Abstract
Intelligent Tutoring Systems (ITSs) are inherently adaptive e-learning systems usually created for teaching well-defined domains (e.g., mathematics). Their objective is to guide the student towards a predefined goal such as completing a lesson, task, or mastering a skill. Defining goals and guiding students is more complex in ill-defined domains where the expert defines the model of the knowledge domain or the students have freedom to follow their own path through it. In this paper we present an overview of our systems architecture that integrates the ITS with data mining tools and performs a number of educational data mining processes to increase the adaptivity and, consequently, the efficiency of the ITS.
Highlights
Educational Data Mining (EDM) is a growing field of research concerned with applying the existing artificial intelligence and machine learning methods, as well as developing new ones, for the purpose of analyzing and learning from data originating in educational environments
This paper presents an overview of the architecture of a web-based Intelligent Tutoring Systems (ITSs) (WITS), developed at our institution, which implements those objectives
By improving the adaptivity of the tutoring model we aim to improve the overall efficiency of the system
Summary
Educational Data Mining (EDM) is a growing field of research concerned with applying the existing artificial intelligence and machine learning methods, as well as developing new ones, for the purpose of analyzing and learning from data originating in educational environments. One of the main objectives of this field is to develop tools that are easy to use for teachers and other nonexperts in data mining. These tools need to be integrated into various e-learning systems to provide insights to teachers and improve the students’ learning experience. This paper presents an overview of the architecture of a web-based ITS (WITS), developed at our institution, which implements those objectives. Our goal for this particular system was to increase its adaptivity and, overall efficiency. By improving the adaptivity of the tutoring model we aim to improve the overall efficiency of the system
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