e-Learning, e-Practising and e-Tutoring: an Integrated Approach

In this paper is described a didactic methodology combining current e-learning methods and the support of Intelligent Agents technologies. The aim is to favor the synthesis among theoretical approach and based practical approach using the so-called Intelligent Agent, software that exploits the Artificial Intelligence and that operates as tutor, facilitating the consumers in the training operations. The paper illustrates how such new Intelligent Agent algorithm (IA) is used in the training of employees working in the transportation sector, thanks to the experience gained with the PARMENIDE project - Promoting Advanced Resources and Methodologies for New Teaching and Learning Solutions in Digital Education

Say Hello to Your New Automated Tutor – A Structured Literature Review on Pedagogical Conversational Agents

In this paper, we present the current state of the art of using conversational agents for educational purposes. These so-called pedagogical conversational agents are a specialized type of e-learning and intelligent tutoring systems. The main difference to traditional e-learning and intelligent tutoring systems is that they interact with learners using natural language dialogs, e.g. in the form of chatbots. For the sake of our research project, we analyzed current trends in the research stream as well as research gaps. Our results show for instance that (1) there is a trend towards using mobile conversational agents in education, (2) a proper generalization of existing research results (e.g. design knowledge) is missing, and (3) there is a need for comprehensive in-depth evaluation studies and corresponding process models. Based on our results, we outline a research agenda for future research studies

A Hybrid Approach for Supporting Adaptivity in E-learning Environments

Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the ECA model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity in any given Learning Management System based on learners’ learning styles. Design/methodology/approach: This paper offers a brief review of current frameworks and systems to support adaptivity in e-learning environments. A framework to support adaptivity is designed and discussed, reflecting the hybrid approach in detail. A system prototype is developed incorporating different adaptive features based on the Felder-Silverman learning styles model. Finally, the prototype is implemented in Moodle. Findings: The system prototype supports real-time adaptivity in any given Learning Management System based on learners’ learning styles. It can deal with any type of content provided by course designers and instructors in the Learning Management System. Moreover, it can support adaptivity at both course and learner levels. Research limitations/implications: Practical implications: Social implications: Originality/value: To the best of our knowledge, no previous work has been done incorporating the concept of the ECA model and intelligent agents as hybrid architecture to support adaptivity in e-learning environments. The system prototype has wider applicability and can be adapted to support different types of adaptivity

Formal models in web based contracting

Legal principles have some difficulty to deal with software agents celebrating contracts and operating in e-commerce environments without direct human intervention. Autonomous intelligent agents have a control on their own actions and states, supporting or taking effective decisions. Therefore, some qualitative parameters such as trust, reputation and quality of information have to be taken under consideration to evaluate, certify and justify such decisions. Indeed, this paper shows how to construct a dynamic virtual world of complex and interacting entities or agents, organized in terms of Multi-Agent Systems (MAS), that compete against one another in order to solve a particular problem, according to a rigorous selection regime in which its fitness is judged by one criterion alone, a measure of the quality of information of the agent or agents, here understood as evolutionary logic theories. This virtual world could witness the emergence of our first learning, thinking machines, that may cater for some issues on the evolution of formal models of the world in general, and on what is concerned with the objectives set to this work, in contracting, and foray into a vast, untapped technological market.Fundação para a Ciência e a Tecnologia (FCT) - Intelligent Agents and Legal Relations Project – POCTI/ JUR/57221/2004

Evaluation Study and Results of Intelligent Pedagogical Agent-led Learning Scenarios in a Virtual World

The use of intelligent pedagogical agents (IPAs) has shown to provide positive learning results and new learning possibilities. The IPA has specific importance in immersive learning environments to enable 24/7 availability, offer a learning companion opportunity, and increase learner engagement. In that regard, a proof of concept prototype implementation of an intelligent pedagogical agent (IPA) in the Open Wonderland virtual world environment was created. This paper reports a qualitative evaluation study and experiment performed by a team of six experts in relevant areas of expertise. Those areas include cognitive science, computer science, e-education, and virtual worlds. The experiment studied key prototype components in relation to four learning scenarios with distributed control between the learner avatar and the pedagogical agent to answer questions relevant to their effect on learning attributes such as motivation, engagement, and the learning experience. Given the qualitative nature of the experiment, the paper also analyzes and reports results relevant to expert input of how the prototype can better contribute to future pedagogical agent realizations and the impact on learning enhancement

Swarm-based Sequencing Recommendations in E-learning

To be presented at the International Workshop on Recommender Agents and Adaptive Web-based Systems (RAAWS 2005) held in conjunction with the Intelligent Systems Design and Applications 2005 Conference (ISDA 2005), Wroclaw, Poland, September 8-10, 2005. Proceedings 5th International Conference on Intelligent Systems Design and Applications, (Eds) Kwasnicka, H. & Paprzycki, M., IEEE Computer Society, 2005, pp.488-493Open and distance Learning (ODL) gives learners freedom of time, place and pace of study, putting learner self-direction centre-stage. However, increased responsibility should not come at the price of over-burdening or abandonment of learners as they progress along their learning journey. This paper introduces an approach to recommending the sequencing of e-learning modules for distance learners based on self-organisation theory. It describes an architecture which supports the recording, processing and presentation of collective learner behaviour designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The article includes initial results from a large-scale experiment designed to validate the approach

An adaptive feedback approach for e-learning systems

The adaptive e-learning systems are a hot topic of educational research. The approach presented is a knowledge-based. There are several types of adaptation of an e-learning system to the learner: content adaptation, interface personalization, etc. This paper dials with a model for adaptation of the learner assessment and the content of one learning system. The model is based on Computer Adaptive Test Theory (CAT) and organization of the learning domains. The learning objects (LO) and the test item ontology play a central role as resource structuring. It supports flexible adaptive strategies for assessment and navigation through the content. Learner knowledge is assessed by CAT and then the system returns the learner to the right leaning material corresponding to the knowledge shown. The congruence between CAT item bank and the LO pool is based on intelligent agents. It supports adaptive feedback to the students depending on the learner evaluation

Deep imitation learning for 3D navigation tasks

Deep learning techniques have shown success in learning from raw high dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: Deep-Q-networks (DQN) and Asynchronous actor critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an e�ective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples