
real needs (Looi, et al., 2009), because researchers’
knowledge about the adaptation process does not
necessarily guarantee that they know about the
end-users’ needs from the system. Not only are
more time and effort needed in the initial design
process, but the researchers (or designers) may
also face costly redesigns if they want to improve
the system in the follow-up research (or design)
iterations. Therefore, the adoption of user-centered
design (UCD) (Norman & Draper, 1986), partici-
patory design (PD) (Schuler & Namioka, 1993)
and the analysis of phenomena characterizing the
human-computer interaction (HCI) (Shneiderman
& Ben, 2003) process should be considered even
since the early design stages, in order to build
more usable systems (Valtolina, et al., 2011). If
the system were designed to provide its end-users
with exactly what they need, it would provide a
better user experience, as well as encourage users
to try features and contents, so that the system
would collect greater usage data, which could
eventually lead to a more useable system with
greater benefits for the learner.
In this chapter, we therefore illustrate how
the customers of e-Learning, the students (note
that in lifelong learning the ‘student’ is often also
the employee and as such this can have a direct
benefit for the business that employs them), can
be involved in the design process, by applying a
PD methodology in the early stage of designing
a social-AEHS. For this purpose, we report our
case study, which mimicked a large co-designer
experiment in a small format and extracted an
ordered list of initial application requirements,
aiming at exploring how to apply a PD methodol-
ogy and gathering issues and initial preferences
for future studies. We further show how this stu-
dent involvement has benefitted the later design,
development and usage of our adaptive, social
e-Learning system.
2. BACKGROUND:
TOWARDS SOCIAL AEH
Adaptive hypermedia (AH) is a field of research
at the crossroads of hypermedia and user model-
ing (Brusilovsky, 1996). The main goal of AH
research is to improve the usability of hyperme-
309
Students as Customers
dia applications, by making them adaptive and
adaptable. As the most popular research area of
AH, adaptive educational hypermedia (AEH)
combines adaptive hypermedia system (AHS) and
Intelligent Tutoring Systems (ITS), with the aim
of breaking away from the “one-size-fits-all” men-
tality (Brusilovsky, 2012). This means engaging
learner interaction as well as enabling e-Learning
systems to adapt to different learners’ specific
needs in a given context, and thereby providing a
personalized learning experience for each learner.
A lot of conceptual A(E)H frameworks have been
proposed since the early 2000s, aiming to simplify
the process of building adaptive systems. Well-
known frameworks include AHAM, proposed by
Wu (2002), XAHM, proposed by Cannataro et al.
(2002), LAOS, proposed by Cristea and De Mooij
(2003), the Munich model, proposed by Koch and
Wirsing (2006), GAF, proposed by Knutov (2008),
GAL proposed by Van Der Sluijs, et al. (2009)
and so on. Afterwards, some conceptual A(E)H
framework with social dimensions were proposed,
such as SLAOS proposed by Ghali and Cristea
(2009b) that extended from LAOS by adding a
collaboration mechanism, and ALEF proposed
by Šimko et al. (2010).
Prior (and partially concomitantly) to the
development of conceptual A(E)H frameworks,
a variety of AEH systems and AEH-based learn-
ing tools have also been researched. For example,
AHA! (De Bra, et al., 2003) was designed as
an adaptive hypermedia platform that delivers
XHTML pages as a series of concepts. Each
concept is recommended to the user according to
a predefined adaptation strategy. MOT (Cristea &
Kinshuk, 2003) is a web-based generic adaptive
hypermedia system based on the LAOS frame-
work for authoring adaptive learning materials.
The GRAPPLE (De Bra, et al., 2013) project cre-
ated the GALE (Smits & De Bra, 2011) delivery
engine, which extended the principles of AHA!,
in order to produce a more general purpose and
fully extendable delivery engine. As regards the
branch that the social dimension is introduced,
one of the first attempts was MOT 2.0 (Ghali and
Cristea, 2009a) that was developed based on the
SLAOS framework, introducing several social
facilities, such as the ability to hold a discussion
via chat tool, to rate, tag learning items, and get
recommendations of advanced learners to contact
(Cristea and Ghali, 2011). Progressor (Hsiao,
et al., 2013) is a web-based tool based on the
concepts of social navigation and open student
modeling (Mitrovic & Martin, 2007) that helps
students to find the most relevant resources in a
large collection of parameterized self-assessment
questions on Java programming. Topolor (Shi, et al,
2013c) is social adaptive personalized e-Learning
system that provides extensive social features and
personalized recommendations including learning
topic recommendation, learning path recommen-
dation, learning peer recommendation and so on,
in a adaptive e-Learning environment with rich
social interactions.
Learning is intrinsically a social endeavor (Ban-
dura, 1977; Zimmerman, 1989; Wenger, 2000).
Social facets of learning have been described in a
variety of theoretical frameworks about people and
their learning (e.g., (Vygotsky, 1978), (Wenger,
2009) and (Dabbagh & Kitsantas, 2012)). It is not
surprising that the AEH research area has shifted
to a social orientation. We believe that the invest-
ments and achievements in this social-AEH branch
are shaping the future of learning and learning as
a business, which is one of the reasons why we are
pursuing this particular research direction. AEHS
allows personalization of e-Learning, meanwhile
social medias enable learners to create, publish
and share content, facilitating interaction and col-
laboration. The integration of social media tools
into AEHS offers new ways for learner/customer
engagement and extended user modeling, thereby
creating the so-called social personalized adap-
tive e-Learning environments (SPAEE) (Shi, et
al., 2013d). Therefore our overall research aim
is to improve the (lifelong) learning experience
and learning efficiency in e-Learning via social
adaptive learning.
310
Students as Customers
3. PARTICIPATORY DESIGN AND
THE WE!DESIGN METHODOLOGY
As one of the most important User-centered
design (UCD) approaches, participatory design
(PD) places greater emphasis on allowing users
to make the decisions (Vink, et al., 2008). March
(2005) states “New and unexpected interactions
with the immaterial have expanded the design
territory to include people as designers”. Rather
than the traditional view that users (and custom-
ers) are not necessary to participate in the design
process before the requirement gathering phase,
PD requires designers and users to equally work
together to set design goals and plan prototypes,
and engages users as active members of the design
process (Muller, 2003). Researchers and system
designers who endorse PD approaches believe that
users are capable (with necessary knowledge and
skills) and should play a more active role during
the design process (Triantafyllakos, et al., 2008;
Shi, et al., 2012b). PD offers users opportunities
to participate during the design process so as to
increase the probability of a usable design. It pro-
vides a chance for system designers to work with
users so as to better understand users’ real needs.
It supplies a tool that helps to identify issues and
solutions (Rashidah, 2011).
The research on learners as co-designers of
educational systems has been increasingly ap-
pealing to researchers. Könings, et al. (2010)
assert PD can be “adapted for use in education
as a promising approach to better account for
students’ perspectives in the instructional design
process in different school subjects”. Seale (2009)
claims that participatory methods have “the po-
tential to both empower students and increase the
possibility that teachers will respond to student
voices”. Many PD approaches introduce learners
as co-designers in the design process, and bring
together design techniques of needs assessment,
evaluation, brainstorming, prototyping, consensus
building and so on. However, most of the existing
PD methodologies have strict requirements, and
most of them are focused on learning content de-
sign only (Triantafyllakos, et al., 2008). Learners
are the core participants in an e-Learning process,
so it is essential for the system designers to take
into consideration the learners’ opinions. Involv-
ing learners in the design process brings benefits
not only for applications, but also for the learners
themselves, because it can help exchange knowl-
edge between students and designers (Roda, 2004).
As one of the PD methodologies, We!Design
is student-centered and can be easily applied in
real educational contexts (Triantafyllakos, 2008).
It brings some merits compared to other PD
methodologies:
1. Conducts cooperation between students and
designers in a short period of time;
2. Supports a content-independent learning
process, including note-taking and assess-
ment, and
3. Exploits the potential of highly computer-
literate students who are driven to collaborate
in order to produce a description of needs,
task sequences and user interface prototypes
(Triantafyllakos, 2008).
For these reasons, we have opted to use the
We!Design methodology in our research for re-
quirements analysis.
The We!Design methodology contains two
phases (see Figure 1).
In PHASE 1, several parallel design sessions
are conducted with small groups of students under
the supervision of coordinators, aiming at propos-
ing a low-tech prototype and a requirements list.
The size of session groups is kept small, in order
to minimize conflict possibility between the stu-
dents, reduce time cost, and establish a friendly
and informal atmosphere. Each session consists
of three stages, including needs collecting, tasks
sequencing and prototype designing. In the first
stage, needs collecting, students build a set of needs
based on their experience of using a similar system
and their expectations from a new system. In the
311
Students as Customers
second stage, tasks sequencing, students design
task sequences to satisfy the previously built set
of needs. In the third stage, prototype designing,
students design a low-tech prototype application
to complete the designed task sequences.
During PHASE 2, the system designers ana-
lyze the requirements proposed in PHASE 1 and
synthesize them into a single application, with an
ordered requirements list. Initially, the designers
organize, group and rewrite the collected needs to
avoid overlapping. Next, these needs are ordered
based on the number of sessions that they are
proposed and their importance assessed by the
students. Finally, the designers compile the diverse
task sequences of each final need into one task
sequence, analyze the prototyped designed by
the students, and eventually synthesize the final
prototype application. In the next section, we
will present the detailed process of applying the
We!Design methodology, together with the actual
data collected from the performed case study.
4. APPLYING PARTICIPATORY
DESIGN FOR ADAPTIVE WEB 3.0
4.1 Setup
In our small-scale case study, 2 coordinators and
6 undergraduates participated. One coordinator
Figure 1. The We!Design methodology (Triantafyllakos, 2008)
312
Students as Customers
was a computer science Ph.D. student from the
University of Nottingham, UK; the other one
was a computer science Ph.D. student from the
University of Warwick, UK .The 6 undergraduate
students were from the ‘Politehnica’ University of
Bucharest, Romania. They were 4th years com-
puter science students, studying a course entitled
‘Semantic Web’.
A short seminar was delivered at the beginning
of the case study to introduce the experimental
process, explain the case study’s goals, and recall
the required background knowledge including
how to design a system and what an AEH system
is. Firstly, one coordinator presented the concept
of AH and AEH, followed by some case stud-
ies of AEH systems, including AHA! (De Bra,
2003), MOT 2.0 (Ghali, 2009) and LearnFit
(Essaid, 2010). Then, the coordinator introduced
the concept of social networking sites (SNS) to
the students. All the students were, as expected,
familiar with SNS, such as Facebook, Google+
and YouTube, etc. They were also familiar with
UML and UML-based design.
Thereafter the students could take upon them-
selves the main roles of discussing and presenting,
while the coordinators were in charge of time
controlling and summarizing. The seminar focused
on the features of the AEH systems and SNS, and
aimed to acquaint the students with both domains,
and encourage them to think deeply about these
two kinds of system, so they could integrate both
to design new social-AEH systems.
4.2 Phase I: Design
Session with Students
We conducted two parallel design sessions, each
of which consisted of 3 students, and lasted for
about 2.5 hours. The two coordinators supported
these sessions, without interfering unless they
considered it necessary to bring the students back
on track. One coordinator was a human computer
interaction (HCI) expert, whose role was that of
ensuring that students consider preserving the
usability of the system; the other coordinator was
an e-Learning system expert, whose task was to
be preventing the students from loosing track of
the system design goals.
For facilitating the work, students in a group
sat together. In front of them was a table with
pens and a big white paper to record their ideas
on, and eventually draw the user interface of the
prototype. The two design sessions were recorded
by a video camera, so the coordinators could fo-
cus on guiding the case study and solve current
issues, instead of noting the problems occurred
for further research.
Stage 1: Needs Collecting
In the needs collecting stage, the students were
asked to extract a set of needs that are currently
not met, according to their previous e-Learning
experience. The expectation was that these needs
could be addressed by using a social-AEH system.
The students contributed to the needs collection
by brainstorming and discussing ideas. Initially,
the students considered the main features that they
expected to be provided by such an e-Learning
system, as well as briefly discussed problems
that they encountered when using such systems
previously. All the students had opportunities to
present their own ideas. Turn taking in suggestions
was supported. Additionally, while one student
was presenting, the others were encouraged to
ask questions and provide suggestions and com-
ments. Afterwards, the students summarized all
the ideas into an initial need list, and then continu-
ally elaborated, categorized and evaluated these
needs. As a result of this process, 97 ‘raw’ needs
were proposed and ordered into a requirement list,
according to their perceived importance.
Stage 2: Task Sequencing
In the task sequencing stage, personas and sce-
narios were adopted as a lightweight method to
capture the system requirements. Personas contain
313
Students as Customers
users’ background information and specific situ-
ation related to using the system (Cooper, 2007).
Four personas were created to outline the real
characteristics of the system’s end-users. Take
one for example:
Michael is a sophomore student, studying a course
of ‘Java Programming Language’. He has learned
PHP, and achieved higher scores than most of the
other students. He prefers to analyze examples,
and then design his own program to check whether
he’s learnt the constructs from the examples. He
likes to share and discuss with other students.
Scenarios, such as the one above, create a story
with settings, personas and a sequence of actions
and events (Carroll, 2000). One of the designed
scenarios was:
When Sam is debugging his program using the
programming tool provided by the system, he re-
ceives a message from his friend asking for help.
He preserves his work, and asks what this friend
exactly needs.
In this stage, personas and scenarios were used
to describe the interaction between the persona
and the potential application to fulfill the proposed
needs, and enable rapid communication about
usage possibilities that might satisfy the needs
proposed in STAGE 1.
Stage 3: Prototype Designing
This stage was a refinement process, asking the
students to convert the needs collected in STAGE
1 and the task sequences designed in STAGE 2
to concrete requirements, so as to design a low-
tech prototype application. Firstly, the students
portrayed the final task sequences and visualized
the scenarios on the large shared white paper
with necessary notes to present the basic ideas
of the interaction process and user interface. For
instance, the students drew a dropdown list that
could be used as a menu to switch between dif-
ferent views of the concept structure. Secondly,
the students re-evaluated each component from
the user interface, and proposed new components
and/or re-organized existing components, to make
sure each proposed task sequence could be com-
pleted smoothly. Finally, a stereotypical end-user
role-play was conducted, to evaluate the usability
of the designed prototype.
4.3 Phase II: Application Synthesis
In PHASE II, the principal designers gathered
and analyzed the product designed in the first
phase to synthesize a single application. The
requirements were firstly grouped into 35 final
ones, by removing duplicates. Next, they were
ordered according to the estimated importance,
which was computed according to the number of
times the requirements appeared in the students’
suggestions, in one form or another. Then, these
requirements were categorized into four catego-
ries, which represented the main areas for which
features could be built in a system, according to
the designer, and which are as follows:
1. Learning: Here entered, for example, re-
quirements such as using of multiple types
of files, including photos, videos, slides, etc.;
allowing for multiple files was considered of
high importance by students; other (optional)
requirements of lesser importance were, for
example, taking tests after learning a topic;
getting assessment and feedback from teach-
ers; etc.
2. Social Networking: This category included
important requirements such as creating
groups that are registered for the same topic;
and, in decreasing order of priority, discuss-
ing the topic with other students; etc.
3. Adaptation: This category involved require-
ments such as recommending other topics
according to the current learning topic;
recommending topics according to student’s
314
Students as Customers
knowledge level and other students’ rating;
etc.
4. Usability: This category listed requirements
such as visibility of the system status; instruc-
tions and tips; graphical user interfaces; etc.
The results of these phases are described in
section 4.4 below. However, before this
data-mashing phase, we have gathered
more information from students, as
follows.
4.4 Additional Quantitative
and Qualitative Feedback
Gathering: The Questionnaire
The students who participated in the design ses-
sions were also invited to answer a questionnaire
with 28 questions. They were asked to evaluate
the e-Learning environments that they had used
in the past, and to elicit their extra expectations
for features of a new social-AEH system. As
the students already went through the introduc-
tory material and design sessions, their answers
were more informed, and were able to help the
designer understand the priorities students set on
the previously extracted requirements. Due to the
limited space, only selected results are shown in
this section.
Students’ Previous Experience
with E-Learning Systems
There were several reasons for students to use e-
Learning systems in the past, as shown in Figure
2. The most important reason they gave was to
‘Save Time and Effort’. This corresponded to
their answers in the open-ended questions part of
the questionnaire, where the students stated that
‘Availability 24/7, everything is organized in one
place’ as being some of the features of e-Learning
systems that they liked the most. Out of this clear
preference, one of the requirements would be to
provide a simple, constantly available ‘one stop-
shop’, where all the material and functionality is
present, and thus not increase the learning burden.
From the point of view of social websites used,
the questionnaire result also indicated that all the
students have experience of collecting learning
resource from Wikipedia (see Figure 3). Wikipe-
dia is indeed the largest general reference on the
Web, offering more than 30 million articles (List
of Wikipedias, 2013). YouTube was mentioned as
the second most popular social networking website
to collect learning resources from, while the third
one was LinkedIn. In the case study, students
also mentioned the requirements of access to and
search for open learning resources from outside
of the system. Therefore access to open learning
resources such as Wikipedia, and searching for
Figure 2. The reasons for using e-Learning systems
315
Students as Customers
learning materials, should have a high priority
to be developed.
After finding out about the students’ experi-
ence with e-learning systems and social platforms,
we further asked about specific features, if they
should or not be included in the system.
Preferences for the New
System Features
In Figure 4, 67% of the students prefer courses to
be published by both teachers and students; while
the other 33% think that the courses can only be
published by teachers. Besides, more students
(83%) prefer topics to be recommended according
to students’ ratings rather than the count of visits.
Figure 5 shows that half of the students prefer that
learning paths are kept static from creation; while
the other half consider that learning paths should
be adapted to the learning context. Furthermore,
the same percentages of students agree that learn-
ing paths can be both designed by teachers and
calculated by data collected from other students’
behaviors. Figure 6 shows that 17% of the students
prefer asynchronous interaction with others in the
system (such as comments); while the other 83%
of the students prefer synchronous interaction such
as chat window. Figure 6 also shows that 33% of
the students hope to have all social interaction
tools when they begin to use the system; while the
other 67% of the students prefer to obtain more
social interaction tools when they move up to a
higher user-level.
Importance of the Selected
System Features
The students were further asked to rate the im-
portance of a list of features pre-selected by the
system designers on a 1-5 scale (1 = not important
at all; 5 = very important). Table 1 displays the
means and standard deviations of the result. The
feature considered the most important by the
students is the ‘Exchange of knowledge and ap-
proaches’ with the maximum mean value (4.83)
and the minimum standard deviation (0.41). The
minimum ones were ‘Multimedia delivery’ and
‘Recommendation of groups and other students’,
with an average of 3.67 > 3 and a standard devia-
tion of 0.82. However, some clear preferences
could be seen from the students’ responses, and
these were further processed towards the system
requirements in the following subsection.
Suggestions on Designing a
New E-Learning System
The questionnaire also contained some open-
ended questions that allowed students to provide
unrestrained wide-range responses, which could
reveal originally unanticipated findings in the
questionnaire (Reja, 2003). The suggestions of the
students are summarized in the list below (ranked
by the implementation priority, and labeled with
the functionality aspects):
Figure 3. SNS websites for collecting learning resources
316
Students as Customers
Figure 4. Preferences for learning material
Figure 5. Preferences for learning path
317
Students as Customers
Figure 6. Preferences for interaction
Table 1. Allocated importance of the features of an adaptive social e-learning system
Feature Scale of Importance
Mean (1-5) Standard Deviation
Exchange of Knowledge and approaches 4.83 0.408
Feedback of learning process and results 4.67 0.516
Recommendation of learning path 4.67 0.516
Trust of group members 4.50 0.548
Share learning materials and experience 4.50 0.548
Revision exercises 4.33 0.516
Trust of user-generated learning contents 4.33 0.816
Recommendation of related topics 4.00 0.894
Collaborative learning and group activities 4.00 0.894
Interactions and tips 4.00 0.632
Interactive learning content 4.00 1.265
Multimedia delivery 3.67 0.816
Recommendation of groups and other students 3.67 0.816
318
Students as Customers
S1. The recommendation of learning materials
for a particular student should be based on
her/his performance during learning, mixed
with results from the exercise/tests. – Per-
sonalization & Exercises;
S2. Students should be able to create their own
learning paths in the courses that they were
interested in, while other students could
provide suggestions or use these learning
paths for their own study. – Adaptability &
Open Student Models & Social Interaction;
S3. The system should provide an interface to
access online libraries for reference while
students are learning related topics, and
make is possible for the students to save
these references inside the system. – Open
corpus & Social Interaction;
S4. Exercise tools are essential, especially for
practice courses such as programming lan-
guage. It would be better to learn by using
the knowledge rather than just reading some
chapters. – Exercises;
S5. The system should introduce some learning
aid for students to improve their learning
efficiency. – Usage Tutorials & Learning
aids.
S6. The user interface should be as simple as
possible, concentrating all needed resources
in one place (a ‘one stop-shop’: either physi-
cally - with all material in one place, or on
one server, or virtually - as in a portal to
all the needed information). – Portal & User
interface.
4.5 Requirement List
Finally, the designer merged the results from
PHASE 2 and the responses from the questionnaire
into a requirement list, ordered by their priority.
The latter was computed from the estimated impor-
tance of a requirement, as stated by the students,
and from the separate information on the number
of times a (version of the) requirement appeared
during the design sessions. The resulting list of
the ordered requirements for social-AEH systems
is shown in Table 2.
5. DISCUSSION OF THE
CASE STUDY
In PHASE 1, the coordinators had to be very clear
in which situation they needed to intervene and to
what extent. In the needs collection stage, espe-
cially at the beginning, the students were always
impatient to start exploring solutions to satisfy the
proposed needs rather than focusing on collect-
ing needs, so the coordinators had to stop them
in time. In the task sequencing stage, personas
and scenarios were used to capture the require-
ments of the system. One of the best practices is
to identify primary personas, ‘the individual who
is the main focus of the design’ (Cooper, 2007).
To be primary, a persona is ‘someone who must
be satisfied but who cannot be satisfied with an
interface designed for any other persona. An inter-
face always exists for a primary persona.’ (Cooper,
1999) With regard to scenarios, storyboards or
customer journeys were used to test the validity
of design and assumptions. The students had to
design an appropriate level of detail, because of
the short period of time. In the prototype design-
ing stage, some solutions were found flawed to
some extent, and the students might be unwilling
to fix flaws or they might need extra time. The
coordinators should encourage them to get the
solution as well as control the time, as even if
the work was incomplete, the highlighted issues
could still inspire the designers.
In PHASE 2, the designers arranged the re-
quirements proposed by the students, the descrip-
tions of content-based requirement. It is possible
for the designers to misunderstand the original
meaning intended by the students, so it is neces-
sary to show the reorganized requirements to
the students, and ask them to check whether the
requirement list is consistent with their original
ideas. Still, even though the students confirmed
319
Students as Customers
Table 2. Ordered requirements list for a social-AEH system
Category Requirement N1I2P3
Learning Use multiple types of files, e.g. PDFs, photos, videos, slides, etc. 5 (q) 1 1
Take tests after learning a topic 4 (q) 3 2
Get assessment and feedback from teachers 5 (q) 4 3
View learning progress in percentage 5 7 4
Tag and flag up topics in the learning path 1 2 5
Access to open learning resource, e.g. Wikipedia 6 5 7
Search learning resource within and outside of the system 6 6 8
Use interactive learning content, e.g. debugging tools. q 9 6
Contribute to learning materials by creating and uploading files 3 8 9
Choose to view the whole or partial learning path 1 10 10
Social
Networking
Create groups that are registered for the same topic 3 1 1
Discuss the current learning topic with other students 6 4 2
Set access rights for learning materials q 8 3
Set access rights for groups q 9 4
Ask and answer questions of other students 5 3 5
Create groups that share common learning interests 4 10 6
Use feedback & questions forum at the end of each lesson 5 5 7
Share and/or recommend learning materials 2 2 8
Use communication tools to chat and leave messages 4 (q) 6 9
Write comments/notions wherever and whenever they want 5 7 10
View history discussion when selecting a particular topic 1 11 11
Design and publish courses for others to use q 12 12
Adaptation Recommend other topics according to the current learning topic 5 (q) 2 1
Recommend topics according to student’s knowledge level 4 (q) 1 2
Recommend topics by referring to other students’ rating 2 (q) 3 3
Adapt learning path according to learning progress 2 (q) 4 4
Adapt learning tools according to student’s user-level 1 7 5
Adapt social interaction tools according to students user-level q 8 6
Recommend other students according to the current topic q 6 7
Recommend other groups according to student’s interests q 5 8
Usability View system status 2 3 1
Use graphical user interfaces 4 1 2
Get instructions and tips 3 (q) 2 3
Select full screen option 1 4 4
Set themes, layout, etc. 2 5 5
1. N: the number of times the requirement appeared in the students’ suggestions, (q: from questionnaire results).
2. I: the average importance of the requirement proposed by the students from the two design sessions.
3. P: the final resulting priority of the requirement, according to the principal designers.
320
Students as Customers
the requirements, it would be still possible that
the designer deviates from their intended design.
Overall, the students willingly contributed to
generating the requirements, and they were satis-
fied with both the experiment and the knowledge
they acquired during the experiment. From the
system designer’s perspective, the requirements
obtained represents a generic level of detail into
the requirements definition, which is collected
as natural language statements describing what
services the system is expected to provide. Be-
sides, these requirements create a common vision
between the students and the system designers,
to make sure the system that will be developed
is what the students really need. The next step
is to generate the requirements specification
(intermediate-detail) and then the application
specification (high-detail) (Sommerville, 1995).
The questionnaire results indicate that cur-
rently the students’ favorite equipment to access
e-learning system is the laptop. While Canalys
recently released the worldwide shipment esti-
mation of equipment for Web access (Titcomb,
2013), which indicates mobile computing devices,
especially smartphones, tablets and phablets (a
cross between phones and tablets), have a much
greater potential. This means that cross-platform
compatibility, including adaptive layout and adap-
tive screen orientation (landscape or portrait), is
urgently needed.
Facebook is the largest SNS in the world and
has 1.19 billion monthly active users, and 728
million daily active users on average in Septem-
ber 2013 (Facebook Newsroom, 2013), but most
people use Facebook for entertainment (Tosun,
2012) rather than learning, which is why the
questionnaire result shows that only 16.7% of
the students chose that they have ever collected
learning resource from Facebook.
Another interesting result is that half of the
students chose ‘Compulsory to Use’ as a reason
to use an e-learning system. This may be because
the systems are hard to use, or the students are
not confident to use them. Therefore it is crucial
to evaluate and analyze existing systems to find
out how to improve them or how to design a
better new system. The opinions of the systems’
end-user, the students, are very important, and
many aspects (e.g., system usability, accuracy of
recommendation, intended learning outcomes,
learning context) of the systems need to be taken
into consideration. Therefore the evaluation should
be conducted using a multi-dimensional approach
(Ozkan, 2009).
The main difference of this case study from the
original We!Design methodology was that, all the
students who participated in the design sessions
were asked to answer a questionnaire for collecting
more information. Although the coordinators were
trying to avoid transferring their own opinions
in the design session, it remains possible that
they could still have influenced the students. In
contrast to the design sessions, the questionnaires
have uniform questions but no middleman bias,
and the research instrument does not interrupt
the students. Besides, the structured question-
naires enable the responses to be standardized,
hence easier to analyze. The questionnaires were
delivered after the application synthesis phase,
because on the one hand, as the designers have
already analyzed the requirement proposed by the
students, they will be able to asked pointed ques-
tions to further understand the students’ opinion;
and on the other hand, since the students have gone
through the design session, they may like to have
more chance of proposing extra expectations and
helping the designers understand the priorities of
the previously extracted requirements.
One issue to raise here is that although the
software engineering knowledge of the computer
science undergraduate students can help shorten
the design duration, as the author of the We!Design
methodology stated (Triantafyllakos, 2008), this
may also have limited their ability to create a
domain-independent e-learning system. For in-
stance, they mentioned the importance of tools for
practice courses such as programming language
courses, but they did not consider multimedia
321
Students as Customers
delivery as highly important, when for instance,
for art and social science subjects, the quality of
multimedia transmission and presentation might
be very important.
6. THE RESULT: TOPOLOR
Based on the case study result (and also the lit-
erature review on e-Learning systems and social
networking sites), we further developed an over-
arching research hypothesis that extensive social
features (based on suggestions S2 and S3 above),
personalized recommendations (based on S1) and
Facebook-like appearance of a system (anticipated
to make the environment more familiar to learn-
ers), subsequently increases the usefulness and
usability of the system (S6).
To be able to validate this hypothesis, a first
version of a personalized social e-learning system,
Topolor1 (Shi, et al., 2013c), was built.
This first prototype provided a learning portal
(S6) with a Facebook-like appearance (Shi, et al.,
2013b) as shown in Figure 7, featuring the profile
avatar and learner information, the fixed-position
top menu and the left side bar for navigation, and
the information flow wall for social interaction,
etc. It supports learning content adaptation (S1),
learning path adaptation (S1), adaptation to test
results (S4), and peer adaptation (S1-S3), and
provides a social e-learning environment (S2, S3),
i.e., learners can comment on a topic, ask/answer
a question about a topic, create and share notes
Figure 7. Screenshot of Topolor (first prototype) home page
322
Students as Customers
related a topic, etc. This represents thus a much
broader range of adaptation than in regular adap-
tive hypermedia. It has been used as an e-Learning
platform for MSc level students in the Department
of Computer Science, at the University of War-
wick, and the usage data is being anonymously
collected for analysis.
In the last year and a half, Topolor has been
under iterative development and evaluation, aim-
ing at testing the hypothesis stated above and
progressing towards achieving the overall research
aim, as mentioned in Section 2. By the time of
the writing of this chapter, we have finished the
first two iterations of system development, as
well as two rounds of evaluation. Following the
experimental study on applying PD methodolo-
gies in developing a social-AEH system reported
in this chapter, which has given us an excellent
starting point in the system design. We have also
conducted several other studies on, among others:
subjective assessments of Topolor’s usability (S6);
social interaction design in a social-AEH system
(S2, S3); Learning behavior pattern analysis in
Topolor (S1-S3); and building light gamification
upon social interactions (S3, S5, S6).
In the primary evaluation of Topolor, SUS,
a ten-item attitude Likert scale (Brooke, 1996)
questionnaire was used to obtain a global view
of subjective assessments of usability for To-
polor (Shi, et al., 2013f). Topolor was used to
teach ‘Collaborative Filtering’ during a two-hour
lecture, after which the students were asked to
fill in an optional SUS questionnaire. 10 (out of
21) students’ responses were received. The SUS
score was 75.75 out of 100 (with 0 worst and 100
best score, and σ=12.36, median=76.25), and the
Cronbach’s alpha value of the questionnaire data
was 0.85 (>0.8). Therefore, we could claim that
the first prototype of Topolor’s usability meets our
initial expectations. Positive qualitative feedback
from the students supported this SUS result.
We have reported the evaluation of Topolor’s
social toolset on each feature’s usefulness and ease
of use, as well as the reliability of the results (Shi,
et al., 2013h). Topolor was designed to include a
wider range of social interaction features than pre-
vious adaptive educational hypermedia systems.
The evaluation results indicated students’ high
satisfactions on both usefulness and ease of use of
the various social features that Topolor provides,
with ‘excellent’ level of reliabilities (Cronbach
& Shavelson, 2004). The oral feedback was that
they would have wanted to have more lessons in
this e-Learning environment. Decisive in this,
we believe, was the fact that a lot of the social
features had a look and feel familiar to them that
was similar to the popular Facebook environment.
Such familiarity is essential to consider in design-
ing such systems.
User modeling is a process where learner’s spe-
cific needs are built and maintained (Brusilovsky
& Millán, 2007), either by explicitly gathering or
implicitly obtaining user data during user-system
interaction, in order to provide personalized and
adaptive services. Using an implicit approach,
a social-AEHS can track learning behaviors
unobtrusively and ubiquitously, hence inferring
unobservable information from observable infor-
mation about a learner. To provide suggestions
on the further development and improvement of
implicit user modeling in Topolor, we analyzed
learning behavior in the first prototype, using data
mining methods and visualization tools (Shi, et al.,
2013g; Shi, et al., 2013j). We explored learning
behaviors patterns in Topolor, focusing on the
analysis of action frequency and action sequence.
The results revealed some interesting individual
learning behaviors and some common learning
behavior patterns (e.g., allowed for identification
of the social learner, using social tools more than
learning, in contrast to the focused learner, using
learning content more, etc.), which suggested
possible directions both to improve implicit user
modeling for the next prototype of Topolor, and
to design user modeling for other social-AEHS.
The evaluation results of the social interaction
features in Topolor showed high students satis-
faction (Shi, et al., 2013i), but we are still keen
323
Students as Customers
to improve these features to make Topolor more
engaging. Therefore, according to the analysis
on the usage of social interaction features, we
proposed three light gamification mechanisms
to build upon those identified social interaction
features with relatively lower rating. Gamifica-
tion is implemented for creating more interest,
attention and interaction to make a system more
engaging (Deterding, et al., 2011). Light gami-
fication mechanisms here literally mean that we
intend to introduce gamification as a solution to
symbiotically make Topolor easier to use and more
engaging, rather than replace its social learning
community (Shi, et al., 2013e). The proposed
three gamification mechanisms include: 1) tip
mechanism as packaged missions (Kim, et al.,
2009) to navigate students to use various features
in Topolor (S5); 2) badge mechanism to cultivate
an environment of collaborative and competitive
e-Learning (Domínguez, et al., 2013)(S5); and
3) peer-review mechanism to prevent learners
from abusing features in Topolor and improve the
quality of posts (S5).
Based on the studies mentioned above, the
second prototype of Topolor was developed (see
Figure 8 for its homepage screenshot). We have
improved various features provided in the first
prototype and introduced some new features such
as open student modeling (Mitrovic & Martin,
2007) (S2) and light gamification mechanisms
(Shi, et al., 2013e)(S5), aiming to further validate
our overall research hypothesis by testing the
improved features and newly introduced features,
e.g., social interactions and adaptation strategies.
The evaluations have started already, and we are
now in the data-gathering phase.
7. FUTURE RESEARCH DIRECTIONS
The participatory design methodology applied in
the experiment is effective and straightforward, as
expected. We believe the readers of this chapter
Figure 8. Screenshot of Topolor (second prototype) home page
324
Students as Customers
can benefit from the showcase of the way of ap-
plying this methodology in the case study. In this
section, we would like to further suggest several
potential research directions, according to the
experience from this research.
Firstly, the We!Design methodology points out
that it is necessary to involve the students with
software engineering knowledge background in
the design sessions. We did observe its benefits.
For instance, it was effective to let them design
personas, scenarios and design a user interface.
But then we also noticed some shortcomings. For
one thing, their computer knowledge might limit
their ability to design a general e-Learning sys-
tem, as mentioned in section 5; for another, they
might somehow think from a system developer/
designer’s point of view, rather than that of an end-
user, a customer of the system. Therefore, one of
the potential research directions is to investigate the
balance of the different knowledge backgrounds
of the students who participate in the design ses-
sions, and how to lead them to communicate and
cooperate smoothly and effectively.
Secondly, this methodology was applied in the
very beginning of the system design process to col-
lect needs and prototype user interfaces. It would
be also valuable to explore its usage in an iterative
system development process. For example, at the
beginning of the second development iteration, the
design sessions can extract users’ opinions of their
experience of using the system, and collect their
needs for improving the existing features and their
expectations of new features for the next version,
because in this stage, they might have already had
deeper understanding about what the system does
and how the system works.
In using an iterative design methodology it is
also possible to refine the priority lists according to
more focused user groups. The work presented in
this chapter describes the first stage of the Topolor
design process, which focused on Higher Educa-
tion students, but can also find applicability to the
customers to be found in the Lifelong Learning
arena. As in any business, modern educational
environments need to be aware of the degree of
customer satisfaction in the products that they
use, and the PD process has proved to be an ideal
avenue to creating a system that brings this aspect
into the ground level of system design.
8. CONCLUSION
The emergence of Web 2.0 and the developmental
trend towards Web 3.0 is changing many perspec-
tives in people’s everyday life, especially the way
that they assimilate, create and share knowledge.
On the other hand, the evolution of the younger
generation’s preferences is pushing the features
and services provided by Web applications to be
social, adaptive and personalized. Learning, as
one of the most important ongoing activities in
daily life, essentially means that e-learning needs
to keep up with these trends, because the learners,
the customers of the global education market,
are not satisfied any more in being the passive
receivers of knowledge. However, the design
methodologies for adapting and personalizing
social e-Learning environment have not yet been
extensively researched. This chapter, therefore,
proposes and explores applying participatory
design methodologies in the early stages of the
social adaptive educational hypermedia system
design process, showing also its benefits for further
design, implementation and usage.
In this chapter, we have reported our case study
on applying a participatory design methodology,
(i.e., the We!Design methodology), in the early
stage of designing a social-AEHS. This study
has created a practical sketch of the participatory
design methodology. From this study, we have
achieved our goal to gather issues and initial
preferences for our follow-up research. The results
from the experiment have been used not only for
starting the initial implementation of Topolor, but
also guiding further development. Therefore, we
suggest that it is crucial to get the customers of
e-Learning, the learners, involved in the whole
325
Students as Customers
system design process, even in the very begin-
ning, and allow them to make decisions on what
services the system should provide and how to
present these features. This is especially neces-
sary in the areas of Web 2.0 and the emerging
Web 3.0, as the experience of these end-users in
using these technologies in other contexts outside
e-Learning is sizeable. Thus, e-learning providers
and implementers need to take into account this
wealth of knowledge, and this chapter illustrates
a simple and straightforward way of doing it, also
further justified by the results of the evaluations
of the implementations created on this basis.
This chapter also sheds some light into the
applicability of Web 2.0 and especially Web 3.0
technology and theory in e-learning, and the
necessity of bringing these fields together to
enhance the experience of our clients/customers,
here, the learners.
REFERENCES
Bandura, A. (1977). Social learning theory. Aca-
demic Press.
Barla, M. (2011). Towards social-based user mod-
eling and personalization. Information Sciences
and Technologies Bulletin of the ACM Slovakia,
3(1), 52–60.
Brooke, J. (1996). SUS-A quick and dirty usability
scale. Usability Evaluation in Industry, 189, 194.
Brusilovsky, P. (1996). Methods and techniques of
adaptive hypermedia. User Modeling and User-
Adapted Interaction, 6(2-3), 87–129. doi:10.1007/
BF00143964
Brusilovsky, P. (2001). Adaptive educational hy-
permedia. In Proceedings of International PEG
Conference (pp. 8-12). PEG.
Brusilovsky, P. (2004). Adaptive educational
hypermedia: From generation to generation. In
Proceedings of 4th Hellenic Conference on In-
formation and Communication Technologies in
Education, (pp. 19-33). Academic Press.
Brusilovsky, P. (2012). Adaptive Hypermedia for
Education and Training. Adaptive Technologies
for Training and Education, 46.
Brusilovsky, P., Farzan, R., & Ahn, J. W. (2006).
Layered evaluation of adaptive search. In Pro-
ceedings of Workshop on Evaluating Exploratory
Search Systems at SIGIR06 (pp. 11-13). ACM.
Brusilovsky, P., & Henze, N. (2007). Open corpus
adaptive educational hypermedia. In The Adaptive
Web (pp. 671–696). Springer Berlin Heidelberg.
doi:10.1007/978-3-540-72079-9_22
Brusilovsky, P., & Millán, E. (2007). User models
for adaptive hypermedia and adaptive educational
systems. In The adaptive web (pp. 3–53). Springer-
Verlag. doi:10.1007/978-3-540-72079-9_1
Cannataro, M., Cuzzocrea, A., Mastroianni, C.,
Ortale, R., & Pugliese, A. (2002, May). Model-
ing adaptive hypermedia with an object-oriented
approach and XML. In Proceedings of Second
International Workshop on Web Dynamics. Aca-
demic Press.
Carroll, J. M. (2000). Five reasons for scenario-
based design. Interacting with Computers, 13(1),
43–60. doi:10.1016/S0953-5438(00)00023-0
Cooper, A., Reimann, R., & Cronin, D. (2012).
About face 3: The essentials of interaction design.
John Wiley & Sons.
Cooper, A., & Saffo, P. (2004). The inmates are
running the asylum (Vol. 1). Sams.
Cristea, A., & Kinshuk, K. (2003). Considerations
on LAOS, LAG and their Integration in MOT. In
Proceedings of World Conference on Educational
Multimedia, Hypermedia and Telecommunica-
tions (pp. 511-518). Academic Press.
Cristea, A. I., & de Mooij, A. (2003, May). LAOS:
Layered WWW AHS authoring model and their
corresponding algebraic operators. In Proceedings
of WWW03 (The Twelfth International World Wide
Web Conference). Academic Press.
326
Students as Customers
Cristea, A. I., & Ghali, F. (2011). Towards adapta-
tion in e-learning 2.0. New Review of Hypermedia
and Multimedia, 17(2), 199–238. doi:10.1080/13
614568.2010.541289
Cronbach, L. J., & Shavelson, R. J. (2004).
My current thoughts on coefficient alpha and
successor procedures. Educational and Psy-
chological Measurement, 64(3), 391–418.
doi:10.1177/0013164404266386
Dabbagh, N., & Kitsantas, A. (2012). Personal
Learning Environments, social media, and self-
regulated learning: A natural formula for connect-
ing formal and informal learning. The Internet
and Higher Education, 15(1), 3–8. doi:10.1016/j.
iheduc.2011.06.002
De Bra, P., Aerts, A., Berden, B., De Lange, B.,
Rousseau, B., & Santic, T. etal. (2003, August).
AHA! The adaptive hypermedia architecture. In
Proceedings of the Fourteenth ACM Conference
on Hypertext and Hypermedia(pp. 81-84). ACM.
doi:10.1145/900051.900068
De Bra, P., Smits, D., van der Sluijs, K., Cristea,
A. I., Foss, J., Glahn, C., & Steiner, C. M. (2013).
GRAPPLE: Learning management systems meet
adaptive learning environments. In Intelligent
and Adaptive Educational-Learning Systems (pp.
133-160). Springer Berlin Heidelberg.
Deterding, S., Dixon, D., Khaled, R., & Nacke,
L. (2011, September). From game design
elements to gamefulness: defining gamifica-
tion. In Proceedings of the 15th International
Academic MindTrek Conference: Envisioning
Future Media Environments (pp. 9-15). ACM.
doi:10.1145/2181037.2181040
Dieberger, A., Dourish, P., Höök, K., Resnick,
P., & Wexelblat, A. (2000). Social navigation:
Techniques for building more usable systems.
Interactions, 7(6), 36-45.
Domínguez, A., Saenz-de-Navarrete, J., De-
Marcos, L., Fernández-Sanz, L., Pagés, C.,
& Martínez-Herráiz, J. J. (2013). Gamifying
learning experiences: Practical implications and
outcomes. Computers & Education, 63, 380–392.
doi:10.1016/j.compedu.2012.12.020
El Bachari, E., Abelwahed, E. H., & El Adnani, M.
(2010). Design of an Adaptive E-Learning Model
Based on Learner’s Personality. Ubiquitous Com-
puting and Communication Journal, 5(3), 27–36.
Everfi. (2013). Young Teens and the Digital World.
Retrieved from http://visual.ly/young-teens-and-
digital-world
Facebook Newsroom. (2013). Retrieved
from http://investor.fb.com/releasedetail.
cfm?ReleaseID=802760
Ghali, F., & Cristea, A. I. (2009a). MOT 2.0: A
Case Study on the Usefuleness of Social Modeling
for Personalized E-Learning Systems. In Proceed-
ings of AIED (pp. 333-340). AIED.
Ghali, F., & Cristea, A. I. (2009b). Social reference
model for adaptive web learning. In Proceedings
of Advances in Web Based Learning–ICWL
2009 (pp. 162–171). Springer Berlin Heidelberg.
doi:10.1007/978-3-642-03426-8_21
Ghali, F., & Cristea, A. I. (2009c). Authoring for
e-learning 2.0: A case study. In Proceedings of
Advanced Learning Technologies, (pp. 170-174).
IEEE.
Hsiao, I. H., Bakalov, F., Brusilovsky, P., & König-
Ries, B. (2013). Progressor: Social navigation sup-
port through open social student modeling. New
Review of Hypermedia and Multimedia, 19(2),
112–131. doi:10.1080/13614568.2013.806960
Kim, B., Park, H., & Baek, Y. (2009). Not just
fun, but serious strategies: Using meta-cognitive
strategies in game-based learning. Computers
& Education, 52(4), 800–810. doi:10.1016/j.
compedu.2008.12.004
327
Students as Customers
Knutov, E. (2008, January). Gaf: Generic adap-
tation framework. In Proceedings of Adaptive
Hypermedia and Adaptive Web-Based Systems
(pp. 400–404). Springer Berlin Heidelberg.
doi:10.1007/978-3-540-70987-9_62
Koch, N., & Wirsing, M. (2006, January). The
Munich reference model for adaptive hypermedia
applications. In Proceedings of Adaptive hy-
permedia and adaptive web-based systems (pp.
213–222). Springer Berlin Heidelberg.
Könings, K. D., Brand-Gruwel, S., & van Mer-
riënboer, J. J. (2010). An approach to participatory
instructional design in secondary education: An
exploratory study. Educational Research, 52(1),
45–59. doi:10.1080/00131881003588204
Krause, U. M., Stark, R., & Mandl, H. (2009).
The effects of cooperative learning and feedback
on e-learning in statistics. Learning and Instruc-
tion, 19(2), 158–170. doi:10.1016/j.learnin-
struc.2008.03.003
List of Wikipedias. (2013). Retrieved from http://
meta.wikimedia.org/wiki/List_of_Wikipedias
Lohnes, S., & Kinzer, C. (2007). Questioning
assumptions about students’ expectations for
technology in college classrooms. Academic Press.
Looi, C. K., Wong, L. H., So, H. J., Seow, P.,
Toh, Y., & Chen, W. etal. (2009). Anatomy of
a mobilized lesson: Learning <i>my way</i>.
Computers & Education, 53(4), 1120–1132.
doi:10.1016/j.compedu.2009.05.021
Magnisalis, I., Demetriadis, S., & Karakostas,
A. (2011). Adaptive and intelligent systems for
collaborative learning support: A review of the
field. IEEE Transactions on Learning Technolo-
gies, 4(1), 5–20.
March, W., Jacobs, M., & Salvador, T. (2005,
April). Designing technology for community ap-
propriation. In Proceedings of CHI’05 Extended
Abstracts on Human Factors in Computing Sys-
tems (pp. 2126-2127). ACM.
McLoughlin, C., & Lee, M. J. (2007, December).
Social software and participatory learning: Peda-
gogical choices with technology affordances in
the Web 2.0 era. In Proceedings of ICT: Providing
Choices for Learners and Learning (pp. 664-675).
Academic Press.
Meeker, M., & Wu, L. (2013). 2013 Internet
Trends. Retrieved from http://www.kpcb.com/
insights/2013-internet-trends
Mitrovic, A., & Martin, B. (2007). Evaluating the
effect of open student models on self-assessment.
International Journal of Artificial Intelligence in
Education, 17(2), 121–144.
Muller, M. J. (2003). Participatory design: The
third space in HCI. In Human-computer inter-
action: Development process, (pp. 165-185).
Academic Press.
Mulwa, C., Lawless, S., Sharp, M., Arnedillo-
Sanchez, I., & Wade, V. (2010, October). Adaptive
educational hypermedia systems in technology
enhanced learning: a literature review. In Pro-
ceedings of the 2010 ACM conference on Infor-
mation technology education (pp. 73-84). ACM.
doi:10.1145/1867651.1867672
Norman, D. A., & Draper, S. W. (1986). User
centered system design: New perspectives on
human-computer interaction. L. Erlbaum As-
sociates Inc.
Ozkan, S., & Koseler, R. (2009). Multi-dimen-
sional students’ evaluation of e-learning systems
in the higher education context: An empirical
investigation. Computers & Education, 53(4),
1285–1296. doi:10.1016/j.compedu.2009.06.011
328
Students as Customers
Rahamat, R., Shah, P. M., Puteh, S. N., Din, R.,
& Karim, A. A. (2011). End-Users’ involve-
ment in the development of web-based learning
resources for English Literature. The Southeast
Asian Journal of English Language Studies.,
17(special issue), 5–18.
Reja, U., Manfreda, K. L., Hlebec, V., & Vehovar,
V. (2003). Open-ended vs. close-ended questions
in web questionnaires. Advances in Methodology
and Statistics, 19, 159–177.
Roda, C. (2004). Participatory System Design as
a Tool for Learning. CELDA.
Schuler, D., & Namioka, A. (Eds.). (1993).
Participatory design: Principles and practices.
Routledge.
Seale, J. (2009). Doing student voice work
in higher education: An exploration of the
value of participatory methods. British Edu-
cational Research Journal, 36(6), 995–1015.
doi:10.1080/01411920903342038
Shi, L., Al Qudah, D., & Cristea, A. I. (2012a).
Apply the We! Design methodology in E-learning
2.0 system design: A pilot study. In Proceedings
of 2012 Imperial College Computing Student
Workshop (pp. 123-128). Academic Press.
Shi, L., Al Qudah, D., & Cristea, A. I. (2013h).
Social e-learning in topolor: A case study. In Pro-
ceedings of the 7th IADIS Conference e-Learning
(pp. 57-64). IADIS.
Shi, L., Al Qudah, D., Qaffas, A., & Cristea, A. I.
(2013c). Topolor: A social personalized adaptive
e-learning system. In User Modeling, Adaptation,
and Personalization (pp. 338-340). Springer Berlin
Heidelberg. doi:10.1007/978-3-642-38844-6_32
Shi, L., Al Qudah, D., Qaffas, A., & Cristea, A. I.
(2013e). To build light gamification upon social
interactions: requirement analysis for the next ver-
sion of Topolor. In Proceedings of the Sixth York
Doctoral Symposium on Computer Science and
Electronics. Department of Computer Science,
University of York.
Shi, L., Al-Qudah, D. A., & Cristea, A. I. (2012b)
Exploring Participatory Design for SNS-based
AEH Systems. In Proceedings of the 11th IADIS
International Conference WWW/INTERNET (pp.
242-249). IADIS.
Shi, L., Al-Qudah, D. A., & Cristea, A. I. (2013a)
Designing Social Personalized Adaptive E-Learn-
ing. In Proceedings of the 18th ACM Conference
on Innovation and Technology in Computer Sci-
ence Education. ACM.
Shi, L., Awan, M., & Cristea, A. I. (2013f) Evalu-
ating System Functionality in Social Personalized
Adaptive E-Learning Systems. In Proceedings
of the 8th European Conference on Technology
Enhanced Learning (pp. 633- 634). Academic
Press. doi:10.1007/978-3-642-40814-4_87
Shi, L., & Cristea, A. I. (2013j). Investigating
the impact of social interactions in adaptive
E-Learning by learning behaviour analysis. In
Proceedings of Sixth York Doctoral Symposium
on Computer Science and Electronics. Depart-
ment of Computer Science, University of York.
Shi, L., Cristea, A. I., Awan, M. S., Stewart, C.,
& Hendrix, M. (2013g). Towards Understanding
Learning Behavior Patterns in Social Adaptive
Personalized E-Learning Systems. In Proceedings
of the 19th Americas Conference on Information
Systems (pp. 1-10). Academic Press.
Shi, L., Cristea, A. I., Foss, J. G. K., Al-Qudah,
D. A., & Qaffas, A. (2013d). A Social Personal-
ized Adaptive E-Learning Environment: A Case
Study in Topolor. IADIS International Journal on
WWW/Internet, 11(3), 13-34.
329
Students as Customers
Shi, L., Gkotsis, G., Stepanian, K. A., Al Qudah,
D., & Cristea, A. I. (2013b). Social personal-
ized adaptive e-learning environment: Topolor-
implementation and evaluation. In Proceedings
of the 16th International Conference on Artificial
Intelligence in Education (pp. 708-711). Academic
Press. doi:10.1007/978-3-642-39112-5_94
Shi, L., Stepanyan, K., Al Qudah, D., & Cristea,
A. I. (2013i). Evaluation of Social Interaction
Features in Topolor-A Social Personalized
Adaptive E-Learning System. In Proceedings
of the 13th IEEE International Conference on
Advanced Learning Technologies (pp. 15-18).
IEEE. doi:10.1109/ICALT.2013.92
Shneiderman, B., & Ben, S. (2003). Designing The
User Interface: Strategies for Effective Human-
Computer Interaction, 4/e (New Edition). Pearson
Education India.
Šimko, M., Barla, M., & Bieliková, M. (2010).
ALEF: A framework for adaptive web-based learn-
ing 2.0. In Key Competencies in the Knowledge
Society (pp. 367-378). Springer Berlin Heidelberg.
Smits, D., & De Bra, P. (2011, June). GALE: a
highly extensible adaptive hypermedia engine.
In Proceedings of the 22nd ACM conference on
Hypertext and hypermedia (pp. 63-72). ACM.
doi:10.1145/1995966.1995978
Sommerville, I., & Sawyer, P. (1997). Require-
ments engineering: a good practice guide. John
Wiley & Sons, Inc.
Titcomb, J. (2013). Tablets forecast to overtake
PC sales at end of year. Retrieved from http://
www.telegraph.co.uk/finance/newsbysector/me-
diatechnologyandtelecoms/electronics/10305093/
Tablets-forecast-to-overtake-PC-sales-at-end-of-
year.html
Tosun, L. P. (2012). Motives for Facebook use
and expressing “true self” on the Internet. Com-
puters in Human Behavior, 28(4), 1510–1517.
doi:10.1016/j.chb.2012.03.018
Triantafyllakos, G. N., Palaigeorgiou, G. E., &
Tsoukalas, I. A. (2008). We! Design: A student-
centred participatory methodology for the design
of educational applications. British Journal of
Educational Technology, 39(1), 125–139.
Valtolina, S., Barricelli, B. R., Mesiti, M., &
Ribaudo, M. (2011). User-Centered Design of
E-Learning Tools for Users with Special Needs:
The VisualPedia Case Study. In Proceedings of
Workshop HCI@ Large-Educate to the new fron-
tiers of the Human-Machine Interaction (Held in
Conjunction with CHItaly 2011). ACM.
Van Der Sluijs, K., Hidders, J., Leonardi, E., &
Houben, G. J. (2009). Gal: A generic adaptation
language for describing adaptive hypermedia.
In Proceedings of 1st International Workshop
on Dynamic and Adaptive Hypertext: Generic
Frameworks, Approaches and Techniques (pp.
13-24). Academic Press.
Vink, P., Imada, A. S., & Zink, K. J. (2008). De-
fining stakeholder involvement in participatory
design processes. Applied Ergonomics, 39(4),
519–526. doi:10.1016/j.apergo.2008.02.009
PMID:18417096
Vygotskiĭ, L. L. S. (1978). Mind in society: The
development of higher psychological processes.
Harvard University Press.
Wenger, E. (2000). Communities of practice
and social learning systems. Organization, 7(2),
225–246. doi:10.1177/135050840072002
Wenger, E. (2009). A social theory of learning.
In Contemporary theories of learning: Learning
theorists... in their own words, (pp. 209-218).
Academic Press.
Werner, H. D. (1986). Cognitive theory. Social
Work Treatment, 246.
Wu, H. (2002). A reference architecture for adap-
tive hypermedia applications. (Doctoral disserta-
tion). Technische Universiteit Eindhoven.
330
Students as Customers
Zimmerman, B. J. (1989). A social cognitive
view of self-regulated academic learning. Jour-
nal of Educational Psychology, 81(3), 329–339.
doi:10.1037/0022-0663.81.3.329
ADDITIONAL READING
Alexander, B. (2006). Web 2.0: A new wave of
innovation for teaching and learning? EDUCAUSE
Review, 41(2), 32.
Björgvinsson, E., Ehn, P., & Hillgren, P. A. (2010,
November). Participatory design and democratiz-
ing innovation. In Proceedings of the 11th Biennial
Participatory Design Conference (pp. 41-50).
ACM. doi:10.1145/1900441.1900448
Brusilovsky, P. (2001). Adaptive hypermedia.
User Modeling and User-Adapted Interaction,
11(1-2), 87–110. doi:10.1023/A:1011143116306
Clark, R. C., & Mayer, R. E. (2011). E-learning and
the science of instruction: Proven guidelines for
consumers and designers of multimedia learning.
John Wiley & Sons. doi:10.1002/9781118255971
Cole, J., & Foster, H. (2007). Using Moodle:
Teaching with the popular open source course
management system. O’Reilly Media, Inc.
Cristea, A., Ghali, F., & Joy, M. (2011). Social,
personalized lifelong learning. E-Infrastructures
and Technologies for Lifelong Learning: Next
Generation Environments, 90-125.
Cristea, A. I., & Ghali, F. (2011). Towards adapta-
tion in e-learning 2.0. New Review of Hypermedia
and Multimedia, 17(2), 199–238. doi:10.1080/13
614568.2010.541289
Dix, A. (2009). [Springer US.]. Human-Computer
Interaction, 1327–1331.
Downes, S. (2005). Feature: E-learning 2.0. Elearn
magazine, 2005(10), 1.
Garrett, J. J. (2010). Elements of User Experi-
ence, The: User-Centered Design for the Web
and Beyond. Pearson Education.
Garrison, D. R. (2011). E-learning in the 21st
century: A framework for research and practice.
Taylor & Francis.
Hakkarainen, K., Lipponen, L., & Järvelä, S.
(2013, May). Epistemology of inquiry and com-
puter-supported collaborative learning. Computer-
Supported Collaborative Learning, 2, 129–156.
Pappano, L. (2012). The Year of the MOOC. The
New York Times, 2(12), 2012.
Schuler, D., & Namioka, A. (Eds.). (1993).
Participatory design: Principles and practices.
CRC Press.
Shen, X., Tan, B., & Zhai, C. (2005). Implicit user
modeling for personalized search. In Proceed-
ings of the 14th ACM international conference
on Information and knowledge management (pp.
824-831). ACM.
Shi, L., Awan, M. S. K., & Cristea, A. I. (2013).
Evaluation of social personalized adaptive E-
Learning environments: end-user point of view.
In Proceedings of the 3th Imperial College Com-
puting Student Workshop (pp.103-110). Schloss
Dagstuhl - Leibniz Center for Informatics.
Shi, L., & Cristea, A. I. (2014) Making it game-
like: Topolor 2 and Gamified Social E-Learning.
In Proceedings of the 22nd Conference on User
Modeling, Adaptation and Personalization.
Springer.
Shi, L., Cristea, A. I., & Hadzidedic, S. (2014)
Multifaceted Open Social Learner Modelling. In
proceedings of the 13th International Conference
on Web-based Learning. Springer.
331
Students as Customers
Shi, L., Cristea, A. I., Hadzidedic, S., & Dervisha-
lidovic, N. (2014) Building Light Gamification
upon Social Interaction – Towards Increasing
Motivation in Social E-Learning. In proceedings of
the 13th International Conference on Web-based
Learning. Springer.
Steichen, B., O’Connor, A., & Wade, V. (2011,
June). Personalisation in the wild: providing
personalisation across semantic, social and open-
web resources. In Proceedings of the 22nd ACM
conference on Hypertext and hypermedia (pp.
73-82). ACM. doi:10.1145/1995966.1995979
Wang, Q., Woo, H. L., Quek, C. L., Yang, Y., & Liu,
M. (2012). Using the Facebook group as a learn-
ing management system: An exploratory study.
British Journal of Educational Technology, 43(3),
428–438. doi:10.1111/j.1467-8535.2011.01195.x
KEY TERMS AND DEFINITIONS
Adaptive Educational Hypermedia System:
A system that applies adaptive hypermedia to the
domain of education. It tailors what the learner
sees to that learner’s goals, abilities, needs, inter-
ests, and knowledge of the subject, by providing
hyperlinks that are most relevant to the learner.
Adaptive e-Learning: The e-learning systems
that has adaptation features.
Adaptive Hypermedia: A disputed research
field where hypermedia is made adaptive accord-
ing to a user model. It tailors what the user sees
to a model of the user’s goals, preferences and
knowledge.
AEHS 2.0: The adaptive educational hyperme-
dia systems that have Web 2.0 and social features.
Participatory Design: An approach to design
attempting to actively involve all stakeholders
(e.g. employees, partners, customers, citizens,
end users) in the design process to help ensure
the result meets their needs and is usable.
Requirement Analysis: The tasks that go into
determining the needs or conditions to meet for
a new or altered product, taking account of the
possibly conflicting requirements of the various
stakeholders, analyzing, documenting, validating
and managing software or system requirements.
Social E-Learning: The e-learning systems
that has social features.
Web 2.0: The description of World Wide Web
sites that use technology beyond the static pages
of earlier Web sites (Web 1.0).
ENDNOTES
1 https://github.com/aslanshek/topolor
