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.

Enhancing learners' experience in e-learning based scenarios using Intelligent tutoring systems and learning analytics: First results from a perception survey

Abstract: E-learning students tend to get jaded and easily dropout from online courses. Enhancing the learners' experience and reducing dropout rates in these e-learning based scenarios is the main purpose of this study. This paper presents the results obtained so far and preliminary conclusions. In a first stage, the objective was to study the background and state of the art of these educational scenarios. In a second phase, identifying key reasons for dropouts, through a survey and interviews, was the aim to understand and detect motives and behavior patterns of students with dropout thoughts. Finally, developing, testing and validating a functional prototype of an Intelligent Tutoring System will allow to evaluate concepts, collect statistical information on its effectiveness, analyze and discover if course completion rates are improved.

Partially Observable Sequential Decision Making for Problem Selection in an Intelligent Tutoring System.

Abstract: A key part of effective teaching is adaptively selecting pedagogical activities to maximize long term student learning. In this poster we report on ongoing work to both develop a tutoring strategy that leverages insights from the partially observable Markov decision process (POMDP) framework to improve problem selection relative to state-of-the-art intelligent tutoring systems, and evaluate the computed strategy in the classroom. We also highlight some of the challenges in data mining related to automatically constructing pedagogical strategies.

Fuzzy and MultiAgent Instructional Planner for an Intelligent Tutorial System

Abstract: This article presents some aspects in our research into the design of a Fuzzy and MultiAgent Instructional Planner belonging to an Intelligent Tutoring System (ITS), which has been designed as a tool for the reinforcement of the addition operation. The authors propose the combined use of both fuzzy and MultiAgent Systems. The fuzzy logic methodology is used to model the student's knowledge and the teaching strategy. Furthermore, the MultiAgent System implemented determines the learning objectives so as to provide the student with an efficient learning process. The fuzzy and MultiAgent Systems comprising the instructional planner were verified with the collaboration of experts in mathematics and in other areas of knowledge. The results obtained by the primary school children who used the ITS are also presented.

Exchanging courses between different Intelligent Tutoring Systems: A generic course generation authoring tool

Abstract: In recent years a great effort has been made in order to create Intelligent Tutoring Systems that get close to human teaching. Some of the handicaps of the systems already created are the impossibility of sharing the courses between different Intelligent Tutoring Systems and the difficulty of creating them. Once the intelligent tutoring system is created, creating a new course is an expensive job that requires the intervention of many people that are expert in different areas. In this paper a generic and extensible authoring tool to create courses for different Intelligent Tutoring Systems is presented. This authoring tool allows the creation of courses for different types of intelligent tutoring systems. Once a course is created it can be exported to another intelligent tutoring system, reusing the domain model that the course represents. The prototype of the authoring tool has been tested with two simple Intelligent Tutoring Systems.

Intelligent Tutoring System-Bayesian Student Model

Abstract: Nowadays different approaches are coming forth to tutor students using computers. In this paper, a computer based intelligent tutoring system (ITS) is presented. It projects out a new approach dealing with diagnosis in student modeling which emphasizes on Bayesian networks (for decision making) and item response theory (for adaptive question selection). The advantage of such an approach through Bayesian networks (formal framework of uncertainty) is that this structural model allows substantial simplification when specifying parameters (conditional probabilities) which measures student ability at different levels of granularity. In addition, the probabilistic student model is proved to be more quicker, accurate and efficient. Since most of the tutoring systems are static HTML web pages of class textbooks, our intelligent system can help a student navigate through online course materials and recommended learning goals.