=Paper=
{{Paper
|id=None
|storemode=property
|title=Client-sided vs. Server-sided MUPPLEs: Can Add-ons Beat Platform-based Solutions?
|pdfUrl=https://ceur-ws.org/Vol-638/moedritscher_mupple10.pdf
|volume=Vol-638
}}
==Client-sided vs. Server-sided MUPPLEs: Can Add-ons Beat Platform-based Solutions?==
https://ceur-ws.org/Vol-638/moedritscher_mupple10.pdf
Client-sided vs. server-sided MUPPLEs: Can add-ons
beat platform-based solutions?
Felix Mödritscher1,
1
Institute for Information Systems and New Media,
Vienna University of Economics and Business,
Augasse 2-6, 1090 Vienna, Austria
felix.moedritscher@wu.ac.at
Abstract. Typically Mash-Up Personal Learning Environments (MUPPLEs)
are based on different theoretical foundations, which lead to different
development strategies and architectural styles. In this paper we examine the
influence of the two most opposite PLE architectures – server-sided and client-
sided solutions – towards relevant social and technical aspects. All in all, we
come to the conclusion that the decision for a specific architecture mainly
depends on very few factors – precisely collaboration, privacy and trust,
computational efforts and scalability, as well as software development and
maintenance – and that some shortcomings can be addressed by additional
technical concepts, like a practice sharing infrastructure in the back-end or the
utilization of existing web-based tools to achieve specific functionality.
Keywords: Personal Learning Environments, Architectural Styles, Social
Aspects, Technical Factors, Technology Review.
1 Introduction
About two years ago, Wild et al. (2008) introduced the concept of Mash-Up Personal
Learning Environments (MUPPLEs), extending the character of Personal Learning
Environments (PLEs) by aspects of End-User Development (Lieberman et al., 2006)
and Opportunistic Design (Hartmann et al., 2008). Precisely, a MUPPLE comprises a
PLE solution which empowers learners to design and use their environments by
utilizing existing tools so that they can connect to learner networks and collaborate on
shared artifacts. In this sense, a MUPPLE infrastructure enables users to mash up
existing functionality, i.e. in the form of web applications and widgets, to an
environment which is useful for them to achieve personal goals and learning needs.
Although being a young concept, various software systems and tools can be
classified as MUPPLE solutions, or include at least few aspects of a Mash-Up
Personal Learning Environment. Amongst others, Palmer et al. (2009) give an
overview of different dimensions for building web-based PLEs, reaching from UI-
related issues over data interoperability and social dimensions to runtime behavior of
PLEs. Furthermore Palmer and his colleagues evaluate various platforms according to
these characteristics. However, each of the PLE software reviewed is realized in the
form of a server-sided platform (e.g. iGoogle, Netvibes, Moodle/Wookie, Google
Wave, etc.), and also the dimensions for building web PLEs do not consider other
architectural styles than the (web) server-client architecture.
From a more technical perspective, MacNeill and Kraan (2010) elaborate different
architectural styles of learning environments and summarize five particular degrees of
distribution: (1) one system in the cloud, many outlets; (2) plug-ins to existing virtual
learning environments (VLEs); (3) many widgets from the web into one widget
container; (4) many providers and many clients; (5) both a provider and a client at the
same time. From our point of view, a MUPPLE solution is closely related to the last
type of a distributed learning environment, as it contains all tools and devices a
learner requires for her everyday activities. In any way, this architectural style does
not specify if the learning environment should be realized in the form of a server-
sided system or a software tool being executed on the computer of a learner.
In this paper, we compare server-sided MUPPLE platforms to client-sided
solutions which have been emerged in the last year – for instance we consider our
own prototype, the so-called ‘Personal Activity Manager’ (PAcMan, see
https://addons.mozilla.org/en-US/firefox/addon/176479/) as a client-sided PLE. The
comparison of these two architectural MUPPLE styles will be conducted according to
different social and technical aspects which are relevant for personal learning
environments. Therefore, the following two sections elaborate advantages and
disadvantages of server and client-sided MUPPLE technology along social and
technical issues. In section 4 we summarize the findings of this comparison and
determine which architectural style is better for a MUPPLE solution.
2 Social aspects of MUPPLE architectures
Personal learning environments aim at supporting learners in utilizing different tools
in order to participate in collaborative activities of learning communities.
Consequently the architecture of a MUPPLE does have an influence on various social
aspects, as elaborated in the following.
2.1 Collaborative activities
Van Harmelen (2006) states that a PLE (and therefore also a MUPPLE) aims at
empowering learners to design their learning environments in order to connect to
learner networks and collaborate on shared artifacts to achieve common goals.
Therefore facilities for PLE-based collaboration are considered to be important. As
evidenced by many social networking platforms (like Facebook, MySpace, XING,
etc.) and Web 2.0 tools (e.g. Wikipedia, Delicious, Flickr, etc.), server-sided web
solutions perfectly enable collaboration between users. However, if understanding a
PLE as an infrastructure to find and manage these (web-based) applications also
client-sided MUPPLEs are fit enough to foster collaboration amongst learners. The
main advantage of a server-sided to a client-sided architecture deals with core
functionalities of a PLE, as will be explained in the upcoming subsection.
2.2 Sharing and reusing PLE experiences
Mödritscher and Wild (2009) report on practice sharing in PLE settings, outlining the
importance of facilities to capture, share, retrieve, and re-use learners’ experiences on
utilizing (web-based) tools for their different (learning) activities. Such facilities can
be built upon data mining mechanisms based on log files, learner interaction
recordings, contextual attention metadata, and so forth. In the sense of educational
mining (Romero & Ventura, 2006), the semantics extracted from this data can be
exploited e.g. for recommending artifacts or tools for a specific situation, finding
appropriate peers, visualizing knowledge and important relations, providing
navigational aids, etc. In this context, Mödritscher et al. (2010) introduces the concept
of a pattern repository for practice sharing which can be plugged into PLE solutions.
By enabling learners to share patterns of their activities, learning experiences can be
made public and re-used by others. The usage of such an activity pattern can trigger
competence development if it fits a learner’s situation and is new to her.
Similar to Web 2.0 platforms and approaches (like Facebook, Google, etc.), server-
sided MUPPLEs have clear advantages for practice sharing to client-sided solutions.
On the other hand, the pattern repository described by Mödritscher et al. (2010) is
independent of the architecture, as it is integrated into the PLE via a web-based API.
Such a repository could be hosted by different parties, e.g. a university offering
alumni learning services, a company trying to foster internal practice sharing, a
private organization supporting their members, etc. Using such a back-end
infrastructure for sharing good PLE practices would equalize the benefits of server
and client-sided MUPPLEs.
2.3 Trust
With respect to social networking sites (cf. Dwyer et al., 2007) a first issue to mention
for PLEs is trust. Trust can be understood as “the willingness of a party to be
vulnerable to the actions of another party based on the expectation that the other will
perform a particular action important to the trustor, irrespective of the ability to
monitor or control that other party” (Mayer et al., 1995). In connection with software,
trust is always closely related to the trustworthiness of the software vendor or
provider. Thus aspect is relevant for the PLE solution as well as for each tool – web
or desktop application! – being part of the learning environment.
However, as PLEs capture critical user data in the form of recordings of learner
interactions, trust also depends on the control of a user over this data. Here, users
might feel more comfortable if the interaction recordings remain on their computers
and can be shared at their request. Independently of the architectural style of a PLE,
Dwyer et al. (2007) state that only a trust in the solution provider facilitates
information sharing. This issues effects both client and server-sided PLEs.
Concerning trust in general, we see slight advantages for client-sided PLE solutions.
2.4 Privacy
Similar to trust, we consider privacy a critical aspect in the scope of Mash-Up
Personal Learning Environments. Privacy is defined as “the interest that individuals
have in sustaining a ‘personal space’, free from interferences by other people and
organizations” (Clarke, 2006). The tools as well as digital recordings of learner
interactions are part of the personal space and should be secured to preserve the
learner’s privacy, which is particularly necessary for open(-content) systems (García-
Barrios, 2009). In the scope of PLEs it is important that shared information does not
expose sensitive user data and that learners can determine what they want to expose to
others, e.g. through adaptable privacy statements. Furthermore García-Barrios (2009)
proposes an approach to create privacy awareness by visualizing the effects of privacy
statements on user data as well as their benefits within the tools available.
Again, the privacy of software in general depends on the reliability of the vendor
or provider, or the community behind it. Addressing the risk of data leakage, server-
sided platforms are more vulnerable to unauthorized access on critical data, as they
are public targets. Client-sided PLEs, on the other hand, are not that exposed and
allow users to secure their data through applying security software (see subsection
3.1). User-adaptable privacy statements are considered to be a feature of the PLE
solution thus not dependent on the architectural style.
Privacy awareness concepts are realizable in the form of warnings and automated
anonymization techniques on sharing PLE-based interaction recordings. Platform
solutions have the advantage that they can mine all user data and include a
visualization approach as mentioned above. Such a crowd-sourcing approach can be
realized through a back-end repository for publishing and retrieving PLE practices
(cf. Mödritscher et al., 2010) but requires more efforts and might not take all
interactions into consideration. Therefore we see server and client-sided PLEs in
balance here.
3 Technical aspects of MUPPLE architectures
Beside these social aspects, the architectural style of a Mash-Up Personal Learning
Environment also has an impact on various technical factors. In the following
subsections we briefly sketch these aspects and try to order them according to their
importance for realizing MUPPLE solutions.
3.1 Computational efforts and scalability
A first and highly relevant factor to mention is the processing effort for providing all
functionality of the personal learning environment. Following the hard facts of large-
scale Web 2.0 platforms, a wide-spread PLE solution would require a lot of
computational power, as evidenced with the distributed calculation of the Google
index (Barroso et al., 2003) or with the many specialized, high-scalable technologies
and servers behind Facebook (Royal Pingdom, 2010). Due to the fact that Mash-Up
PLEs aim at integrating existing tools into one’s learning environment, a lot of
computational effort is outsourced to the servers providing these web applications.
For the remaining PLE infrastructure, a server-sided architecture has clear
disadvantages to client-sided MUPPLEs if a Facebook like platform is being targeted.
Although computational resources could be used on the client computers (e.g. through
JavaScript and Ajax) the server would have to deal with a high computational load
and a lot of communication traffic. Parts of these efforts could be shifted to other
servers, as we have shown for instance with the practice sharing infrastructure (cf.
Mödritscher et al., 2010) but a distributed server architecture is required in any way.
On the other hand, a clients-sided MUPPLE would have to serve only one user who
can directly work with web applications or make use of back-end services in order to
access a sharing infrastructure. Collaboration with peer actors can be fully achieved
with the different tools available to and used by the learners. All kinds of computation
are performed on the client machine, having a client-sided PLE approach the more
scalable solution.
3.2 Software development and maintenance
A second critical issue of PLEs is the one on rolling out and maintaining such
solutions. From the perspective of a developer and provider of a PLE product, a
server-sided architecture has clear benefits in providing access to a web-based
platform and maintaining this centralized system. As mentioned in the last subsection,
scalability might require deploying a server cluster in the back-end, which would
increase the efforts in maintenance. For accessibility reasons a MUPPLE platform
should be also compliant with the mostly used browsers, which however can be
achieved by using mature, wide-spread Ajax libraries. Generally server-sided PLE
solutions can be easily rolled out within a community or organization, while new
features and patches can be installed quickly and at a central point.
On the other hand, client-sided PLEs are restricted to a user’s computer, e.g. to an
operating system, a browser or even a certain version of a browser. Thus, the
complexity of the software developed tends to be low while large-scaled roll-outs can
be problematic if users underlie technical restrictions on their computers e.g. due to an
organizational ICT policy. For this situation it would be possible to provide pre-
configured installation packages or runnable versions e.g. on a memory stick.
Maintenance of client-sided PLEs is easily achievable through automated update
mechanisms, whereby the updates normally have to be triggered by the users.
Consequently installing time-critical patches is problematic for client-sided
approaches. Concerning roll-outs and maintenance we see clear disadvantages of
client-sided PLE solutions, which we have experienced with the PAcMan prototype
realized as a Firefox add-on.
3.3 Security
Thirdly, security plays a role for personal learning environments in general. In the
scope of MUPPLEs and this paper, we mainly refer to the low-level concepts of
security, namely to computer security and vulnerability to hackers (cf. Landwehr,
2001). Computer security aims at mechanisms to protect the PLE-related data of
users, e.g. through authentication, data encryption, etc. Vulnerability describes the
weaknesses of a computer system or software which allow attackers accessing or
duplicating data of PLE users.
Computer and particularly data security is necessary for both kinds of PLE
architectures, i.e. a user’s PLE-related data should be protected from unauthorized
access over a web-based interface or to the physical device (a client computer or a
server machine). Client computers can be stolen more easily and might not be
protected as good as server systems. In case of data abuse however a vulnerability of
a server-sided MUPPLE would concern sensitive data of many PLE users. Moreover
servers often provide specific services which increase the vulnerability to hackers.
Finally server-sided PLEs are also more vulnerable to attacks on the communications
between the server and the client computers, i.e. hackers could also exploit security
leaks of a browser to break into the server machine. Overall, we consider client-sided
MUPPLEs as more secure than server-based platforms.
3.4 Availability
Availability is defined as the degree to which a MUPPLE is operable and accessible
by the end users. Hereby it has to be distinguished between the availability of the
web-based tools and the PLE infrastructure integrating the tools and providing users a
unified access to their learning environments. The availability of the tools has to be
guaranteed by the providers responsible for them, thus it is not relevant for a PLE in
general. The availability of a PLE infrastructure is highly related to its architecture.
While client-sided MUPPLEs are available even if the computer is not connected to
the Internet server-based solutions always require a connection to the server hosting
the PLE platform. Consequently, client-sided MUPPLEs can be used for offline
activities (e.g. designing one’s activities and the corresponding environments), as long
as no access to one of the web-based tools or background services is required.
3.5 Responsiveness and usability
The responsiveness of a MUPPLE describes how quickly the environment responds to
user input. Similar to the availability of a PLE we have to differentiate between the
tools being part of the learning environment and the infrastructure. The
responsiveness of tools is fully carried by the tool providers and relies on issues like
the connection to the hosting server or the usability of the applications itself. The
responsiveness of the PLE infrastructure however is influenced by the architectural
design. As client-sided solutions do not require continuous communication with a
server, they are a clear favorite regarding this factor. Particularly they do not depend
on the quality of the Internet connection like MUPPLE platforms.
Usability of a PLE solution highly depends on the responsiveness but can also
consider aspects like single-sign on. It is obvious that a PLE solution – independently
of the architectural style – will be used less if users have to authenticate for every tool
being part of an environment separately. Here, platform-based MUPPLE require some
server-sided mechanism, like OpenID or Shibboleth, to overcome this problem while
clients, i.e. browsers, support users by storing authentication data locally.
Consequently, working with a client-sided PLE is more comfortable if users are
(semi-)automatically logged into the tools they need within their activities.
3.6 Analyzability
According to Mödritscher (2009), an important aspect of PLEs – and semantic
technologies in general – deals with analyzing user data and extracting semantics to
be utilized for supporting end-users such as lifelong learners within their
communities. Therefore it is necessary to capture and bring together the user data
which can either be entered explicitly or recorded while interacting with the
environment. The tools integrated into one’s PLE are normally recording their own
log files. At best, one can analyze them through a cross-platform approach, as seen
with the PALADIN approach (Klamma et al., 2008).
Server-sided MUPPLEs have the advantage that they capture and store the
interaction recordings at a central point, so this data can be analyzed immediately.
Client-sided PLE solutions lead to recordings distributed over many client computers.
Therefore they always require a back-end service (like the pattern repository
mentioned in section 2) for collecting the data from different users, e.g. all
participants in one activity. As a consequence, server-sided PLE platforms fulfill the
requirements on analyzability of user data, while client-sided MUPPLEs lack
facilities to bring together the interaction recordings.
3.7 Visualization and other user interface issues
Concerning usability and visual feedback mechanisms, a PLE solution should enable
the application of visualization techniques and comfortable user interface features,
like drag&drop functionality, interactive UI elements, etc. Here, client-sided
MUPPLEs have certain advantages. For instance browsers provide possibilities to
integrate libraries and executables, e.g. to realize fancy visualization and navigation
utilities like the Cooliris add-on (cf. http://www.cooliris.com). Moreover Mozilla’s
XML User Interface Language (XUL, see https://developer.mozilla.org/en/xul)
specification allows developers to modify and adapt all existing UI elements of the
browser and add state-of-the-art facilities, which we have done within our PAcMan
add-on (available at https://addons.mozilla.org/en-US/firefox/addon/176479/).
Server-sided PLE platforms rely on technologies for creating dynamic web
interfaces (HTML and JavaScript, Java Applets, Flash, Silverlight, etc.) or, better, on
respective libraries, e.g. Ajax frameworks, Flash libraries, and so forth. The
consideration of local programs (libraries or executables) is not possible at all. Overall
both architectural styles support the provision of usable UI elements and graphical
information. The realization is expensive if not utilizing good libraries, however
client-sided have slight advantages to MUPPLE platforms concerning this factor.
3.8 Scrutability
A key characteristic in connection with personalization systems is scrutability
(Holden & Kay, 1999). As system developers determine “what can be modeled, how
it is represented, how users can contribute to it and how it can evolve” (Kay, 2000), it
is important that users can understand how an adaptive system works and how the
user can influence the adaptation process. Thus, user models have to be scrutable in
order to guarantee controllability of a personalization system. In MUPPLEs user
profiles – referring to García-Barrios (2008) the simplest form of a user model – can
be explicitly found within the tools used as a part of the environment.
Certain PLE solutions – such as the MUPPLE.org prototype, a widget-enabled
LMS like Moodle, pure widget engines like Wookie or iGoogle, etc. – provide user
profile features as part of their core functionality. The higher-level aspects of user
models however are considered as a hidden component implicitly contained in the
user interactions with the environment (Mödritscher et al., 2010) and externalized in
the form of interaction recordings for one of the learner’s activities. Addressing the
scrutability of a learning environment, the architectural style seems to have no impact
on this aspect at first sight. Having a closer look a valuable feedback on the behavior
of one’s personal learning environment depends on the factors examined in the last
two subsections, analyzability and visualization.
4 Overview, findings and concluding remarks
In Table 1 we have ranked the issues examined in the former sections according to
their advantages and disadvantages for each of the two architectural styles. Therefore,
we have assigned the most critical aspects to the two PLE architectures in a first step.
Then, we have ordered the other issues according to their dependency on the critical
ones, which is described in the following.
Table 1. Overview of PLE-related aspects, ranked by their relevance for an architectural style.
Social perspective Technical perspective
Server-sided Collaboration (2.1) Analyzability (3.6)
MUPPLEs Software development/maintenance (3.2)
↑ Sharing/reusing PLE Scrutability (3.8)
⏐ experiences (2.2) Visualization/user interface (3.7)
⏐ Security (3.3)
↓ Trust (2.3) Availability (3.6)
Client-sided Responsiveness/usability (3.5)
MUPPLEs Privacy (2.4) Computation/scalability (3.1)
From the perspective of social factors, server-sided MUPPLE architectures are
better in terms of collaboration and practice sharing while client-sided solutions have
benefits concerning privacy and trust. As collaboration is an important driver of
learning and privacy is regulated by law, we weighted these two issues stronger. On a
technical level, platforms are ideal for analyzing user data, for rolling out and
maintaining the software, and for explaining adaptation effects to end-users (scrutable
system behavior). As argued in the former sections, client-sided MUPPLEs have
advantages concerning the distribution of computational efforts, their availability,
responsiveness and usability, as well as security and user interface issues.
To refine the ranking we use the similarity and dependency relations between some
of these PLE-related issues. For instance, scrutability and providing visual feedback
rely on the possibility to analyze user data and visualize valuable semantics extracted
from the interaction recordings, e.g. a collaboration graph of an activity or one’s
immediate learner network. Furthermore availability and responsiveness are very
similar, both assumed to be good if no remote connection is required. Additionally,
privacy and trust are related to security, as they focus on user data. On the other hand,
these three factors are in opposition to analyzability and data mining.
All in all, we think that the decision for a certain architectural style mainly depends
on the primary criteria(s) of a MUPPLE solution. Concluding this paper we have
identified two important, opposing combinations of (social and technical) aspects: (1)
collaboration and analyzability vs. privacy, trust and security, (2) computational
efforts and scalability vs. efforts in software development, roll-outs and maintenance.
These main challenges might also explain why community-enabling software
primarily follows one of the two well-known streams: (a) large-scale platforms like
Facebook, MySpace, XING, Google, Twitter, etc. or (b) highly specialized, useful
tools realized in the form of add-ons and client-sided programs (e.g. Skype, ICQ,
Cooliris, Shareaholic, PAcMan, or any of the 125 million add-ons available at
http://addons.mozilla.org/de/firefox/ on August 16, 2010).
Considering the success of platform-based community approaches (like Facebook)
it seems that MUPPLE platforms will be the future – however this assumption might
fail for PLEs, as privacy and trust are important factors if dealing with sensitive data
such as learner interaction recordings or the artifacts produced in (lifelong) learning
activities. So far we are more convinced of client-sided PLE solutions which can
overcome various shortcomings (analyzability, practice sharing or collaboration) by
the application of adequate tools in one’s environment and by specialized back-end
services like the pattern repository.
Acknowledgement
The research leading to these results has partially received funding from the European
Community's Seventh Framework Programme (FP7/2007-2013) under grant
agreement no 231396 (ROLE project).
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