G. Leitner, M. Hitz, and A. Holzinger (Eds.): USAB 2010, LNCS 6389, pp. 501–504, 2010.
© Springer-Verlag Berlin Heidelberg 2010
Semantic Mash-Up Personal and Pervasive Learning
Environments (SMupple)
Ahmet Soylu1, Fridolin Wild2, Felix Mödritscher3, and Patrick De Causmaecker1
1
K. U. Leuven, Department of Computer Science, CODeS, iTec, Kortrijk, Belgium
{Ahmet.Soylu,Patrick.DeCausmaecker}@kuleuven-kortrijk.be
2
The Open University, Knowledge Media Institute, Milton Keynes, United Kingdom
f.wild@open.ac.uk
3
Vienna University of Economics and Business, Department of Information Systems,
Vienna, Austria
felix.moedritscher@wu.ac.at
Abstract. Personal Learning Environments have emerged as a complementary,
even challenging, paradigm to Adaptive Learning Systems. We consider the
mash-up era as an appropriate approach for a successful realization of digital
personal learning environments. However, mash-ups are also accompanied by
critical technical and usability challenges. In this paper, we try to identify some
of these challenges and present our solution approach which results in Semantic
Mash-up Personal and Pervasive Learning Environments (SMupple).
Keywords: Personal Learning Environments, Mash-up, Ontologies, Embedded
Semantics, Workflows, Pervasive Computing.
1 Introduction
Adaptive Learning Systems (ALSs), in general, focuses on automatically, often intru-
sively, changing the system behavior, according to the learner’s needs and other char-
acteristics and aiming at adapting the learning material and its presentation. However,
it is already apparent that it is not possible to predefine adaptation rules for all differ-
ent usage contexts. Furthermore, Wild and his colleagues [1] claim that adaptation
technologies take away experiences from end-users (learners) thus prohibiting the
development of important competences. In this respect, Personal Learning Environ-
ments (PLEs) emerge as a complementary, even challenging, paradigm to the ALSs.
Wild et al [1] value learning environment as an important aspect of the learning proc-
ess and consider it as an output of learning rather than a mere input. Digital learning
environments can be composed of different applications, artifacts, and people etc. The
individual at the centre modifies this environment through interacting with it, intend-
ing to positively influence her social, self, methodological, and professional compe-
tences and to change her potentials for future action. In other words, a learner actively
or passively creates her own personal learning environment. In short, one can argue
that PLEs aim at replacing the physical learning environment while ALSs focus on
replacing the instructor.

502 A. Soylu et al.
Considering PLEs, learners acknowledge the abundance and variety of web appli-
cations, services and data sources to be used within their environments. Moreover,
different technological devices, like mobile phones, digital media solutions, tablet
PCs, intelligent household appliances, etc. are expected to be connected to the Web
and serve their functionalities through embedded web servers or gateways coupled
with the internal functions of available devices, possibly, with RESTful APIs [2]. This
leads us to extend PLE paradigm to Personal and Pervasive Learning. Here, mash-up
approaches enable users to design their ubiquitous and personal learning environ-
ments through combining functionalities and data available on the Web. However this
leads to some challenges. In this paper, we identify these important challenges and
present our solution approach, which builds on semantic technologies and, referring to
[1], is called Semantic Mash-up Personal and Pervasive Learning Environments
(SMupple).
2 Approach
We consider the mash-up paradigm to be crucial for realizing the PLE vision within
the infinite space of the Web. However, before moving forward with our approach,
we believe that a conceptual description of a personal learning environment and iden-
tification of basic requirements for a digital PLE shall be useful for situating impor-
tant challenges. On a conceptual level, a user (learning) environment can be seen as
space of entities, including people, artifacts, tools, learning objects etc., available to
the learner. Each of these entities is attached with several possible activities; addition-
ally composite activities and composite entities encompass several other entity-
activity pairs and entities respectively. In that space learners derive their personal
(sub-) environments, orchestrate member entities for their goals through maintaining
data and interaction flows between these entities, and continuously refine the PLEs as
a result of their activities and often through their own implicit formative assessment
methods. A PLE can be further partitioned into disjoint or overlapping clusters with
respect to varying goals of learners. Learners often shift their focus from one cluster
to another according to their current goals. From this perspective, a mash-up personal
and pervasive learning environment enables learners to construct their digital learning
environments spanning various digital web resources and web-enabled devices encap-
sulated through widget like constructs.
Considering mash-ups, they can be created at client-side (i.e., at browser) or at web
server-side. We identify two different types of mash-ups: (1) dashboard type (e.g.,
[1]), (2) box type (e.g., [3]). The former is usually created at the client-side where
different applications are placed to the learner browser as widgets (all visible). Data
and events can be moved from one widget to another one mainly through inter widget
communication on the client, occasionally also through server-sided synchronization
mechanisms. The latter mash-up type is usually created and provided by a server,
combining the different applications into one single user experience (only the result-
ing application is visible). Data and events can be moved from one application to
other application through server-sided synchronization mechanisms. The end product
can also be used for developing other mash-ups of both types.

Semantic Mash-Up Personal and Pervasive Learning Environments 503
With respect to above descriptions and by considering the existing implementa-
tions [4], we identified several challenges. These challenges and our approach is de-
scribed in three tiers which is partially depicted in Fig. 1.

Fig. 1. Presentation and comparison of different approaches along the three tiers
Seven challenges have been identified each mapping to at least one tier: (1) com-
position/integration (services, applications and data), (2) inter widget communication.
The first two challenges deal with data links between different applications, through
server-sided synchronization or inter widget communication based on syntactic
means, which is not sufficient for automated integration and composition of services
leaving a huge burden to the end-user. Accordingly, injecting semantics through

504 A. Soylu et al.
ontologies and embedded semantics technologies (e.g., microformats) may serve well
for automated linking (e.g., [3, 5]). (3) Workflow management: this challenge is re-
lated to typical mash-up composition and requires users to define full workflows thus
cognitively overloading the learner. Therefore, it is important to enable mash-up
composition on the basis of incomplete workflows automatically generated through
observing user interactions. (4) Environment awareness and control [6]: in physical
environments users manage a limited number of entities with a relatively high aware-
ness, however the Web offers an almost infinite amount of resources; therefore it is
crucial to maintain awareness and control of one’s space, so that the links between a
learner and the environment stay tight. (5) Ease of orchestration: since the learner is
confronted with more resources, learners should not experience a cognitive overload
while managing the space. (6) Engaging learner experience: learners should feel com-
fortable through their experiences with PLEs. Hence identification and amalgamation
of engaging and easy-to-use end-user design facilities and metaphors are required. (7)
Adaptive guidance and support: this challenge is necessitated from the fact that learn-
ing process and end-user design of the environments requires adequate machine sup-
port, in terms of non-invasive adaptations, and recommendations. At this point, a
formalized representation of the user’s context through an ontology is promising with
respect to “intelligent” guidance and end-user environment design. For the usability
concerns, we approach a new type of mash-ups, a “flow” (see Fig. 1). Unlike
dashboard type mash-ups, it tries to provide a reflection of the workflow among the
widgets and the clustered nature of the learning environment.
We have elaborated on a scripting language and a design environment for realizing
box like mash-ups addressing users ranging from experts to naïve. Our end-user tests,
particularly on the interface mockup, have revealed that the mash-up paradigm is
quite new, and hard to grasp for non-experts. Developing natural and easy-to-use
design environments stands as a main challenge. However, apart from appropriates of
design facilities, setting a smooth balance between machine and user control is re-
quired, so that users are not overloaded or not totally dominated by the machine. In
that sense, we believe that automated data linking and workflow creation, as well as
adaptive recommendations are more promising than strong, rule-based adaptation.
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