Kalliopi Tourtoglou

User Stereotypes Concerning Cognitive, Personality and Performance Issues in a Collaborative Learning Environment for UML

In this paper, we describe the user modelling mechanism of AUTO-COLLEAGUE, which is an adaptive Computer Supported Collaborative Learning system. AUTO-COLLEAGUE provides personalised and adaptive environment for users to learn UML. Users are organized into working groups under the supervision of a human coacher/trainer. The system constantly traces the performance of the learners and makes inferences about user characteristics, such as the performance type and the personality. All of these attributes form the individual learner models, which are built using the stereotype theory. User modelling is applied in order to offer adaptive help to learners and adaptive advice to trainers aiming to support them mainly in forming the most effective groups of learners.

An Adaptive Training Environment for UML

Adaptivity in training software systems is of great importance as it can adjust one single training environment to the needs of each individual trainee. ASSET is such a UML training software system as it provides adaptive support to both the trainer and the trainees. Specifically, it provides an environment that can be parameterised by the trainer to suit the training needs of his/her company or educational institution. The adaptive help to the trainer concerns mainly advice on the best schemes of collaboration between the trainees and the most efficient and productive forming of group as well. The adaptive help destined for the trainee refers to knowledge, user type and personality attributes. The special characteristic of ASSET is that it does not base its inferences on specific tests, but constantly monitors every action of the trainees during their interaction with the system.

Simulated Annealing in Finding Optimum Groups of Learners of UML

The Simulated Annealing (SA) algorithm (Kirkpatrick et al. 1983) is a genetic algorithm that serves as a general optimization technique for solving combinatorial optimization problems. The local optimization algorithms start with an initial solution and repeatedly search for a better solution in the neighborhood with a lower cost. So, the locally optimal solution to which they result is this with the lower cost in the neighborhood.

An Empirical Study for Integrating Personality Characteristics in Stereotype-Based Student Modelling in a Collaborative Learning Environment for UML

The aim of this paper is to present an empirical study for defining the appropriate triggers for personality related stereotypes. These stereotypes are used for modelling the students in a Computer Supported Collaborative Learning (CSCL) system for UML. The system builds individual student models to provide adaptive and intelligent advice concerning their knowledge and the most adequate colleagues for collaboration. Most of existing CSCL systems and Intelligent Learning Environments (ILE’s) also include student models. However, the vast majority of them use the student models to describe the students from the perspectives of knowledge and/or participation in collaborative activities. In our approach, the student models additionally to the knowledge describe the students regarding specific personality characteristics related to their learning and collaboration attitudes. The student models are built using the stereotype-based method, which entails the definition of the stereotypes, their facets and the triggers (the triggering conditions for a student to belong to a stereotype). As the definition of the triggers is a task of high importance for the effectiveness and accuracy of the student models, we conducted an empirical study among experienced trainers of software engineering.