Intelligent tutoring system

About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.

Intelligent Tutoring Systems

Abstract: Intelligent Tutoring Systems (ITS) are meant to provide useful tutoring services for assisting the student. These services include coaching, assisting, guiding, helping, and tracking the student during problem-solving situations. To offer high-quality tutoring services, an ITS must be able to establish the correct student profile, then understand and diagnose the student cognitive as well as its affective state. This special issue of Educational Technology & Society presents recent works dealing with those matters. Extracting Procedural Models Using Educational Data Mining The main goal of an intelligent tutoring system is to actively provide guidance to the student in problem-solving situations. Relevant feedback should be founded on a thorough understanding and diagnosis of student responses. Building such understanding and diagnosis model is a difficult issue that is also a time-intensive process involving human experts. This issue becomes even more difficult in ill-defined domains where an explicit representation of the training task is hard, if not impossible, to set up. Educational data-mining (EDM) brings some promising solutions to this issue. You will find in this special issue two EDM-based solutions proposed for coping with this problem. Each of these solutions consists of a model that can constantly learn from new learner or user data and thus, guaranties that the tutor provides an up-to-date feedback. In one hand, Barnes and Stamper propose a novel application of Markov decision processes (MDPs) to automatically generate hints for an intelligent tutor that learns. This approach eases the process of building the understanding and diagnosis model of student actions. The authors extracted MDPs from four semesters of student solutions created in a logic proof tutor, and calculated the probability of being able to generate hints for students at any point in a given problem. The results indicate that extracted MDPs and their proposed hint-generating functions are able to provide hints over 80% of the time. The results also indicate that they can provide valuable tradeoffs between hint specificity and the amount of data used to create an MDP. In the other hand, Fournier-Viger et al. present a novel framework for adapting the behavior of intelligent agents based on human experts' data. The framework consists of an extended sequential pattern-mining algorithm that, in combination with association rule discovery techniques, is used to extract temporal patterns and relationships from the behavior of human learners of multiple profiles, executing a procedural task. The proposed framework has been integrated within CanadarmTutor, an intelligent tutoring system aimed at helping students solve procedural problems that involve moving a robotic arm in a complex virtual environment. CanadarmTutor acts in an ill-defined domain where the problem space associated with a given task consists of an infinite number of paths. The framework was used to improve the behavior of a cognitive agent that adapts its decision by learning from data gathered during past cognitive cycles. …

Responding to Unexpected Student Utterances in CIRCSIM-Tutor v.3: Analysis of Transcripts

Abstract: CIRCSIM-Tutor is a dialogue-based intelligent tutoring system that conducts dialogues with medical students about blood pressure regulation. To obtain models for computergenerated dialogues, we analyzed dialogues involving expert human tutors. In this paper we describe some of the interesting and complex patterns we isolated from the human tutorial dialogues in cases where the student gave erroneous or otherwise unexpected results.

Adaptivity in a learning companion system

Abstract: In the last few years, there is an increasing interest in the development of educational applications that seek to improve the teaching and learning process in distance education. This paper presents LeCo-EAD, a learning companion system for distance education. A learning companions system is an intelligent tutoring system that includes an additional component: a learning companion. LeCo-EAD uses the information about a particular student in order to adapt diverse aspects of its functionalities to the students' individual needs. The system user-adapted interaction is accomplished through the choice of the kind of learning companion, the support provided to the conceptual maps navigation, and the kind of feedback messages used.

A Remote and Virtual Lab with Experiments for Secondary Education, Engineering and Lifelong Learning Courses

Abstract: The Information and Communication Technology tools are nowadays invaluable to support e-learning and b-learning programs. The Remote and Virtual Laboratory in development at the Department of Informatics Engineering of the University of Coimbra (Portugal), RVL@DEI-UC, is a web-based platform that allows users to perform a large set of experiments in different areas and contexts, such as in education or training. This paper aims to describe the inherent potential of this platform in secondary education, engineering and lifelong learning courses. The conceptualization, architecture and implementation of the web platform for real and virtual experiments, which is remotely accessed using the Internet, are presented and the relevance of the labâ??s integration in an intelligent tutoring system is also highlighted, mainly in what regards the requirements of adaptation and customization to different usersâ?? profile in different learning contexts.

The Physics Tutor: Integrating Hypertext and Expert Systems

Abstract: The Physics Tutor is a prototype intelligent tutoring system (ITS) built with “off-the-shelf” software in order to test the utility, practicality, and generalizability of the ITS concept. The tutorial information is accessed through a semantically structured hypertext which enables students to browse through the knowledge base, secure examples, and quiz themselves with practice items. The student model and expert model are encoded in several hundred rules that are evaluated by a commercial expert system shell. The output of the expert system knowledge bases is a judgment about the learners level of conceptual development and some tutorial strategies for remediating deficiencies. Because of the slowness of the system, and the enormous amount of required development time, the practicality of ITSs is called into question.