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.

An Intelligent Tutoring System for Learning Java

Abstract: Java is one of the most widely used languages in Desktop developing, Web Development and Mobile Development, so there are many lessons that explain its basics, so it should be an intelligent tutoring system that offers lessons and exercises for this language. Why tutoring system? Simply because it is one-one teacher, adapts with all the individual differences of students, begins gradually with students from easier to harder level, save time for teacher and student, the student is not ashamed to make mistakes, and more. In this paper, we describe the design of an Intelligent Tutoring System for teaching Java to help students learn Java easily and smoothly. Tutor provides beginner level in Java. Finally, we evaluated our tutor and the results were excellent by students and teachers.

A Kohonen Network for Modeling Students' Learning Styles in Web 2.0 Collaborative Learning Systems

Abstract: The identification of the best learning style in an Intelligent Tutoring System must be considered essential as part of the success in the teaching process. In many implementations of automatic classifiers finding the right student learning style represents the hardest assignment. The reason is that most of the techniques work using expert groups or a set of questionnaires which define how the learning styles are assigned to students. This paper presents a novel approach for automatic learning styles classification using a Kohonen network. The approach is used by an author tool for building Intelligent Tutoring Systems running under a Web 2.0 collaborative learning platform. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the author tool.

Cognitive task analysis, interface design, and technical troubleshooting

Abstract: We propose a model of interface design that makes use of two interdependent levels of cognitive analysis: 1) the study of the criterion task through an analysis of expert/novice differences and; 2) the application of a GOMS analysis to a working interface design. We review this dual analysis in the context of HYDRIVE, a video-disc based intelligent tutoring system designed to facilitate the development of troubleshooting skills for aircraft hydraulics systems. The initial cognitive task analysis enabled the identification of criticat troubleshooting skills and troubleshooting procedures. We find, though, that even with an in-depth initial cognitive task analysis, the GOMS interface analysis resulted in significant and beneficial design changes.

Special issue: best of ITS 2010

Abstract: The current special issue and its companion issue, which is to follow, present the “Best of ITS 2010.” The articles included in these two issues are extended versions of the eight papers ranked highest by the reviewers of the 10th International Conference on Intelligent Tutoring Systems (ITS 2010). We, as Programme co-Chairs for ITS 2010 and the Special Issue editors, are very pleased to say that all authors invited to submit to the special issue responded positively to our request. All of them submitted expanded versions of their conference papers, which then underwent careful peer review for the journal, resulting in further revisions and the articles that you now see. As the Best of ITS 2010, these articles represent an excellent snapshot of where the field of AI in Education (AIED) currently is, and where it is heading. All eight articles in this special issue fall under the broad AIED umbrella. Beyond that simple fact, they defy simple categorizations; they represent diverse technologies and a range of domains. At the same time, we do see important themes and trends, the most striking of which is that all articles involved extensive analysis of data about student student learning with advanced learning technologies. This fact may come as no surprise to those who have followed research in AIED in recent years, but it illustrates vividly that our field is about development of advanced learning technologies while also striving to be an empirical science about how technology can best support and enhance human learning. In line with this overarching theme, many articles in the special issue touch on educational data mining (EDM). In addition, the following themes are represented: student modeling, dialogue analysis and dialogue systems, educational games, hybrid teaching systems, authoring (and automation thereof, using data), and evaluation. We briefly review how the papers exemplify these themes. Educational data mining (EDM). This relatively new area is concerned with developing and applying methods to explore data from educational settings to better understand students and the settings in which they learn (paraphrased from http://www.educationaldatamining.org/). Under this definition, five out of the eight articles in this special issue (and arguably even all eight) touch on EDM. These articles reflect the emergence of increasing amounts of learning data that is ripe for exploitation and, in parallel, a growth of work in new techniques for exploiting that data. Two of these articles present EDM work to develop and refine techniques for student modeling,which has long been a cornerstone of AIED research, fundamental to the goal of personalisation. The article by Baker, Goldstein, and Heffernan frames and tackles an interesting and important problem: is it possible to look at an instructional event (such as a student’s solving a problem step in an intelligent tutoring system) and at the very moment that the event happens predict how much learning it produces? Baker et al. addressed this

An Algebra Subsystem for Diagnosing Students' Input in a Physics Tutoring System

Abstract: To help a student in an introductory physics course do quantitative homework problems, an intelligent tutoring system must determine information of an algebraic nature. This paper describes a subsystem which resolves such questions for Andes2. The capabilities of the subsystem would be useful for any ITS which deals with problems involving complex systems of equations. This subsystem is capable of 1) solving the systems of equations at the level of introductory physics problems, 2) checking the validity of equations the students enter, 3) investigating whether an equation is independent from a set of other equations, and if not, determining on which equations it does depend, and finally 4) providing tools to help the student with algebraic manipulations, including a "solve-tool" that solves her equations. The ability to determine dependence of equations is first used by Andes during problem generation, by providing information to that component of the ITS which generates correct solutions to the problem. Later, during tutoring, it enables the help module to model which equations the student appears to know. One new feature of this algebra subsystem is that it deals with the dimensional units of physical quantities throughout. An important change from a previous approach is in the meaning of "correctness" of an equation and in the method of determining which equations it can be derived from. The theoretical differences between the two methods, and the pros and cons of each, are discussed. Then we evaluate how the capabilities of the subsystem have affected the Andes tutor's effectiveness, with a particular emphasis on the effects of the changed method.