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.

J-Latte: a Constraint-Based Tutor for Java

Abstract: We present J-LATTE, a constraint-based intelligent tutoring system that teaches a subset of the Java programming language. J-LATTE supports two modes: concept mode, in which the student designs the program without having to specify contents of statements, and coding mode, in which the student completes the code. We present the style of interaction with J-LATTE, its interface, domain model and the student modeling approach. We also report the results of a study we conducted in an introductory programming course. Although we did not have enough participants to obtain statistical significance, the results show very promising trends indicating that students learned the constraints.

Rapid Authoring of Intelligent Tutors for Real-World and Experimental Use

Abstract: Authoring tools for Intelligent Tutoring Systems are especially valuable if they not only provide a rich set of options for the efficient authoring of tutoring systems but also support controlled experiments in which the added educational value of new tutor features is evaluated. The Cognitive Tutor Authoring Tools (CTAT) provide both. Using CTAT, real-world "Example-Tracing Tutors" can be created without programming. CTAT also provides various kinds of support for controlled experiments, such as administration of different experimental treatments, logging, and data analysis. We present two case studies in which Example-Tracing Tutors created with CTAT were used in classroom experiments. The case studies illustrate a number of new features in CTAT: Use of Macromedia Flash MX 2004 for creating tutor interfaces, extensions to the Example-Tracing Engine that allow for more flexible tutors, a Mass Production facility for more efficient template-based authoring, and support for controlled experiments.

My science tutor: A conversational multimedia virtual tutor for elementary school science

Abstract: This article describes My Science Tutor (MyST), an intelligent tutoring system designed to improve science learning by students in 3rd, 4th, and 5th grades (7 to 11 years old) through conversational dialogs with a virtual science tutor. In our study, individual students engage in spoken dialogs with the virtual tutor Marni during 15 to 20 minute sessions following classroom science investigations to discuss and extend concepts embedded in the investigations. The spoken dialogs in MyST are designed to scaffold learning by presenting open-ended questions accompanied by illustrations or animations related to the classroom investigations and the science concepts being learned. The focus of the interactions is to elicit self-expression from students. To this end, Marni applies some of the principles of Questioning the Author, a proven approach to classroom conversations, to challenge students to think about and integrate new concepts with prior knowledge to construct enriched mental models that can be used to explain and predict scientific phenomena. In this article, we describe how spoken dialogs using Automatic Speech Recognition (ASR) and natural language processing were developed to stimulate students' thinking, reasoning and self explanations. We describe the MyST system architecture and Wizard of Oz procedure that was used to collect data from tutorial sessions with elementary school students. Using data collected with the procedure, we present evaluations of the ASR and semantic parsing components. A formal evaluation of learning gains resulting from system use is currently being conducted. This paper presents survey results of teachers' and children's impressions of MyST.

Addressing the testing challenge with a web-based e-assessment system that tutors as it assesses

Abstract: Secondary teachers across the country are being asked to use formative assessment data to inform their classroom instruction. At the same time, critics of No Child Left Behind are calling the bill "No Child Left Untestedo emphasizing the negative side of assessment, in that every hour spent assessing students is an hour lost from instruction. Or does it have to be? What if we better integrated assessment into the classroom, and we allowed students to learn during the test? Maybe we could even provide tutoring on the steps of solving problems. Our hypothesis is that we can achieve more accurate assessment by not only using data on whether students get test items right or wrong, but by also using data on the effort required for students to learn how to solve a test item. We provide evidence for this hypothesis using data collected with our E-ASSISTment system by more than 600 students over the course of the 2004-2005 school year. We also show that we can track student knowledge over time using modern longitudinal data analysis techniques. In a separate paper [9], we report on the ASSISTment system's architecture and scalability, while this paper is focused on how we can reliably assess student learning.

An intelligent tutoring system for circuit analysis

Abstract: The Interactive Multimedia Intelligent Tutoring System (IMITS) is designed to assist electrical engineering undergraduate students taking their first circuits courses. The IMITS system places the student in a real-life engineering scenario in which the student is a newly hired engineer within the fictional IMITS Corporation and given "real-life" problems to solve, corresponding to course material. The office has file cabinets, bookshelves, a printer, and a personal computer. The personal computer allows the student to receive televideo messages, receive "e-mail", and send "e-mail" reports to senior engineers. A feature of IMITS is that the student decides which actions to take and may validate analyses and designs using a virtual laboratory incorporated with the software. A brief historical perspective of intelligent tutoring systems is presented, followed by an explanation of their architecture. Next, a detailed discription of the intelligent tutoring system IMITS is given. Then the results of usability and effectiveness evaluations of the software are given