scholarly journals Recommending learning material in Intelligent Tutoring Systems

2016 ◽  
Vol 16 (1) ◽  
pp. 1
Author(s):  
Jarosław Bernacki
Keyword(s):  
Learning Styles ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Psychological Research ◽  
Learning System ◽  
Learning Material ◽  
Optimal Learning ◽  
Tutoring Systems ◽  
Learning Path ◽  
E Learning

<p>Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used.</p>

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.

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 Optimal Hierarchical Learning Path Design With Reinforcement Learning

2020 ◽  
Vol 45 (1) ◽  
pp. 54-70
Author(s):  
Xiao Li ◽  
Hanchen Xu ◽  
Jinming Zhang ◽  
Hua-hua Chang
Keyword(s):  
Reinforcement Learning ◽  
Learning System ◽  
Learning Material ◽  
Optimal Learning ◽  
Model Free ◽  
Learning Path ◽  
The Hierarchical Structure ◽  
E Learning ◽  
Efficient Learning ◽  
Skills Learning

E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners’ current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners’ hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners’ learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.

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.

Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type

2019 ◽  
Vol 53 (2) ◽  
pp. 189-200 ◽  
Author(s):  
Aisha Yaquob Alsobhi ◽  
Khaled Hamed Alyoubi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning Experience ◽  
Assistive Technologies ◽  
Learning Systems ◽  
Learning System ◽  
Fixed Cost ◽  
Learning Material ◽  
Content Type ◽  
E Learning

PurposeThrough harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type.Design/methodology/approachThe DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases.FindingsAn experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes.Originality/valueThe DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.

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.

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