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.

Knowledge-based web-enabled agents and intelligent tutoring systems

Abstract: Intelligent tutoring systems have been in existence for decades, and their characteristics can be beneficially applied in environments utilizing information and communication technology (ICT). The "intelligence" in these systems is seen through the way these systems adapt themselves to the characteristics of the students, such as speed of learning, specific areas in which the student excels as well as falls behind, and rate of learning as more knowledge is learned. In such intelligent learning environments, the agent or set of agents can be modeled to perform pedagogical tasks. This paper considers the necessary characteristics that constitute a good intelligent tutoring system. This paper introduces a framework incorporating an incremental machine-learning approach to capture 1) the dynamics of knowledge creation in the domain of interest and 2) the learned-knowledge content of the student over time. Some of the components of the proposed system are illustrated using examples from an introductory course on database design.

Toward legal argument instruction with graph grammars and collaborative filtering techniques

Abstract: This paper presents an approach for intelligent tutoring in the field of legal argumentation. In this approach, students study transcripts of US Supreme Court oral argument and create a graphical representation of argument flow as tests offered by attorneys being challenged by hypotheticals posed by Justices. The proposed system, which is based on the collaborative modeling framework Cool Modes, is capable of detecting three types of weaknesses in arguments; when it does, it presents the student with a self explanation prompt. This kind of feedback seems more appropriate than the “strong connective feedback” typically offered by model-tracing or constraint-based tutors. Structural and context weaknesses in arguments are handled by graph grammars, and the critical problem of detecting and dealing with content weaknesses in student contributions is addressed through a collaborative filtering approach, thereby avoiding the critical problem of natural language processing in legal argumentation. An early version of the system was pilot tested with two students.

Predicting Learning from Student Affective Response to Tutor Questions

Abstract: Modeling student learning during tutorial interaction is a central problem in intelligent tutoring systems. While many modeling techniques have been developed to address this problem, most of them focus on cognitive models in conjunction with often-complex domain models. This paper presents an analysis suggesting that observing students' multimodal behaviors may provide deep insight into student learning at critical moments in a tutorial session. In particular, this work examines student facial expression, electrodermal activity, posture, and gesture immediately following inference questions posed by human tutors. The findings show that for human-human task-oriented tutorial dialogue, facial expression and skin conductance response following tutor inference questions are highly predictive of student learning gains. These findings suggest that with multimodal behavior data, intelligent tutoring systems can make more informed adaptive decisions to support students effectively.

A problem solving oriented intelligent tutoring system to improve students' acquisition of basic computer skills

Abstract: Personalization and intelligent tutor are two key factors in the research on learning environment. Intelligent tutoring system (ITS), which can imitate the human teachers' actions to implement one-to-one personalized teaching to some extent, is an effective tool for training the ability of problem solving. This research firstly discusses the concepts and methods of designing problem solving oriented ITS, and then develops the current iTutor based on the extended model of ITS. At last, the research adopts a quasi-experimental design to investigate the effectiveness of iTutor in skills acquisition. The results indicate that students in iTutor group experience better learning effectiveness than those in the control group. iTutor is found to be effective in improving the learning effectiveness of students with low-level prior knowledge. We model a problem solving oriented ITS architecture.We develop iTutor to support skills acquisition in real-life problem situation.A quasi-experimental was designed to investigate the effectiveness of iTutor.iTutor can better facilitate skills acquisition.iTutor can improve the skill learning effect of low-level prior knowledge.