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.

Beyond Knowledge Tracing: Modeling Skill Topologies with Bayesian Networks

Abstract: Modeling and predicting student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for student modeling is Bayesian Knowledge Tracing BKT. BKT models, however, lack the ability to describe the hierarchy and relationships between the different skills of a learning domain. In this work, we therefore aim at increasing the representational power of the student model by employing dynamic Bayesian networks that are able to represent such skill topologies. To ensure model interpretability, we constrain the parameter space. We evaluate the performance of our models on five large-scale data sets of different learning domains such as mathematics, spelling learning and physics, and demonstrate that our approach outperforms BKT in prediction accuracy on unseen data across all learning domains.

Classifying Student Initiatives and Tutor Responses in Human Keyboard-to-Keyboard Tutoring Sessions

Abstract: This study analyzed twenty-eight 1-hr-long tutoring sessions that were carried out keyboard-to-keyboard with tutor and student in different rooms. The tutors were professors of physiology at Rush Medical College. The students were 1st-year medical students. We classified student initiatives and tutor responses in human tutoring sessions with the goal of making our intelligent tutoring system capable of handling mixed-initiative dialogue. Student initiatives were classified along 4 dimensions: communicative goal, surface form, focus of attention, and degree of certainty (i.e., does the student hedge or not?). Student goals included request for confirmation, request for information, challenge, refusal to answer, and conversational repair. Tutor responses were classified along 3 dimensions: communicative goal, surface form, and delivery mode. The tutor goals included causal explanation, acknowledgment, conversational repair, instruction in the rules of the game, teaching the problem-solving algorithm, and te...

How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System

Abstract: The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students’ (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the sequences of engaging in cognitive and metacognitive self-regulated learning processes during learning with MetaTutor, an Intelligent Tutoring System that teaches students about the human circulatory system. Results revealed that there were no significant differences in fixations on single areas of interest by the prior knowledge group students were assigned to; however there were significant differences in fixations on pairs of areas of interest, as evidenced by eye-tracking data. Furthermore, there were significant differences in sequential patterns of engaging in cognitive and metacognitive self-regulated learning processes by students’ prior knowledge group, as evidenced from log-file data. Specifically, students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not. These results have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.

An agent based intelligent tutoring system for parameter passing in java programming

Abstract: We have developed an agent based intelligent tutoring system for the parameter passing mechanisms in computer science (2), an introductory Java programming language, in Al-Azhar University in Gaza. The agent based intelligent tutoring system helps students better understand parameter passing mechanisms in Java using problem based technique. In this paper, we will describe the architectural design and features of the agent based intelligent tutoring system. An initial evaluation of effectiveness of the system was carried out and the result was found to be positive. The evaluation confirmed the established hypothesis that using the intelligent tutoring system would result in an improvement in the learning of the students [5]-[9].

Embedding an intelligent tutoring system in a business gaming-simulation environment

Abstract: Gaming-Simulation environments have recently become valuable tools for education and training. To enhance their pedagogical effectiveness as a teaching environment they have to be equipped with intelligent tutoring support. The integration of an Intelligent Tutoring System into a Man-Machine Gaming-Simulation Environment provides such an intelligent teaching tool. This paper presents INTUITION, the implementation of the Metal Box Business Simulation Game, that illustrate show an Intelligent Tutoring System may be embedded within a Gaming-Simulation Environment.