Tutor Agent Research Articles

EFFICIENT PERSONALIZATION OF E‐LEARNING ACTIVITIES USING A MULTI‐DEVICE DECENTRALIZED RECOMMENDER SYSTEM

Personalization is becoming a key issue in designing effective e‐learning systems and, in this context, a promising solution is represented by software agents. Usually, these systems provide the student with a student agent that interacts with a site agent associated with each e‐learning site. However, in presence of a large number of students and of e‐learning sites, the tasks of the agents are often onerous, even more if the student agents run on devices with limited resources. To face this problem, we propose a new multiagent learning system, called ISABEL. Our system provides each student, that are using a specific device, with a device agent able to autonomously monitor the student's behavior when accessing e‐learning Web sites. Each site is associated, in its turn, with a teacher agent. When a student visits an e‐learning site, the teacher agent collaborates with some tutor agents associated with the student, to provide him with useful recommendations. We present both theoretical and experimental results to show that this distributed approach introduces significant advantages in quality and efficiency of the recommendation activity with respect to the performances of other past recommenders.

Agent based affective tutoring systems: A pilot study

An important trend in the development of Intelligent tutoring systems (ITSs) has been that providing the student with a more personalized and friendly environment for learning. Many researchers now feel strongly that the ITSs would significantly improve performance if they could adapt to the affective state of the learner. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. However, ATSs are not widely employed in the tutoring system market. In this paper, a survey was conducted to investigate the critical factors affecting learner’s satisfaction in ATSs based on an ATS developed by us. The results revealed that learner’s attitude toward affective computing, agent tutor’s expressiveness, emotion recognition accuracy, number of emotions recognized by agent tutor, pedagogical action and easy of the use of the system have significant influence on learner’s satisfaction. The results indicate institutions how to further strengthen the ATSs’ implementation.

Improving the Behavior of Intelligent Tutoring Agents with Data Mining

An extended sequential pattern- mining algorithm combined with association-rule- discovery techniques can extract temporal patterns and relationships from human agents.

An Interactive Web-Based Environment Using Pedagogical Agents

This paper describes the architecture of an Interactive Learning Environment (ILE) on the Internet using companions, including a human one geographically distant from the learning site. The achieved system relies on 3-tiers Client/Server architecture (Client, Web Server, Data and applications Server) where human and software agents can communicate via the Internet and use the DTL strategy (Double Test Learning). The architecture contains five main agents: a tutor agent in charge of guiding the learner; a system agent whose role is to manage and to control the accesses to the system; a teacher agent in charge of the management and the updating of the different bases; a learner agent representing the main agent of the system for whom the teaching is dedicated, and finally a learning companion agent whose role can sometimes be as an assistant, and other times, as a troublemaker.

Virtual knowledge modelling for distributed teams: towards an interactive design approach

A distributed framework for knowledge modelling in interactive design will be presented in this article. A multi-agent approach is proposed in order to support distributed partners in the creation of coherent models for engineering analysis. A granular modelling process is proposed to ensure a coherent construction of models and their analysis. It consists of a global and a local analysis based on a networked Product Life Cycle (PLC). A set of 'Tutor Agents' (TA) intends to guide each partner to extract relevant information from his/her technical domain while a 'Standardisation Process' (SP) is performed with supplied information to find potential redundancies. A coherent model is then obtained that is ready to be analysed by traditional inference engines such as optimisation solvers and constraint satisfaction programming. Furthermore, a case will be presented in order to clarify the framework. The results will enable design teams to integrate constraints from throughout the PLC in order to optimise the evolution of design concepts in distributed design.

A Tutor Agent for supporting distributed knowledge modelling in interactive product design

Currently, the process of problem analysis in product design involves multiple partners in a distributed environment. A tutoring approach to support knowledge modelling will be presented in this article, focusing on variables as a key component of knowledge. A set of Tutor Agents intends to aid distributed partners to coherently elicitate their know-how in order to use to analyse design problems. A granular Knowledge Base is proposed to standardise distributed knowledge, providing Product Life-Cycle (PLC) constraints (based on product knowledge) to construct optimisation models. Furthermore, a case will be presented in order to clarify the approach. The results will enable design teams to integrate constraints from PLC knowledge in order to optimise the evolution of design concepts.

Designing for metacognition—applying cognitive tutor principles to the tutoring of help seeking

Intelligent Tutoring Systems have been shown to be very effective in supporting learning in domains such as mathematics, physics, computer programming, etc. However, they are yet to achieve similar success in tutoring metacognition. While an increasing number of educational technology systems support productive metacognitive behavior within the scope of the system, few attempt to teach skills students need to become better future learners. To that end, we offer a set of empirically-based design principles for metacognitive tutoring. Our starting point is a set of design principles put forward by Anderson et al. (Journal of the Learning Sciences, 4:167–207, 1995) regarding Cognitive Tutors, a family of Intelligent Tutoring Systems. We evaluate the relevance of these principles to the tutoring of help-seeking skills, based on our ongoing empirical work with the Help Tutor. This auxiliary tutor agent is designed to help students learn to make effective use of the help facilities offered by a Cognitive Tutor. While most of Anderson’s principles are relevant to the tutoring of help seeking, a number of differences emerge as a result of the nature of metacognitive knowledge and of the need to combine metacognitive and domain-level tutoring. We compare our approach to other metacognitive tutoring systems, and, where appropriate, propose new guidelines to promote the discussion regarding the nature and design of metacognitive tutoring within scaffolded problem-solving environments.

AgentGeom: a multiagent system for pedagogical support in geometric proof problems

This paper aims, first, to describe the fundamental characteristics and workings of the AgentGeom artificial tutorial system, which is designed to help students develop knowledge and skills related to problem solving, mathematical proof in geometry, and the use of mathematical language. Following this, we indicate the manner in which a secondary school student can appropriate these abilities through interactions with the system. Our system uses strategic messages of the agent tutor in an argumentative process that collaborates with a student in the construction of a proof.

Research on humanised web-based learning model

Web technology has provided numerous significant educational opportunities and web-based education services have spread throughout the world. However, the web has brought debates as well as opportunities. According to the constructivism educational theory, the external learning environment and learner's mentality must be regarded in a web-based learning model. Students' psychology sub-model is used to describe the learner's mental states and the transformations among them. Two categories of mentality (positive and negative), subdivided further into five classes, are proposed. Collaborative learning sub-model is used to describe the interactions between the learner and the learning environment, which includes tutor agents, other learners and basic knowledge resource.

A Category-based Framework of a Self-improving Instructional Planner

To have an instructional plan guide the learning process is significant to various teaching styles and an important task in an ITS. Though various approaches have been used to tackle this task, the compelling need is for an ITS to improve on its own the plans established in a dynamic way. We hypothesize that the use of knowledge derived from student categories can significantly support the improvement of plans on the part of the ITS. This means that category knowledge can become effectors of effective plans. We have conceived a Category-based Self-improving Planning Module (CSPM) for an ITS tutor agent that utilizes the knowledge learned from learner categories to support self-improvement. The learning framework of CSPM employs unsupervised machine learning and knowledge acquisition heuristics for learning from experience. We have experimented on the feasibility of CSPM using recorded teaching scenarios.

AFFECT IN TUTORING DIALOGUES

ABSTRACT This paper is about INES, an intelligent, multimodal tutoring environment, and how we build a tutor agent in the environment that tries to be sensitive to the mental state of the student that interacts with it. The environment was primarily designed to help students practice nursing tasks. For example, one of the implemented tasks is to give a virtual patient a subcutaneous injection. The students can interact multimodally using speech and a haptic device under the guidance of the virtual embodied tutor. INES takes into account elements of the student's character and an appraisal of the student's actions to estimate the mental state of the student. This information is used to plan and execute the actions and responses of the tutor agent.

Expert tutoring system for teaching computer programming languages

This paper presents an Expert tutoring system (E-TCL) for teaching computer programming languages through WWW. In this version, many teachers can cooperate together to put the curriculum of one/more computer programming language(s). Their contributions may include: (a) add or modify the commands' structure that will be taught; (b) generate different tutoring dialogs for the same command; and (c) generate different tutoring styles. On the contrary, the students can access the system through WWW, select any language they want to learn as well as the style of presentation they prefer and they can exchange their experiences. A personal assistant agent for teachers (PAA-T), a personal assistant agent for students (PAA-S) with an adaptive interface, and tutoring agent (TA) has been built. The TA resides on the server side and communicates via HTTP and IIOP with both the PAA-T and PAA-S on the clients side. This structure allows customization of the PAA-T and PAA-S to the needs of the teachers and students, without putting extra burden on the server. In addition, this allows having many teacher agents attending to the needs of a single or multiple student agent(s).

A multi-agent architecture for teaching dermatology.

This work proposes the integration of computer-aided instruction systems in the curricula of medical education, and describes an intelligent tutoring system used for teaching Dermatology. The Dermatology Tutor uses a self-organized society of autonomous software agents which have different capabilities or roles. The society contains tutor, medical and information agents which participate in the tutoring process and collaborate through deliberation in order to achieve a tutoring task. The agents are built according to a BDI architecture, which implements the mental attitudes of beliefs (B), desires (D) and intentions (I). Each medical agent is a specialist in a medical field, while a tutoring agent, which implements a widely accepted dermatology teaching process, coordinates the overall operation of the system. Depending on the subject that is to be taught during any session, the tutoring agent forms teams of medical agents, which in turn use search agents to retrieve information. Although the presented multi-agent architecture is dedicated to teaching dermatology (since the tutor agent is specialized in Dermatology), it can be extended to other domains also with the incorporation of other tutor agents.