Knowledge Representation in Intelligent Collaborative Educational Systems

Abstract. In this paper, the concept of collaborative intelligent educational system is presented. Different knowledge representation models are compared in the context of their use in collaborative intelligent educational systems. Advantages of semantic networks for knowledge representation in such systems are described. M ain advantages of extended semantic networks are shown and a set of basic operations regarding them is drawn up

aiTutor : um etutor de agentes inteligentes

Relatório de estágio apresentado para a obtenção do grau de mestre em Educação e Comunicação MultimédiaOs Sistemas Multiagente são estruturas complexas que têm um conjunto próprio de objetivos e que interagem com o seu mundo dinâmico, através de um sistema organizacional, bastante complexo, que lhes permite atingir objetivos e resolver tarefas. Com o incremento de níveis de superiores de complexidade na estrutura destes sistemas, é fundamental usar metodologias, ferramentas e notações orientadas para o desenvolvimento de sistemas orientados a agentes. Desta forma a metodologia TROPOS vem acrescentar o método para o desenvolvimento de um SMA. Esta metodologia é apoiada na ferramenta i* e é constituída por quatro fases de desenvolvimento: Requisitos Iniciais (Early requirements), Requisitos Finais (Late requirements), Arquitetura de Projeto (Architectural design) e Projeto Detalhado (Detailed design). O objetivo deste relatório é o de apoiar um futuro desenvolvimento de um Sistema de Tutoria Inteligente que permita adequar as plataformas existentes, nomeadamente, o Moodle, a este tipo de interações.The Multi-agent systems are complex structures that have their own set of goals and interact with their dynamic world through an organizational system, very complex, which allows them to reach goals and solve tasks. With the increase of higher levels of complexity in their structure, it’s fundamental to use methodologies, tools and oriented notations to develop systems oriented by agents. Therefore, the methodology TROPOS adds the method to the development of a SMA. This methodology is supported by the tool i* and it’s consisted of four phases of development: Early Requirements, Late Requirements, Architectural Design and Detailed Design. The purpose of this report is to support the future development of an Intelligent Tutoring System that allows adaptation to the existing platforms, like Moodle

Bridging the training needs of cybersecurity professionals in Mauritius through the use of smart learning environments.

Doctoral Degree. University of KwaZulu-Natal, Durban.Teaching and Learning confined to within the four walls of a classroom or even online Learning through Massive Online Courses (MOOCs) and other Learning Content Management Systems (LCMS) are no longer seen as the optimal approach for competency and skills development, especially for working professionals. Each of these busy learners have their own training needs and prior knowledge. Adopting the one-size-fits-all teaching approach is definitely not effective, motivating and encouraging. This is why this research presents the use of SMART Learning Environment that makes use of Intelligent Techniques to personalise the learning materials for each learner. It has been observed that on one hand the country is not able to provide the required number of IT professionals with the desired skills and on the other hand, the number of unemployed graduates in areas other than IT is increasing. This mismatch in skills is becoming a pressing issue and is having a direct impact on the ICT Sector, which is one of the pillars of the Mauritian Economy. An in-depth Literature Review was carried out to understand the training needs of these Cybersecurity professionals and also to understand the different Intelligent Techniques that can be used to provide personalisation of learning materials. Data was collected during three phases, namely an Expert Reference Group Discussion, a pre-test questionnaire and a survey questionnaire. The Expert Reference Group Discussion was carried out to further shed light on the research question set and to further understand the training needs and expectations of Cybersecurity professionals in Mauritius. A SMART Learning Environment making use of Artificial Neural Networks and Backpropagation Algorithm to personalise learning materials was eventually designed and implemented. Design Science Research Methodology (DSRM), Activity Theory, Bloom’s Taxonomy and the Technology Acceptance Model were used in this study. Due to the inherent limitations of the models mentioned, the researcher also proposed and evaluated an emergent conceptual model, called the SMART Learning model. The major findings of this research show that personalisation of learning materials through the use of a SMART Learning Environment can be used to effectively address the training needs of Cybersecurity professionals in Mauritius

MIPITS - An Agent based Intelligent Tutoring System

During the last decades many Intelligent Tutoring Systems (ITS) are developed to add adaptivity and intelligence to the e-learning systems. Intelligent agents and multi-agent systems are widely used to implement intelligent mechanisms for ITSs due to their characteristics. The paper presents an agent based ITS for the course “Fundamentals of Artificial Intelligence” named MIPITS. The MIPITS system is based on the holonic multi-agent architecture for ITS development. The system offers learning materials, provides practical problems and gives feedback to the learner about his/her solution evaluating his/her knowledge. The goal of the system is to realize individualized practical problem solving, which is not possible in the classroom due to the large number of students in the course. Thus, the main focus of the system is on problem solving. The system offers three types of problems: tests, state space search problems and twoplayer games algorithm problems. The MIPITS system is open: new types of problems can be added just by including appropriate agents in the system. The problems are adapted to the learner’s knowledge level and preferences about difficulty, size and practicality of problems