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Self-Organising P2P Learning for 21C Education
Conference Paper · November 2013
DOI: 10.13140/2.1.1309.9843
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2.
Self-Organising P2P Learning for 21C Education
Dymitr Ruta, Leigh Powell, Di Wang, Benjamin Hirsch, Jason Ng
Etisalat BT Innovation Centre (EBTIC)
Khalifa University of Science, Technology and Research
Abu Dhabi, United Arab Emirates
Email: {dymitr.ruta, leigh.powell, di.wang, benjamin.hirsch}@kustar.ac.ae
and jason.ng@bt.com
Abstract—Knowledge becomes a highly valuable shared com-
modity that once acquired can be exploited again and again for
the rest of our lives. Successful societies in 21st
century are likely
to be determined by the efficiency and depth of their knowledge
transfer throughout their education systems and beyond. In this
work we try to address the challenge of how to maximise the
students learning efficiency in the typical educational system both
on the conceptual and practical levels. Inspired by the research
on peer-to-peer (P2P) sharing networks we define the arguably
optimal self-organising learning environment for education and
demonstrate several practical tools and systems supporting our
model within the holistic intelligent campus framework - iCampus
(intelligentcampus.org).
I. INTRODUCTION
The knowledge infrastructure becomes a fundamental com-
ponent of any society and the process of acquiring knowledge
— learning becomes critical and life-changing endeavour ex-
tending into entire lifetime well beyond formal education [1].
The key property of knowledge in the context of human
learning is that once acquired it can by repeatedly used not only
to our benefits, but also of others as the new bearer instantly
becomes a new source of knowledge i.e. teacher. Ignoring the
issue of forgetting at this stage, it is important to realise that
you will never be less ignorant than you already are and you
will never have more time to learn than at this very moment.
Hence, the sooner you learn, the sooner and longer you can
enjoy the benefits of being more informed and pass knowledge
on to others [2]. These thought provoking statements spell
out the importance of efficient and early learning and convey
the essence of group self-learning, where initial teacher-to-
student knowledge transfer is replaced by the autonomous
peer-to-peer (P2P) style knowledge exchange that maximizes
the total transferred knowledge and leaves all participants the
most informed they can possibly be.
This challenge is not free of obstacles ranging from student
motivation, teacher authority issues all the way up to knowl-
edge content structuring and creating an efficient network
infrastructure enabling global sharing [3], [4]. Organically,
we intend to lay the foundations for the autonomous self-
organising learning network by first defining general model as-
sumptions and then introducing a portfolio of suitable support-
ing tools and learning environments developed at EBTIC in the
context of the iCAMPUS initiative (intelligentcampus.org) [6].
II. SELF-ORGANISING PEER-TO-PEER LEARNING
Human learning is a complex and multi-modal process of
acquiring knowledge that can be approached from many dif-
ferent standpoints. Biologists would see the effects of learning
as reconfigurations of the brain’s neural network, sociologists
might measure the effect of learning as the ability to teach
others, while physicists might measure the entropy loss. In
order to devise some semi-quantitative measure of learning and
its optimality we adopt an information theoretic perception of
knowledge as the collection of information units, and learning
itself as a process of information transfer from the more to
less informed source via an imperfect channel [7].
For simplicity let us consider that a group of learners is
fully connected i.e. all participants are allowed to directly
exchange information. In a typical learning episode the teacher
attempts to transfer his knowledge to all the students. The
traditional effect of such a knowledge broadcasting exercise
is that the students acquired the proportion of the knowledge
delivered by the teacher determined by their individual knowl-
edge capture (or learning) rates αi. As students are different,
individual knowledge items captured throughout the learning
process are expected to be uniformly distributed across the
knowledge space and diversely distributed among students.
This creates the potential for students to teach each other i.e. to
absorb individual knowledge portions that they did not capture
yet from each other and thereby improve learning efficiency.
One way to structure such emergent self-organised learning
could be to collect requests for knowledge items from less
informed and pass them on to more informed for provision.
In this setup the roles of learners and teachers would be dy-
namically mixed such that a student would request for several
missing knowledge items while providing items he already
has to others. To simplify the communication both knowledge
requests and their answers can be made visible (broadcast)
to all other students to seamlessly verify and answer similar
requests while improving knowledge retention of the whole
group. If the resources availability and / or communication
overhead are of concern the network can be further intelligently
sub-grouped to stimulate maximum knowledge transfer while
minimising the communication traffic. This model can be
further enhanced by first capturing information about who
possesses the requested knowledge within the group (click on-
request function) and then directing the requests for answer
to the most knowledgeable, available, or inexpensive teachers
depending on the criterion of learning optimality applied.
Figure 1 illustrates the concept of the self-organised learning
network in the schematic lecture situation.
Such a model directly relates to the peer-to-peer sharing
networks and hence by drawing from the rich expertise in this
field [8], [9] we conjecture that such P2P inspired group self-
learning is semi-optimal in terms of the amount of knowledge

3.
Fig. 1. Self-organised learning network in a schematic lecture situation
transferred and retained within a group in a unit of time,
i.e. it facilitates the fastest group learning possible. Due to
difficulties with precise measurement of the knowledge the
proof of this conjecture is beyond the scope of this paper.
Some specific model setups might take into account more
detailed dynamics of the learning encounter, learning-curve
and forgetting effects, offline-teacherless learning, as well
as address student participation problems by some kind of
intelligent knowledge provision incentive scheme.
The following sections showcase our significant practical
steps towards self-organised leaning that demonstrate improved
efficiency, participation, quality and depth of the learning
process in the real learning environment developed within the
Intelligent Campus (iCampus) framework for 21C education.
III. INTELLIGENT PARTICIPATION AND GROUPING
Research in eduction and learning clearly shows that group-
ing heterogeneous students together improves their learning
efficiency [5], [3], [4]. This matches the intuition that students
with diverse expertise have the potential for greater and
possibly faster knowledge transfer as they jointly cover a
wider knowledge space and simply have more to gain from
each other. The reason why such further group structuring
might improve learning efficiency becomes also clear when we
realise the limited time that constrains the access to resource-
ful students-teachers. By pro-actively restricting knowledge
transfer flow to only high ”traffic” channels we maximise the
knowledge throughput which otherwise might be affected by
frequent, time- and resource-consuming small knowledge ex-
changes. Summarising, in a search for the shortest knowledge
transfer path or a de-facto minimal cost of learning we try to
address the challenge of how to restrict learners’ knowledge
exchange by organising them into subgroups such that their
learning efficiency is maximised.
Quantitative solutions of this problem require precisely
measurable knowledge content of all the students along with
their individual learning rate estimates. In the absence of
these, however, we decided to develop a proxy solution that
automates intelligent grouping based on diversity enforcing
rules. The proposed tool, more extensively reported on in [10],
is able to automatically assemble groups of students based on
a set of user defined rules and available student information.
The screenshot of the tool is shown in Fig. 2. Combinations
of student data (left column) and the logical rules definition
section (top panel) allow users to define flexible and generic
rules that become instantly deployed with live results (visible
in the bottom-right section). Examples of the rules that can be
generated by such mechanisms include:“at least one student
with high mark in math”, “no mixed gender”, “at least one
student with good English skills” etc. This tool has been devel-
oped as a standalone system; but its capability can be readily
made available to the other modules if necessary. It is expected
to enhance the learning outcomes especially when used in
conjunction with the collaborative learning environment. The
rules engine that we have developed can also be harnessed to
other applications within the smart learning platform.
Fig. 2. Screenshot of the student grouping tool
IV. COLLABORATIVE LEARNING ENVIRONMENT (CLE)
The student grouping tool presented above gives only the
potential for improved learning efficiency. What enables a col-
laborative learning is actually the whole learning environment
where students can connect and work together using new func-
tionalities that support easier knowledge sharing and stimulate
joint learning through creative processes and experiences.
The Collaborative Learning Environment (CLE) is a system
developed at EBTIC [10], [11] that brings together a collection
of tools and functionalities enabling communication, informa-
tion sharing and collaborative document creation within the
same environment. As opposed to individual communication
and sharing tools like Skype, Facebook, or Google Drive which
focus on a specific interaction or activity, CLE is designed
to integrate these different functionalities into one, cohesive
environment. The aim of CLE is to stimulate the collaborative
learning process and enable instructors to facilitate collabora-
tive assignments more easily. Moreover, the whole interaction
history is logged, which provides data enabling a dynamic
analysis of contributions, usage and participation as well as to
allow for more advanced future functions such as knowledge
elicitation. A sample screenshot of the CLE is shown in Fig. 3.
Communication features of the CLE include synchronous
text chat and audio/video communication, which allow partic-
ipants to exchange ideas and communicate directly with each
other regardless of their geographic location. Additionally,

4.
Fig. 3. Collaborative Learning Environment (CLE) in action
a collaboration area is provided to allow students to either
synchronously or a-synchronously create an assignment. This
area, called the collaborative editing pad, provides a canvas on
which each student can contribute and revise their ideas. Each
contributor to the pad is assigned a unique color, so individual
contributions are evident, and each keystroke, whether it is an
add, edit or delete is recorded by the pad. Using this data, the
CLE statistics module (shown in Fig. 4) can output detailed
usage statistics to the instructor upon request, allowing for an
in-depth analysis of how an individual assignment was built
and giving insight into how the group collaborated together
as a whole. Beyond statistics, a playback feature is provided,
allowing students and instructors to watch the entire creation
of the assignment, from start to finish, much like watching
a video. Both the statistics and playback features of CLE
were used by instructors of the Freshman Design Engineering
Course at Khalifa University to assist in analyzing student
group adherence to a prescribed engineering design cycle
throughout the Spring 2013 semester.
Fig. 4. CLE statistics module: sample group contributions graphs
Implemented as a set of modules for Moodle, an open-
source learning management system (LMS), CLE is able
to capitalize on existing Moodle functionalities like group
creation, file sharing and forums. And by leveraging the flex-
ibility which open-source technologies provide, CLE is able
to seamlessly integrate into the LMS, providing a workspace
that is already familiar to both students and faculty, thereby
reducing cognitive load and enabling more focus to be placed
on collaborative learning and interaction.
Further work around CLE that can stimulate learning effi-
ciency and promote self-learning is planned to cover a wider
pool of curriculum elements such as lectures and tutorials,
open collaborative learning beyond formally assigned groups
and to better capture and evaluate students groupwork both in
terms of merit and the knowledge transferred by individuals
to the group alongside this experience.
V. SOCIAL NETWORK FOR LEARNING
CLE enriches traditional forms of learning by adding
interaction and knowledge exchange to the organised learning
experiences like courseworks or assignments. What it effec-
tively embodies is a special case of social interaction focussed
on a particular task within a defined small social network.
Online social networks with the help of tools like Face-
book, Twitter, LinkedIn are now well established means of
interacting, discussing and sharing virtually anything, anytime,
and anywhere. In the context of informal learning, social
network is an invaluable resource. No learning is more efficient
than filtered, high quality and reliable information passed on
by an expert we know. The value of social interaction for
learning lies in communicating the knowledge quickly and
directly using only relevant content at the self-adjustable level
of complexity mutually acceptable by the interacting parties.
This is a stark contrast to the lengthy isolated web searches,
tedious process of understanding, filtering relevant content etc.
In an attempt to harness the power of social networking for
learning EBTIC has developed a Learning and Social Environ-
ment (ELSE) platform [10]. It promotes informal collaborative
learning that combines traditional networking activities like
walls, groups and file sharing and novel Social Network
Analysis (SNA) techniques to identify and recommend relevant
contact, group or content to speed up and enhance the learn-
ing process. Student profiles are automatically updated using
information available from university systems. A combination
of static information from user profiles and their most recent
interaction is combined to derive content recommendations
meant to be most relevant at the time. A screenshot of the
tool is presented in Fig. 5.
Fig. 5. ELSE Learning & Social Environment
Further possible enhancements of ELSE could exploit its
building up interaction data to knowledge elicitation, discovery
and crowdsourcing.

5.
VI. MOBILE LEARNING PLATFORM
Social networks experienced a very rapid expansion thanks
to the mobile revolution. According to Gartner, worldwide PC
shipment is to fall over 10% while tablet shipment are expected
to grow 67% in 2013. Most of technology research agencies
forecast mobile devices to take over traditional desktop PCs in
terms of the number of users, overall data traffic and time spent
by 2014. This unprecedented dynamics is equally prevalent
in education where easier access to information, emerging
collaborative learning infrastructure and the culture of sharing
open entirely new opportunities for learning truly anywhere
and anytime. These opportunities have been spotted early
by some education governing institutions example of which
includes the iPad-for-every student initiative launched across
UAE Higher Education institutions [12].
The key value that mobile platform brings to the learn-
ing is convenience and simplicity of interactions with the
outside world truly anytime and anywhere. Channelling it to
support collaborative learning translates predominantly into
further improvements of inclusion and participation in terms
of both knowledge absorption and provision. Mobile platform
enablement for learning is supported by the UAE government.
Our initial efforts to enable mobile platform for learning
concerned the development of the Mobile Companion Sys-
tem [13] providing interactive management of personalised
learning environment on mobile devices. Using this tool stu-
dents can select and register for different courses, access their
up-to-date timetable, monitor their academic performance and
access recorded or live video lectures all on the always avail-
able mobile device. Figure 6 illustrates some features of the
tool like timetable, video lectures and academic performance.
Fig. 6. Mobile Companion in action: timetable, video lecture, performance
Finally, yet another mobile learning project involved the
development of the Mobile Assessment Tool [13], able to effi-
ciently conduct multiple choice – type evaluations of learning
objectives utilising mobile devices. The tool as depicted in
Fig. 7 allows to carry out real-time personalised evaluations
of learning results and as such becomes a key source of data
for our further research into self-organised learning networks.
VII. CONCLUSION
Summarising, we have presented the concept of self-
organised P2P learning and conjectured its pseudo-optimality
with respect to the learning efficiency understood as the total
knowledge transferred from the teacher to a group of students
per unit of time. We have sketched various scenarios where
Fig. 7. Mobile Assessment Tool
such new learning paradigm could improve current teach-
ing and learning standards across the education system and
demonstrated several practical tools that support our concept.
These tools spanning intelligent grouping, collaborative learn-
ing environment, social networking and mobile learning have
been developed at EBTIC and successfully trialled at Khalifa
University under the iCAMPUS initiative. It is our intention to
integrate all these tools together within an autonomous open
platform for self-organised P2P learning that would synergi-
cally and adaptively deliver highly efficient learning experience
to the end users within a knowledge-based economy.
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