An Architectural Model for Component Groupware

This paper proposes an architectural model to facilitate the design of component-based groupware systems. This architectural model has been defined based on (1) three pre-defined component types, (2) a refinement strategy that relies on these component types, (3) the identification of layers of collaboration concerns, and (4) rules for the coupling and distribution of the components that implement these concerns. Our architectural model is beneficial for controlling the complexity of the development process, since it gives concrete guidance on the concerns to be considered and decomposition disciplines to be applied in each development step. The paper illustrates the application of this architectural model with an example of an electronic voting system

An Agent-based System for Supporting Learning from Case Studies

Abstract. A main issue in collaborative learning is providing support and monitoring both the individual learners and the group activities. In this sense, there is a variety of functions that might be accomplished by a collaborative learning support system. Some examples are: knowledge diagnosis and evaluation, group and individual feedback, student and group modelling, and so on. LeCS (Learning from Case Studies) is a collaborative case study system that provides a set of tools and accomplishes some functions that together support learners during the development of a case study solution. This paper gives an overview of LeCS, focusing on the system design and architecture. The LeCS design is based on our model of supporting the learning from case studies method in a computer-based environment and in a distance learning context. The LeCS architecture is agent-based and includes three kinds of agents.

Helping Groups Become Teams: Techniques for Acquiring and Maintaining Group Models

Abstract: People are small group beings. Interacting with group members provides us with the opportunity to receive feedback, to discuss different ideas, and to get support for our endeavours. Groups generally learn faster, make fewer errors, recall better, make better decisions and are more productive than individuals working on their own. However, not all groups achieve high performance. Typically, groups start off as being traditional groups and in some cases evolve to teams, where the group performance is outstanding. In order to assist their groups in the process of evolving to teams, collaborative/cooperative systems need to keep a model of their group. In this work we present a review of different AI techniques and tools that implement them, presenting their benefits and difficulties regarding the user and group modelling tasks. The aim is to point out open questions related to choosing which technique use to model the group in different circumstances in order to help groups become teams

An Approach of Student Modelling in a Learning Companion System

Abstract. Nowadays there is an increasing interest in the development of computational systems that provide alternative (to the traditional classroom) forms of education, such as Distance Learning (DL) and Intelligent Tutoring Systems (ITS). Adaptation in the process of interaction with the student is a key feature of ITS that is particularly critical in web-based DL, where the system should provide real-time support to a learner that most times does not rely on other kinds of synchronous feedback. This paper presents the LeCo-EAD approach of student modelling. LeCo-EAD is a Learning Companion System for web-based DL that includes three kinds of learning companions-collaborator, learner, and trouble maker- that are always available to interact with and support the remote students. The student modelling approach of LeCo-EAD is appropriate to the DL context as it allows updating the student model in order to provide feedback and support to the distant students in real-time.

A learning object on computational intelligence

This paper presents a Learning Object in the domain of Computational Intelligence that can be used in graduate and undergraduate courses. Additionally, it can be reused in other contexts and scenarios, such as a distance learning course on Artificial Intelligence

A learning object on computational intelligence

This paper presents a Learning Object in the domain of Computational Intelligence that can be used in graduate and undergraduate courses. Additionally, it can be reused in other contexts and scenarios, such as a distance learning course on Artificial Intelligence