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 q-matrix method of fault-tolerant teaching in knowledge assessment and data mining

Abstract: Fault tolerant teaching (FTT) systems are adaptive teaching systems that tolerate student, teacher, and system errors in diagnosing student misconceptions. These systems automatically assess student knowledge of the concepts underlying a tutorial topic, and use this assessment to direct remediation of knowledge. FTT methods use statistical techniques to interpret student responses to questions, and are constructed to tolerate the usual errors that occur during student testing—such as a student answering a question correctly without knowing how, or accidentally missing a question they understand well. These methods do not require any knowledge about the subject area being taught. In this dissertation, we implement the q-matrix method of FTT in three NovaNET tutorials, covering three topics and several levels of difficulty. During the course of the experiment, a q-matrix model was constructed to explain the relationship between tutorial questions and the concepts underlying these questions. The q-matrix model was then used to assess student knowledge of each concept, and to guide their remediation. The learning paths of self-guided students were compared to those prescribed by the FTT system, to determine if a student's self-assessment corresponds to that made by the system. We evaluate the q-matrix model in terms of interpretability and its correspondence to expert models of the topics. We also compare the q-matrix extraction method to other data mining techniques, such as cluster analysis and factor analysis. This dissertation resulted in the construction of a fully automated, fault tolerant, intelligent tutoring system, which can diagnose and correct student misconceptions. This system also provides a model for each topic that relates each tutorial question to its underlying concepts. The experimental analysis provides valuable insight into the factors that influence the extraction and interpretability of these models, as well as their value in automatically assessing student knowledge. In addition, the q-matrix method is used as a general data mining tool in one tutorial where a traditional application of the q-matrix method would not be appropriate. This application and its favorable comparison with other data mining tools mark the q-matrix method as a viable data clustering and interpretation tool for data mining.

Involving Effectively Teachers and Students in the Life Cycle of an Intelligent Tutoring System

Abstract: This paper highlights the important role that teachers and students may play in the life cycle of an intelligent tutoring system. In this research, we have developed a system called “EasyMath”, a tutoring system for Algebra that incorporates intelligence. One of the primary aims of EasyMath is to make it useful in school classrooms. This is why, school teachers of mathematics and their students have been involved throughout the life cycle of EasyMath. The system was developed following the rational unified process, an object-oriented methodology for developing software through multiple iterations. The design of EasyMath has been based on the results of an empirical study that was conducted at schools and the resulting product was evaluated by school teachers as well as students.

From Modelling Domain Knowledge to Metacognitive Skills: Extending a Constraint-Based Tutoring System to Support Collaboration

Abstract: Constraint-based tutors have been shown to increase individual learning in real classroom studies, but would become even more effective if they provided support for collaboration. COLLECT- $\mathcal{UML}$ is a constraint-based intelligent tutoring system that teaches object-oriented analysis and design using Unified Modelling Language. Being one of constraint-based tutors, COLLECT- $\mathcal{UML}$ represents the domain knowledge as a set of constraints. However, it is the first system to also represent a higher-level skill such as collaboration using the same formalism. We started by developing a single-user ITS. The system was evaluated in a real classroom, and the results showed that students' performance increased significantly. In this paper, we present our experiences in extending the system to provide support for collaboration as well as problem-solving. The effectiveness of the system was evaluated in a study conducted at the University of Canterbury in May 2006. In addition to improved problem-solving skills, the participants both acquired declarative knowledge about good collaboration and did collaborate more effectively. The results, therefore, show that Constraint-Based Modelling is an effective technique for modelling and supporting collaboration skills.

Feedback on Children's Stories via Multiple Interface Agents

Abstract: This paper describes StoryStation, an intelligent tutoring system designed to give ten to twelve year old children feedback on their creative writing. The feedback is presented via eight animated interface agents. Each agent gives a different sort of support to the writer including: a thesaurus, a dictionary, feedback on vocabulary and characterisation, help with spelling, help with plot structure, example stories to read and help with the interface itself. This paper focuses on the strategies for generating feedback to the children and discusses some issues in presenting this feedback through the interface agents.