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 Overview of Intelligent Tutoring System Authoring Tools: Updated Analysis of the State of the Art

Abstract: This paper consists of an in-depth summary and analysis of the research and development state of the art for intelligent tutoring system (ITS) authoring systems. A seven-part categorization of two dozen authoring systems is given, followed by a characterization of the authoring tools and the types of ITSs that are built for each category. An overview of the knowledge acquisition and authoring techniques used in these systems is given. A characterization of the design tradeoffs involved in building an ITS authoring system is given. Next the pragmatic questions of real use, productivity findings, and evaluation are discussed. Finally, I summarize the major unknowns and bottlenecks to having widespread use of ITS authoring tools.

Learning styles diagnosis based on user interface behaviors for the customization of learning interfaces in an intelligent tutoring system

Abstract: Each learner has different preferences and needs. Therefore, it is very crucial to provide the different styles of learners with different learning environments that are more preferred and more efficient to them. This paper reports a study of the intelligent learning environment where the learner's preferences are diagnosed, and then user interfaces are customized in an adaptive manner to accommodate the preferences. A learning system with a specific interface has been devised based on the learning-style model by Felder & Silverman, so that different learner preferences are revealed through user interactions with the system. Using this interface, learning styles are diagnosed from learner behavior patterns on the interface using Decision Tree and Hidden Markov Model approaches.

Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration

Abstract: Intelligent tutoring systems are quite difficult and time inten- sive to develop. In this paper, we describe a method and set of software tools that ease the process of cognitive task analysis and tutor development by allowing the author to demonstrate, instead of programming, the behav- ior of an intelligent tutor. We focus on the subset of our tools that allow authors to create "Pseudo Tutors" that exhibit the behavior of intelligent tu- tors without requiring AI programming. Authors build user interfaces by di- rect manipulation and then use a Behavior Recorder tool to demonstrate al- ternative correct and incorrect actions. The resulting behavior graph is an- notated with instructional messages and knowledge labels. We present some preliminary evidence of the effectiveness of this approach, both in terms of reduced development time and learning outcome. Pseudo Tutors have now been built for economics, analytic logic, mathematics, and language learn- ing. Our data supports an estimate of about 25:1 ratio of development time to instruction time for Pseudo Tutors, which compares favorably to the 200:1 estimate for Intelligent Tutors, though we acknowledge and discuss limitations of such estimates.

Procedural help in Andes: generating hints using a Bayesian network student model

Abstract: One of the most important problems for an intelligent tutoring system is deciding how to respond when a student asks for help. Responding cooperatively requires an understanding of both what solution path the student is pursuing, and the student's current level of domain knowledge. Andes, an intelligent tutoring system for Newtonian physics, refers to a probabilistic student model to make decisions about responding to help requests. Andes' student model uses a Bayesian network that computes a probabilistic assessment of three kinds of information: (I) the student's general knowledge about physics, (2) the student's specific knowledge about the current problem, and (3) the abstract plans that the student may be pursuing to solve the problem. Using this model, Andes provides feedback and hints tailored to the student's knowledge and goals.

Motivation and performance in a game-based intelligent tutoring system.

Abstract: One strength of educational games stems from their potential to increase students’ motivation and engagement during educational tasks. However, game features may also detract from principle learning goals and interfere with students’ ability to master the target material. To assess the potential impact of game-based learning environments, in this study we examined motivation and learning for 84 high-school students across eight 1-hr sessions comparing 2 versions of a reading strategy tutoring system, an intelligent tutoring system (iSTART) and its game-based version (iSTART–ME). The results demonstrate equivalent target task performance (i.e., learning) across environments at pretest, posttest, and retention, but significantly higher levels of enjoyment and motivation for the game-based system. Analyses of performance across sessions reveal an initial decrease in performance followed by improvement within the game-based training condition. These results suggest possible constraints and benefits of game-based training, including time-scale effects. The findings from this study offer a potential explanation for some of the mixed findings within the literature and support the integration of game-based features within intelligent tutoring environments that require long-term interactions for students to develop skill mastery.