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Ultra-Personalization and Decentralization: The Potential of Multi-Agent Systems in Personal and Informal Learning

Record type: Conference or Workshop Item (Paper)

Agents are autonomous software components that work with one another in a decentralized fashion to achieve some end. Agent systems have been used in Technology Enhanced Learning (TEL) before, but these applications seldom take advantage of the fact that each agent may have its own goals and strategies, which makes agent systems an attractive way of providing personalized learning. In particular, since agents can solve problems in a decentralized way, this makes them an attractive way of supporting informal learning. In this paper we use scenarios to examine how common problem solving techniques from the agents world (voting, coalition formation and auction systems) map to significant challenges for personalized and informal learning in the TEL world. Through an agent simulation we then show how an agent system might perform in one of those scenarios and explore how different agent strategies might influence the outcome. Based on this work we argue that agent systems provide a way of providing ultra-personalization of the learning process in a decentralized way and highlight equitability and scrutability as two key challenges for future investigation.

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Citation

Aseere, Ali, Millard, David and Gerding, Enrico (2010) Ultra-Personalization and Decentralization: The Potential of Multi-Agent Systems in Personal and Informal Learning At 5th European Conference on Technology Enhanced Learning, Spain. , pp. 30-45.

More information

Published date: 2010
Venue - Dates: 5th European Conference on Technology Enhanced Learning, Spain, 2010-01-01
Organisations: Web & Internet Science, Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 271295
URI: http://eprints.soton.ac.uk/id/eprint/271295
PURE UUID: 40db64f8-69ba-4aaf-943f-be03609a90ed
ORCID for David Millard: ORCID iD orcid.org/0000-0002-7512-2710

Catalogue record

Date deposited: 23 Jun 2010 10:52
Last modified: 18 Jul 2017 06:44

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Contributors

Author: Ali Aseere
Author: David Millard ORCID iD
Author: Enrico Gerding

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