scholarly journals Adaptive intelligent tutoring systems for e-learning systems

2010 ◽  
Vol 2 (2) ◽  
pp. 4064-4069 ◽  
Author(s):  
Pipatsarun Phobun ◽  
Jiracha Vicheanpanya
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Learning Systems ◽  
Tutoring Systems ◽  
E Learning

Using Effect Size for Group Modeling in E-Learning Systems

2011 ◽  
pp. 237-257
Author(s):  
Divna Krpan ◽  
Suzana Tomaš ◽  
Roko Vladušic
Keyword(s):  
Research And Development ◽  
Future Development ◽  
Effect Size ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Learning Systems ◽  
Tutoring Systems ◽  
Group Modeling ◽  
Modeling Group ◽  
E Learning

There is great need for collaboration in education and e-learning systems which imply the necessity for group modeling. Since Bloom’s experiment, which produced effect size of 2-sigma, there were many attempts to repeat those results with intelligent tutoring systems. Our experiments show effectiveness of xTEx-Sys in measure of effect size. The goal of our research and development is to get as close as possible to effect size of 2-sigma. There is greater need for collaboration in e-learning systems and there are some indications that collaboration could increase effectiveness. Since collaboration is closely coupled with groups, directions for future development and exploration of e-learning systems lay in field of group modeling. Group modeling also implies creation of stereotype models.

E-Learning with Affective Tutoring Systems

2011 ◽  
pp. 129-140
Author(s):  
Abdolhossein Sarrafzadeh ◽  
Jamshid Shanbehzadeh ◽  
Scott Overmyer
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Affective State ◽  
Learning Systems ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Effective Current ◽  
Animated Agent ◽  
E Learning ◽  
K 12

E-learning has attracted a great deal of interest in educational circles from K-12 to universities. A question that is often rightly asked is how effective current e-learning systems are. It is argued that there is little individualization of instruction by adapting to the pedagogical needs of each learner in current e-learning systems. Intelligent tutoring systems have tried to fill this gap but even they fail to compete with human one-to-one tutoring. This paper presents Affective Tutoring Systems which are e-learning systems capable of detecting learners’ affective state and reacting to it through a life like agent called Eve. This paper presents an Affective Tutoring System in the domain of mathematics and the research that led to its development. It also presents the findings from the study and testing of the system indicating that the animated agent Eve carried a persona effect.

scholarly journals Intelligent Tutoring Systems

2017 ◽  
Vol 26 (4) ◽  
pp. 717-727 ◽  
Author(s):  
Vladimír Bradáč ◽  
Kateřina Kostolányová
Keyword(s):  
Intelligent Tutoring Systems ◽  
Technological Development ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Point Of View ◽  
Tertiary Sector ◽  
Tutoring Systems ◽  
Number Of Ends ◽  
E Learning ◽  
Education Of Students

AbstractThe importance of intelligent tutoring systems has rapidly increased in past decades. There has been an exponential growth in the number of ends users that can be addressed as well as in technological development of the environments, which makes it more sophisticated and easily implementable. In the introduction, the paper offers a brief overview of intelligent tutoring systems. It then focuses on two types that have been designed for education of students in the tertiary sector. The systems use elements of adaptivity in order to accommodate as many users as possible. They serve both as a support of presence lessons and, primarily, as the main educational environment for students in the distance form of studies – e-learning. The systems are described from the point of view of their functionalities and typical features that show their differences. The authors conclude with an attempt to choose the best features of each system, which would lead to creation of an even more sophisticated intelligent tutoring system for e-learning.

Building and Assessing Intelligent Tutoring Systems with an e-Learning 2.0 Authoring System

2010 ◽  
pp. 1-9
Author(s):  
M. L. Barrón-Estrada ◽  
Ramón Zatarain-Cabada ◽  
Rosalío Zatarain-Cabada ◽  
Hector Barbosa-León ◽  
Carlos A. Reyes-García
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Authoring System ◽  
Tutoring Systems ◽  
E Learning

scholarly journals Increasing the Adaptivity of an Intelligent Tutoring System with Educational Data Mining: A System Overview

2016 ◽  
Vol 11 (03) ◽  
pp. 67 ◽  
Author(s):  
Igor Jugo ◽  
Božidar Kovačić ◽  
Vanja Slavuj
Keyword(s):  
Data Mining ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Educational Data Mining ◽  
Knowledge Domain ◽  
Systems Architecture ◽  
Tutoring Systems ◽  
E Learning ◽  
Mining Tools

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.

Artificial Intelligence Methods in E-Learning

2019 ◽  
pp. 287-307
Author(s):  
Meltem Eryılmaz ◽  
Afaf Muftah Adabashi ◽  
Ali Yazıcı
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Learning Systems ◽  
Educational Systems ◽  
Educational Materials ◽  
Tutoring Systems ◽  
Challenges And Opportunities ◽  
E Learning ◽  
Effective Teaching And Learning

Gathering and extracting knowledge from the large amount of data available today is becoming more and more important in our information society, and similarly, learning is an essential important part of our everyday lives. The new requirements of the competing world and the development of more advanced technologies have also changed traditional educational systems, which now employ better and more effective teaching and learning methods. In this regard, the integration of artificial intelligence (AI) technologies in the field of education offers both great challenges and opportunities in building e-learning systems. E-learning systems allow learners to access the educational materials ubiquitously from anywhere at any time. Therefore, these systems have to become adaptive to the needs and preferences of each individual learner. This chapter presents a review of the important concepts and background for research to include introduction and examination of e-learning systems and intelligent tutoring systems (ITSs), available today.

A Meta-Analytic Estimation of a Common Effect Size from a Series of Experiments Related To an E-Learning System Effectiveness Evaluation

2011 ◽  
pp. 327-341
Author(s):  
Ani Grubišic
Keyword(s):  
Student Performance ◽  
Effect Size ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Meta Analysis ◽  
Learning System ◽  
Tutoring Systems ◽  
Learning And Teaching ◽  
E Learning ◽  
Series Of Experiments

As the acquisition of knowledge is often an expensive and time-consuming process, it is important to know whether it actually improves the student performance. The e-learning is a revolutionary paradigm that has lately been significantly evolving and it is closely related to the intelligent tutoring systems. Methodology for evaluating the educational influence of learning and teaching process, questions whether and in what amount, students learn effectively. Our contribution to this compulsive field of research is a meta-analysis of a series of experiments based on the same two-group methodology that reveals a more precise effect size of one particular e-learning system - eXtended Tutor-Expert System, a representative of web-based authoring shells for building intelligent tutoring systems.

scholarly journals A method for learning scenario determination and modification in intelligent tutoring systems

2011 ◽  
Vol 21 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Adrianna Kozierkiewicz-Hetmańska ◽  
Ngoc Nguyen
Keyword(s):  
Learning Styles ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Educational Process ◽  
Learning System ◽  
Optimal Learning ◽  
Tutoring Systems ◽  
Learning Path ◽  
E Learning ◽  
Using Data

A method for learning scenario determination and modification in intelligent tutoring systemsComputers have been employed in education for years. They help to provide educational aids using multimedia forms such as films, pictures, interactive tasks in the learning process, automated testing, etc. In this paper, a concept of an intelligent e-learning system will be proposed. The main purpose of this system is to teach effectively by providing an optimal learning path in each step of the educational process. The determination of a suitable learning path depends on the student's preferences, learning styles, personal features, interests and knowledge state. Therefore, the system has to collect information about the student, which is done during the registration process. A user is classified into a group of students who are similar to him/her. Using information about final successful scenarios of students who belong to the same class as the new student, the system determines an opening learning scenario. The opening learning scenario is the first learning scenario proposed to a student after registering in an intelligent e-learning system. After each lesson, the system tries to evaluate the student's knowledge. If the student has a problem with achieving an assumed score in a test, this means that the opening learning scenario is not adequate for this user. In our concept, for this case an intelligent e-learning system offers a modification of the opening learning scenario using data gathered during the functioning of the system and based on a Bayesian network. In this paper, an algorithm of scenario determination (named ADOLS) and a procedure for modifying the learning scenario AMLS with auxiliary definitions are presented. Preliminary results of an experiment conducted in a prototype of the described system are also described.

Sign in / Sign up

Export Citation Format

Share Document