Intelligent agent supported personalization for virtual learning environments

Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically

Embodied agents in virtual environments: The Aveiro project

We present current and envisaged work on the AVEIRO project of our research group concerning virtual environments inhabited by autonomous embodied agents. These environments are being built for researching issues in human-computer interactions and intelligent agent applications. We describe the various strands of research and development that we are focussing on. The undertaking involves the collaborative effort of researchers from different disciplines

Design of Virtual Tutoring Agents for a Virtual Biology Experiment

Virtual learning environments (VLEs) may possess many advantages over traditional teaching methods in skills training that offer empowerment of constructing the skills by freely exploring a VLE. However, a conflict between the free exploration and ensuring the learning tasks tackled emerges in the learning process. A strategy to balance the conflict is to employ virtual tutoring agents to scaffold the learning tasks. This research has been carried out to investigate the issues of design and utility of a virtual tutoring agent system in a VLE to allow higher education (university based) students to practise immunology laboratory experiments, which simulates a well known immunochemical assay in the Life Sciences area, namely a Radio Immunoassay. This paper discusses the classification of category of the virtual agents in a VLE and focuses on the design of tutoring agents. Three types of the tutoring agents have been selected and implemented in the Radio Immunoassay simulation. The considered points in programming the virtual tutoring agents and their tasks are presented in this paper. A formative evaluation studies have been carried out and discussed to verify the designed virtual tutoring agents are satisfied to the target students' needs. Keywords Design of virtual tutoring agent, agent-based virtual learning environments, agent-based virtual environment for biology experiment, agent-based training software in biology, intelligent virtual laboratory, interactive learning software

Implementing intelligent pedagogical agents in virtual worlds: Tutoring natural science experiments in OpenWonderland

Intelligent Pedagogical Agents (IPAs) can be thought of as embodied intelligent agents that are designed for pedagogical purposes to support learning. They can be designed in particular for virtual worlds. Virtual worlds are becoming an interesting medium for engineering education for the properties of visual collaboration abilities providing authentic learning experiences and for the opportunity of providing active learning. However, virtual worlds need more educational support to be more inhabited with increased learning services. Incorporating intelligent pedagogical agents into virtual worlds adds such learning support by adding intelligence, improving believability, and the opportunity to increase communication with an artificial educator. However the implementation of intelligent pedagogical agents and adopting them in a virtual world require several efforts with different aspects of implementation. This paper reports our first prototype implementation of an IPA interacting with a learner and a learning object in natural science experiment in a virtual world while providing supporting multi-modal communication abilities. The IPA has features of text chat based on the Artificial Intelligence Markup Language (AIML), a text-to-speech synthesis function, and non-verbal communication abilities through gesture animation. The implementation is presented through explained scenarios of the IPA tutoring an experiment or monitoring a learner avatar interaction with a learning object in a Virtual World. The IPA & the learning scenarios are implemented in the open source of Open Wonderland

Agent Assistance: From Problem Solving to Music Teaching

We report on our research on agents that act and behave in a web learning environment. This research is part of a general approach to agents acting and behaving in virtual environments where they are involved in providing information, performing transactions, demonstrating products and, more generally, assisting users or visitors of the web environment in doing what they want or have been asked to do. While initially we hardly provided our agents with 'teaching knowledge', we now are in the process of making such knowledge explicit, especially in models that take into account that assisting and teaching takes place in a visualized and information-rich environment. Our main (embodied) tutor-agent is called Jacob; it knows about the Towers of Hanoi, a well-known problem that is offered to CS students to learn about recursion. Other agents we are working on assist a visitor in navigating in a virtual world or help the visitor in getting information. We are now designing a music teacher - using knowledge of software engineering and how to design multi-modal interactions, from previous projects

CHRYSAOR: an Agent-Based Intelligent Tutoring System in Virtual Environment

11 pagesInternational audienceThe various existing Intelligent Tutoring Systems (ITS) models do not capitalize on all the possibilities permitted by the use of virtual reality. In this paper, we first establish the important characteristics of ITS (genericity, modularity, individualization, scenario edition, adaptativity). Subsequently we present our studies using an agent metamodel (Behave) based on an environment metamodel (Veha), in order to make a generic ITS. We focus on describing our agent model and its knowledge of the pedagogical situation and incorporate a pedagogical scenario model in our ITS. The use of this ITS is illustrated by an application of a virtual biomedical analyzer which enables to learn the technical procedures of the device

Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System

Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System Roya Aminikia Learning Management Systems (LMSs) are digital frameworks that provide curriculum, training materials, and corresponding assessments to guarantee an effective learning process. Although these systems are capable of distributing the learning content, they do not support dynamic learning processes and do not have the capability to communicate with human learners who are required to interact in a dynamic environment during the learning process. To create this process and support the interaction feature, LMSs are equipped with Intelligent Tutoring Systems (ITSs). The main objective of an ITS is to facilitate students’ movement towards their learning goals through virtual tutoring. When equipped with ITSs, LMSs operate as dynamic systems to provide students with access to a tutor who is available anytime during the learning session. The crucial issues we address in this thesis are how to set up a dynamic LMS, and how to design the logical structure behind an ITS. Artificial intelligence, multi-agent technology and machine learning provide powerful theories and foundations that we leverage to tackle these issues. We designed and implemented the new concept of Pedagogical Agent (PA) as the main part of our ITS. This agent uses an evaluation procedure to compare each particular student, in terms of performance, with their peers to develop a worthwhile guidance. The agent captures global knowledge of students’ feature measurements during students’ guiding process. Therefore, the PA retains an updated status, called image, of each specific student at any moment. The agent uses this image for the purpose of diagnosing students’ skills to implement a specific correct instruction. To develop the infrastructure of the agent decision making algorithm, we laid out a protocol (decision tree) to select the best individual direction. The significant capability of the agent is the ability to update its functionality by looking at a student’s image at run time. We also applied two supervised machine learning methods to improve the decision making protocol performance in order to maximize the effect of the collaborating mechanism between students and the ITS. Through these methods, we made the necessary modifications to the decision making structure to promote students’ performance by offering prompts during the learning sessions. The conducted experiments showed that the proposed system is able to efficiently classify students into learners with high versus low performance. Deployment of such a model enabled the PA to use different decision trees while interacting with students of different learning skills. The performance of the system has been shown by ROC curves and details regarding combination of different attributes used in the two machine learning algorithms are discussed, along with the correlation of key attributes that contribute to the accuracy and performance of the decision maker components

Construals as a complement to intelligent tutoring systems in medical education

This is a preliminary version of a report prepared by Meurig and Will Beynon in conjunction with a poster paper "Mediating Intelligence through Observation, Dependency and Agency in Making Construals of Malaria" at the 11th International Conference on Intelligent Tutoring Systems (ITS 2012) and a paper "Construals to Support Exploratory and Collaborative Learning in Medicine" at the associated workshop on Intelligent Support for Exploratory Environments (ISEE 2012). A final version of the report will be published at a later stage after feedback from presentations at these events has been taken into account, and the experimental versions of the JS-EDEN interpreter used in making construals have been developed to a more mature and stable form

A multi-agent system model to integrate Virtual Learning Environments and Intelligent Tutoring Systems

Virtual learning environments (VLEs) are used in distance learning and classroom teaching as teachers and students support tools in the teaching–learning process, where teachers can provide material, activities and assessments for students. However, this process is done in the same way for all the students, regardless of their differences in performance and behavior in the environment. The purpose of this work is to develop an agent-based intelligent learning environment model inspired by intelligent tutoring to provide adaptability to distributed VLEs, using Moodle as a case study and taking into account student's performance on tasks and activities proposed by the teacher, as well as monitoring his/her study material access