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\title{PEER PROFILE DRIVEN GROUP SUPPORT FOR MOBILE LEARNING TEAMS}

\author{
Karin Anna Hummel\footnote{Address of corresponding author, 
karin.hummel@univie.ac.at}, 
Robert Kopecny, 
Gabriele Kotsis}


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\noindent {\it Abstract } \\
{\it The requirements on infrastructures for digital learning are changing as learners become
more and more mobile. Ad hoc interactions, dynamically changing learning teams
and communication and collaboration centered learning have to be considered. In
contrast to traditional digital learning environments focused on simulation,
content representation and distance learning support, we focuse on collaborative
learning. Using the notion of a group as a structuring element and building
group hierarchies, mobile learners can be assigned to groups based
on similar interests and skills, projects or location. To express the
learners' attributes, peer profiles are used. Based on the peer to peer
technology JXTA, a prototype capable to support spontaneous collaboration of
learning teams is implemented. Finally, we demonstrate the
advantages of hierarchical grouping in mobile learning environments in an
example lecture.
\\             
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}



\section{Introduction}
During the past decade, digital learning approaches have changed significantly.
Starting with computer based training, the use of simulators to explain complex
system behavior and automatic testing facilities, nowadays learning platforms
are broadly discussed. A variety of learning media and content representation
covers various dimensions of education. To enhance lectures, Web-based learning
platforms provide content delivery, organizational information and communication
from anywhere using devices running a Web browser. Furthermore, personalized
meta-learning information covering for example information about the learner's
status and feedback facilities can be accessed \cite{HuHl03}, \cite{WebCT},
\cite{Black}.

In contrast to approaches which address digital learning from the
perspective of educational theory \cite{UlAuDiRaHo02}, some
improvements are technology driven and focus on enhancing classrooms 
and the whole University campus with
wireless networks, like W-LAN IEEE 802.11b. Learners and students are expected
to access the information networks, mainly the Internet, using mobile devices,
like Personal Digital Assistats (PDAs). Accessing content using mobile devices
will not change the content or the pedagogical techniques, but the
representation will have to change based on the capabilities of the mobile
devices.


Going beyond information access using mobile devices, mobile learning offers a
new aspect of location and situation aware learning \cite{nyiri02},
\cite{shovet01}. In addition to traditional teacher-student scenarios or long
term self-studies, new learning styles based on communication centered learning
and changing learning context will emerge \cite{SaSaVuKy01}. As a consequence, short interactions
and spontaneously formed groups of interest capable of exchanging data without
infrastructure support (e.g. access to servers in the Internet) have to be
supported \cite{HuHl03}.

Focusing on the new learning styles, collaborative learning support is a key
research area \cite{Di99}. Forming and 
finding teams of similar interest or identyfing other
learners near by, for example while preparing for the same exam, is required.
Besides materialistic motivation, which is based on improvement of living
factors like money or position, and intellectual motivation focusing on the
challenging subject itself, collaborative group support potentially addresses
social motivation. Learning teams may be a place for cooperation or
comptetition. Furthermore, teams can compete or cooperate with other teams, for
example restricting access to shared files.

The paper is structured as follows: First, we introduce the notion of
groups for digital learning and show how the dimensions of collaborative group
support can be described using hieararchically organized groups. Furthermore, we
will motivate and explain the use of peer to peer technology for spontaneous
group support. Then, we describe the design and the implementation of a JXTA
based peer to peer prototype. We show in detail, how meaningful interaction
between learners can be achieved in a selected example and we conclude with
future perspectives.

\section{Peer Profiles for Mobile Learning Groups}

In former and recent research work, social awareness and collaborative learning are
seen as a major aspect of motivation in learning environments. Approving this approach, we aim at improvements of collaboration
in digital learning environments. The dimensions of improvement are: to achieve
a supporting lookup for findinging other learners near by or with similar
interests, the support of emerging, spontaneaous and student-driven learning
structures and finding new media for motivation. To cover these aspects, we
state that the notion of a group together with building hierarchies leads to a
sufficient model. The formation of groups is based on the learners' profiles
(\emph{peer profiles}) which includes context information, like location,
meeting or lecture attendance and affiliation. The concept of profiles can also
be extended in the future including information on domain and
user knowledge and using this information as the basis for 
group formation.

\subsection{Achieving Collaborative Learning based on Grouping}

\subsubsection{Motivation}

Assuming social structures and collaboration
to be a driving force for learning motivation, demands for building and
supporting learning groups with similar interests appear naturally. Using a
model of hierarchical groups to grant or restrict access to the group's
ressources, leads to implicitly structuring the collaboration process. As a
consequence, information distribution (like data sharing following a
download-judge-forward pattern as described in \cite{nyiri02}) and social
motivation based on cooperation and competition among the group members is
achieved.

\subsubsection{Hierarchical Groups}

Figure \ref{fig:peer-grouping} shows the categories of a peer profile
describing a person. While personal information, information about the
affiliation, the interest and the skills are rather static information, the
current project the student attends, the learning meeting and lecture
information (i.e. the information about any kind of congress, coming to-gether
focused on learning) and location information are expected to change dynamically
in case of roaming learners. Using the peer information for grouping allows to
model the collaborative learning environment as a hierarchy of groups. A peer
may be assigned to several groups and subgroups. It makes sense to assign a peer
to all of its preceeding groups as well.

\begin{center}
\begin{figure}[htb] \epsfig{figure=peer-grouping.eps, width=12cm}
\caption{Peer Profile Driven Hierarchical Groups \label{fig:peer-grouping}}
\end{figure} 
\end{center}

According to the profile entry values, groups are
formed naturally on the basis of similarities. For example, peers attending
the same lecture or being in the same room will form a group). Introducing
hierarchies allows to arrange grouping according to a finer granularity and to
restrict the access to ressources. Thus, introducing skill driven quality levels
is easily achieved and might be used to motivate the learners. For example, a
group hierarchy for a laboratory lecture on client/server programming using
JAVA/RMI may lead to a group of peers capable of Java programming with a
sub-group of peers being real experts on Java programming.

It is expected that pro-active and automatic grouping by the collaborative
learning system is not sufficient for covering all possible meaningful groups
and hierachies. Furthermore, preference on the hierarchical ordering have to
be introduced. When thinking about combinations of properties, like a group
consisting of all Java experts near by, both location information and skills
have to be considered. Allowing the learning peers to build groups and
supporting the discovery of fitting peers for the new groups is necessary.

\subsection{Demanding Issues in Ad-hoc Interaction, Motivation and
Reliabilty}

Being focused on roaming and highly mobile learners, groups and group
members currently joining will change frequently. New learning peers will appear
and search for other peers and peer groups using discovery and advertisement
techniques. Typical mobile learning situations are described by short
interactions with the learning environment, lack of bandwidth and usually lack
of concentration \cite{HuHl03}. Making advanced use of the physical
surroundings, like smart rooms or identifying learners near by to start
a collaboration, are major benefits of the mobile learning pattern. In our
model, these aspects are addressed by peer profile entries related to context
awareness.

When introducing digital media support, learners often criticize the lack of
personal contact to the teacher or other learners. On the other
hand, new motivating factors may be introduced using cooperating and competiting
groups. To give an example, groups may be used to divide learners in experts,
mediocre learners or novices in a selected area. To become an expert may become
a major motivating factor for a novice. In our model, peer profile based
grouping can be used for introducing motivating factors. Therefore, e.g. skills
are divided into the qualitative categories: perfect, good, basic.

Furthermore, supporting mobile learners assigned to groups will lead to
dynamically changing assignments and changes in the group hierarchy. In case
additional group creation and joining is allowed, branches with no peers
assigned any more needs garbage collection techniques and recovery strategies to
support reliable collaboration. Covering these aspects will foster a dependable
digital learning environment, but is not part of our basic model.



\section{Conclusions and Outlook}

Mobile persons will become mobile learners quite naturally. The major challenges
of digital learning in mobile environments are subjects addressed by ambient
computing research, like location and context awareness and ubiquitous
connectivity. In addition, learning styles will change and require new services
supporting the identification of other learners with similar interests or near
by, short interactions with other learners or the environment and sharing of
data.

Focusing on collaborative learning, we argue the benefits when using
hierarchical nested groups for emerging learning communities based on interest
or context information. The values describing a person's interest or context is
described using learning peer profiles. Grouping is based on similarities in
the peer profile information. In addition, to allow more complex combinations
for grouping, learners are allowed to create and join self-determined
groups. As a consequence, also social group behavior may be modeled by group
hierachies and policies.

Corresponding to the demands of spontaneous grouping, a prototypical
implementation using the peer to peer technology JXTA is described,
which uses the notion of groups to control the access and interactions.
Mobile learning teams are supported with basic grouping and subgrouping
functions. Furthermore, a content manager for sharing and downloading of data,
and a chat application for synchronous communication are implemented.

Finally, an example is presented applying the model to a University course. We
plan to use the prototype for a course to be held in summer term 2003
at the University of Vienna. The experiences gained in using the prototype
will help us in improving the system. In particular, we want to study
the usability of the system as well as the performance and scalability of the
proposed approach. The empirical evaluation of the prototype is currently in progress.


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\end{document}
