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Title: Software agents support for personalised learning: Negotiating and e-contracting with multiple providers
Author: Vegah, Godwill
ISNI:       0000 0004 2720 2497
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2012
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E-learning is increasingly adopted to support face-to-face classroom-based learning or implemented as a complete standalone learning system. Its inherent adaptable nature and ability to provide learning anywhere, everywhere and anytime makes it a versatile tool for access to basic, professional and higher education. This research proposes and develops an adaptable e-learning approach, focusing on the learner's requirement specification and negotiation of course with multiple providers to improve online learning. This addresses issues of inflexible learning model, narrow coverage of subject domains in existing systems and ineffective use of educational resources, using design research methodology (DRM). The proposed Intelligent Learning approach provides learning support by applying collaborative and deliberative capabilities of software agents to e-learning systems. Designated learning support agents negotiate with providers on behalf of the learner for courses, matching specified requirements. This is achieved through agent negotiation strategies, devising dynamic learning plans (DPLAN) and online learning contract (or EContract) between the system and a range of providers, to harness the changing needs of the learner, hence, providing an Adaptive Agent Learner Plan (ADALP) approach. It develops and applies a 'Basic Requirements Learning' model, addressing specific learning objectives, supported by a two way evaluation process that enforces learning flexibility, empowering learners and accommodating a wide spectrum of learning needs. Unlike traditional Intelligent Tutoring System (ITS), learning objectives are not fixed and are constituted dynamically from learner specifications. The ADALP approach provides multiple provider support options, generating learner feedback for goal oriented, but flexible learning. This deviates from the traditional 'top-down' approach, where instructors and designers create fixed models of different categories of learners and their needs. The prototype of multi-agent system (MAS) demonstrates contributions of the approach, applying Multi-issue-Negotiation and Contracting Courses with Multiple Providers; devising dynamic personalised learning plans and learning commitment (or e-contracts) between learners and providers. It implements designated agents which generate tasks and sub-tasks corresponding to the learners' goals and objectives; 'biding' for learning and tutoring resources from multiple providers to deliver on the derived tasks. Personalised learning plan aligned with online learning contract is generated for each learner based on the specified requirements and learning goals, as a result. It is argued that the ADALP approach empowers learners and improves on similar approaches, in comparison to existing adaptive learning systems.
Supervisor: Mehandjiev, Nikolay Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Intelligent and Adaptive learning system ; Dynamic learning plan ; Agent