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Self-Organising P2P Learning for 21C Education

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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/270824086
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Self-Organising P2P Learning for 21C Education
Dymitr Ruta, Leigh Powell, Di Wang, Benjamin Hirsch, Jason Ng
Etisalat BT I...
Fig. 1. Self-organised learning network in a schematic lecture situation
transferred and retained within a group in a unit...
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Self-Organising P2P Learning for 21C Education

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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 commodity 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).

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 commodity 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).

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Self-Organising P2P Learning for 21C Education

  1. 1. See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/270824086 Self-Organising P2P Learning for 21C Education Conference Paper · November 2013 DOI: 10.13140/2.1.1309.9843 CITATIONS 6 READS 139 5 authors, including: Some of the authors of this publication are also working on these related projects: Hyperbox based machine learning algorithms View project Classifier Diversity in Combined Pattern Recognition Systems View project Dymitr Ruta Khalifa University 82 PUBLICATIONS   1,367 CITATIONS    SEE PROFILE Leigh Powell Khalifa University 9 PUBLICATIONS   43 CITATIONS    SEE PROFILE Benjamin Hirsch Khalifa University 71 PUBLICATIONS   611 CITATIONS    SEE PROFILE All content following this page was uploaded by Dymitr Ruta on 14 January 2015. The user has requested enhancement of the downloaded file.
  2. 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. 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. 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. 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. REFERENCES [1] L. Lee-Kelley and A. Crossman. ”Knowledge as a shared commodity in 21st century networked organisations: trust and power as prefects”. Proc. of the International Telework Academy Workshop, Crete, 2004. [2] J.R. Anderson. Learning and memory: An integrated approach, (2nd eds). New York, NY, US: John Wiley & Sons, Inc, 2000. [3] T.S. Roberts. Online collaborative learning: Theory and practice. Her- shey, PA.: Information Science Publishing, 2004. [4] E.F. Barkley, K.P. Cross and C.H. Major. Collaborative learning tech- niques: A handbook for college faculty. San Francisco: Jossey-Bass, 2005 [5] P. Heller, R. Keith, and S. Anderson. ”Teaching Problem Solving Through Cooperative Grouping. Part 1: Group Versus Individual Problem Solving”. American Journal of Physics, vol. 60, no. 7: 627636, 1997. [6] J.W.P. Ng. ”White Paper on the Intelligent Campus (iCampus)”. Etisalat- BT Innovation Center (EBTIC), Abu Dhabi, UAE, Version 2.1, 2010. [7] G.J. Klir. Uncertainty and Information: Foundations of Generalized Information Theory. John Wiley & Sons, Hoboken. New Jersey, 2005. [8] P. Antoniadis, C. Courcoubetis, R. Weber. ”An Asymptotically Optimal Scheme for P2P File Sharing”. 2nd Workshop on the Economics of Peer- to-Peer Systems, Harvard University, 2004. [9] M. Mehyar, WH Gu, S.H. Low, M. Effros and T. Ho. ”Optimal Strategies for Efficient Peer-to-Peer File Sharing”. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, (ICASSP 2007), Honolulu, HI, 2007. [10] B. Hirsch, A. Al-Rubaie, D. Wang, C. Guttmann, J.W.P. Ng. ”Enabling the Next Generation Learning Environment”. Proc. of the Int. iCampus Symposium (IC’12), Macau, China, 2012. [11] B. Hirsch, G.W. Hitt, L. Powell, K. Khalaf, S. Balawi. ”Collaborative Learning in Action”. Proc. of the IEEE Int. Conf on Teaching, Assess- ment and Learning for Engineering (TALE’2013), Bali, Indonesia, 2013. [12] N. AlTaher. ”UAE Vice-President launches iPad Initiative.” In- ternet: http://gulfnews.com/news/gulf/uae/education/uae-vice-president- launches-ipad-initiative-1.1080182. Sep 23, 2012. [13] O. Al Hammadi, M.J. Zemerly and J.W.P. Ng. ”Personalized uLearning in a Smart Anytime-Anywhere Campus Environment”. Ambinet Intellu- gence and Smart Environments, Vol. 10: Proc. of the 7th Int. Conf. on Intelligent Environments, pp 511-522. IOS Press, 2011 [14] A. Al Dhanhani, R. Mizouni, H. Otrok and J.W.P. Ng. ”Smart Mobile Assessment Tool”. Ambinet Intellugence and Smart Environments, Vol. 10: Proc. of the 7th Int. Conf. on Intelligent Environments, pp 523-533. IOS Press, 2011. View publication statsView publication stats

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