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.

The Massive User Modelling System (MUMS)

Abstract: Effective distributed user modelling in intelligent tutoring systems requires the integration of both pedagogical and domain applications. This integration is difficult, and often requires rebuilding applications for the specific e-learning environment that has been deployed. This paper puts forth both an architecture and an implementation prototype for achieving this integration. It focuses on providing platform and language neutral access to services, without commitment to any particular ontology.

Game-Based Practice in a Reading Strategy Tutoring System: Showdown in iSTART-ME

Abstract: Many contend that the future of affordable, high-quality education lies in harnessing the potential of computer technologies. While implementing computer technologies in schools has had both failings and challenges (Dynarski et al., 2007), significant progress in the quality of education to some extent depends on our ability to leverage the many advantages of computer technologies. Computer technologies enable adaptive, one-on-one tutoring to virtually all students in the classroom. The most common goal of these one-on-one intelligent tutoring systems (ITSs) is to produce learning gains. Two of the most common areas of learning address content within specific domains (e.g., physics) or cognitive skill acquisition (e.g., strategies to improve reading comprehension). Both types of learning are often characterized by exposure to declarative information and subsequent interaction with the material (Anderson, 1982). However, acquiring a new skill usually requires a significant commitment to continued practice and application. Skills are often developed and improved with practice over an extended period of time (Newell & Rosenbloom, 1981).

Integrating iSTART into a High School Curriculum

Abstract: Integrating iSTART into a High School Curriculum Courtney M. Bell (cbell@mail.psyc.memphis.edu) Danielle S. McNamara (d.mcnamara@mail.psyc.memphis.edu) Institute for Intelligent Systems University of Memphis Memphis, TN 38152 Active Reading and Thinking (iSTART) into their science classrooms and curriculum. Abstract This study examines the viability in the classroom of a tutoring system called Interactive Strategy Training for Active Reading and Thinking (iSTART). This study investigated the effects of teacher-guided and iSTART- guided extended practice (following initial reading strategy training with iSTART) including 78 high school students from four biology classes. Eight high school teachers were also trained to administer reading strategy training. The results supported the conclusion that teachers can successfully integrate educational technology in such a way that it compliments their traditional teaching roles while helping to meet the needs of their students. Intelligent Tutoring Systems Intelligent tutoring systems are part of the advancements in educational technology aimed at promoting student- centered learning. As an outgrowth of earlier computer- aided instruction systems (Lajoie & Azevedo, 2005), intelligent tutoring systems are designed to capitalize on the power of one-to-one tutoring. Research by Cohen, Kulik, and Kulik (1982) indicates that when compared to traditional classroom instruction, one-to-one tutoring by untrained tutors, such as peers, cross-age tutors, or paraprofessionals, can produce an effect size for learning of .4 sigma (i.e., .4 standard deviations). Research using trained tutors suggests varying effects. For example, Bloom (1984) reported an effect size of 2 sigma (2.0 standard deviations) for math skills training, whereas VanLehn and colleagues (2007) reported an effect size of only 1 sigma (1.0 standard deviation) for physics tutoring. Despite these variations, research indicates that overall, one-to-one tutoring is effective in producing learning gains but impractical to implement on a large scale because of cost and time requirements. Intelligent tutoring systems can provide cost-effective one-to-one tutoring which is adaptable to individual students’ needs and personalizable (Tsiriga & Virvou, 2004). With the help of a student model that is dynamically monitored and updated using assessment tasks, intelligent tutors are able to provide students with adaptive feedback (Lajoie & Azevedo, 2005). This feedback guides students in correcting misconceptions and errors while helping them to effectively progress through the system (Graesser et al., 2004). iSTART is based on a human-delivered intervention called SERT (McNamara, 2004). It is an automated reading strategy trainer that teaches students to self- explain texts using five reading comprehensions strategies: comprehension monitoring, paraphrasing, elaboration, prediction, and bridging (McNamara et al., 2004). The system consists of both vicarious and interactive components to enhance learning and also consists of three phases: an introduction, demonstration, and practice, which are guided by pedagogical agents. In the introduction, students vicariously learn the five reading strategies by watching the instructor agent, Dr. Julie, use examples and definitions to teach two student agents, Mike and Julie. A short quiz follows each Keywords: Intelligent tutoring systems; iSTART; AutoTutor; classroom; curriculum; educational technology; artificial intelligence; education; science. Introduction As a result of educational reforms and new standards, educational goals and instructional methods are changing. Traditionally, school districts defined their goals and methods in terms of students being exposed to large amounts of declarative knowledge - the more, the better. And, students’ understanding and knowledge have generally been assessed in terms of explicit recall, primarily relying on fill in the blank and multiple choice tests: assessments that fail to assess deep level understanding. These techniques have often been supplemented by skill-based technologies centering on repetitive drills and practices that lack the benefits of feedback, adaptation, and knowledge construction. More recently, school districts have begun adopting instructional measures that train students to learn, train students in methods of research, help students develop the motivation to pursue personal enrichment, and train students to be creative and innovative (Domingo et al., 2002). Consequently, educational technology is also changing, and must change to adapt to the evolving understanding of how to enhance the learning process. Although policy-makers, educators, and researchers have invested large amounts of resources to meet the technological changes, successful integration at the rates hoped for has yet to occur for a number of reasons including a lack of teacher training as well as teachers’ fears of replacement and threats to their traditional roles. Thus, in this study, we examine how training influences teachers’ ability to successfully integrate an intelligent tutoring system called Interactive Strategy Training for

Advances in Intelligent Tutoring Systems: Problem-solving Modes and Model of Hints

Abstract: The paper focuses on the issues of providing an adaptive support for learners in intelligent tutoring systems when learners solve practical problems. The results of the analysis of support policies of learners in the existing intelligent tutor- ing systems are given and the revealed problems are emphasized. The concept and the architectural parts of an intelligent tutoring system are defined. The approach which provides greater adaptive abilities of systems of such kind offering two modes of problem-solving and using a two-layer model of hints is described. It is being implemented in the intelligent tutoring system for the Minimax algorithm at present. In accordance with the proposed approach the learner solves problems in the mode which is the most appropriate for him/her and receives the most suitable hint.

CAPTOR: a model for delivering Web-based intelligent tutoring system technology

Abstract: The goal of the Crisis Action Planning Tutored On-line Resource (CAPTOR) project is to design, develop, and implement a state-of-the-art, Internet-based course of instruction that utilizes Intelligent Tutoring System (ITS) technology. ITS technology is ideally suited to teach complex, cognitive tasks, such as those required for troubleshooting, problem-solving, and for resolving critical situations. As currently taught, Crisis Action Planning is part of an Armed Forces Staff College 12-week classroom course. It is projected that fully implementing CAPTOR as a distance learning ITS will dramatically reduce instructional training time.