Design Optimization Module For Hierarchical Research and Learning Environment

The present paper describes a learning module on design optimization courses within a hierarchical research and learning network (HRLN). In this environment a knowledge organization can be created as a hierarchical learning network to link diverse inter- and trans- disciplinary teams from a consortium of universities, industry, government agencies and the providers of learning technologies. It is an approach that builds on computer-based training, intelligent tutoring systems, interactive learning, collaborative-distributed learning, and learning networks. The present design optimization module has been developed and described herein, as a demonstrator of a learning module in this environment. This module allows for the learners of design optimization to get the course material at their own convenience and time either via the internet or packaged files. Consequently, it is expected that the learner’s ability to understand design optimization and review its pertinent details will be enhanced significantly

Intelligent Agents for Distance Learning

Abstract. Distance learning involves a lot of work of human assistants. These assistants need to be connected for answering student doubts and questions. Intelligent agents can do part of this repetitive work because they can observe students interacting with educational courses, detect learning troubles of these students, and then suggest them some way for overcoming those troubles. However, a design problem appears with this promised possibility: how to connect educational applications with these agents. This paper presents a solution to this problem, in which both the capture of student’s intentions and agent intervention for helping students are specified. These two architectural design points are defined as connection points. The first connection point is named student intentions. Student intentions define situations in which agents might help. This connection point depends on the user interface of the educational application that students are using; the agent needs to know the gestures that students could do for interpreting their intentions. The second connection point is named agent interventions. Agent interventions define the context in which agent might assist and the type of help that might give, like a suggestion or a warning. This solution is introduced in the context of one specific application for distance learning named SAVER, which is used for exemplifying each architectural design point. Key words: intelligent agents, distance learning. 1

Abstract Intelligent Agents for Distance Learning

Distance learning involves a lot of work of human assistants. These assistants need to be connected for answering student doubts and questions. Intelligent agents can do part of this work. However a problem appears with this promised possibility: how to connect educational applications with these agents. This paper presents a solution to this problem, in which both the capture of student’s intentions and agent intervention for helping students are specified. These two design points are defined as connection points. Student intentions define situations in which agents might help. Agent interventions define the context in which agent might assist and the type of help that might give