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Athabasca University

Project Information

Name: Reasoning Capability of Intelligent Agents for Adaptive Learning
Abstract     An adaptive learning system (AL system) is able to adapt its behavior to the learner's needs by personalizing educational curricula and contents, and by providing remedial or tutorial tailored to the properties of the individual learner. To adapt its behavior, an AL system must consider the differences among learners in terms of learning objectives, knowledge, experience, learning styles, and learning preferences. In an AL system with an agent-based architecture, an intelligent agent is associated with each learner or a specific task to simulate a course instructor or tutor performing pedagogical tasks. Due to its distributed nature and task complexity, an agent-based AL system is structured as a multi-agent system. The abilities of the agents to adapt rationally in an open environment hinge on possessing factual knowledge, such as knowledge of curricula, learners, educational resources, and, most importantly, possessing reasoning capabilities. The reasoning capabilities include "understanding" learner characteristics and knowledge domains; the ability to plan and optimize study plans and learning activities; the ability to update learning models; coordination of the intervention of a set of task-specific agents; and coalition formation for collaborative learning, etc. Existing learning systems, however, have their reasoning mechanisms hardwired for specific domains, and the domain-specific nature of the reasoning mechanisms restricts the reusability of the systems themselves. This proposal highlights a new methodology for formalizing the reasoning models and mechanisms for the agents of AL systems. In the short-term, this research will focus on designing algorithms for adaptive course planning, adaptive testing, and adaptive coalition formation for collaborative learning, and applying different models and mechanisms to different tasks to determine which option is the most appropriate in each instance. In the long-term, this research will attempt to develop a fully open and scalable, agent-based learning environment. The technology to be developed in the research will facilitate further R&D in modern distributed learning. Also, the students to be trained through this project will become pioneer engineers or researchers in agent-based intelligent systems.
Start Date 2008-04-01
End Date 2013-04-01
Research Areas
adaptive learning systeme-learningmechanism design
Source Name Amount
NSERC75,000
Faculty Researcher(s)

Updated June 19 2017 by FST Technical Staff

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