Collaborative Learning Assistant for Android



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Collaborative Learning Assistant for Android

Abstract:

The quantitative and qualitative increase in mobile devices that reach the average user opens more and more topics for research. In education, m-Learning has been an interesting topic for several years. However, the smartphones, that today display an unprecedented mix of computing capability, connectivity and interactivity, leverage new possibilities for m- Learning applications. Such applications can seamlessly connect remote individuals, but can also provide access to various resources, such as media, or interactive quizzes. We focus on collaborative learning and peer-review learning, two closely related concepts, promoting methods such as sharing educational resources, organizing study sessions and giving feedback to fellow students. We propose a client-server system to provide all these features and we study it under performance considerations such as scalability and mobility.

Introduction

This paper discusses ways to improve the educational process by sharing resources among the stakeholders of the process using mobile technologies. This learning process encourages peer-review among the students while being overseen by professors.

ITU Statistics [1] describe the potential benefits that mobile devices and, in particular, mobile phones and tablets can bring to the educational field. Being somewhat of an untapped resource, these devices can prove useful considering that they have already been embraced by the younger generation.

Architecture Diagram:





Conclusion:

This paper describes an approach to improve the educational techniques through the use of mobile devices.

We have outlined a set of objectives and architectural specifications that would describe a collaborative mobile learning system. We also analyze technical aspects, such as problems that may arise when using the system under heavy load, scalability issues, and centralization of data and efficient organization of client access.

Besides functional tests, performance holds a key role in such a system. Scalability issues could arise when a large number of clients would interact with the central component:

References:


  1. Acharya, S., & Teltscher, S. “ITU estimates two billion people online by end 2010”, Newsroom: Press Release Retrieved 11 November, 2012, from:

http://www.itu.int/net/pressoffice/press_releases/2010/39.aspx

  1. Cochrane, Thomas. "mLearning: why? What? Where?

How?." Proceedings of the 28th ASCILITE Conference, ASCILITE. 2011.

  1. Naismith L., Lonsdale P., Vavoula G., and Sharples M. Literature review in mobile technologies and learning, 2004.

  2. L. Dyson, A. Litcheld, E. Lawrence, R. Raban, and P. Leijdekkers. Advancing the m- learning research agenda for active, experiential learning, 2007.

  3. Cook J. Phases of mobile learning. joint european summer school on technology enhanced learning, May 2009.

  4. Barker, Andrea, Greig Krull, and Brenda Mallinson. "A proposed theoretical model for m-learning adoption in developing countries." Proceedings of mLearn. Vol. 2005.

  5. Zahariev, Alexander. "Google app engine" TKK T-110.5190 Seminar on Internetworking. 2009.

  6. “Android Cloud to Device Messaging Framework” Available online at: https://developers.google.com/android/c2dm/

  7. “Google Cloud Messaging for Android” Available online at: http://developer.android.com/google/gcm/index.html

  8. Bolosky, William J., et al. "Paxos replicated state machines as the basis of a high- performance data store." Symposium on Networked Systems Design and Implementation (NSDI). 2011.




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