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.

Techniques, Technologies and Patents Related to Intelligent Educational Systems

2011 ◽  
pp. 1-28
Author(s):  
Jim Prentzas ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Artificial Intelligence ◽  
Learning Systems ◽  
Future Research ◽  
Educational Systems ◽  
Learner Characteristics ◽  
Tutoring Systems ◽  
Artificial Intelligence Methods ◽  
E Learning ◽  
Future Research Directions ◽  
Intelligent Methods

E-learning systems play an increasingly important role in lifelong learning. Tailoring the learning process to individual needs is a key issue in such systems. Intelligent Educational Systems (IESs) are e-learning systems employing Artificial Intelligence methods to effectively adapt to learner characteristics. Main types of IESs are Intelligent Tutoring Systems (ITSs) and Adaptive Educational Hypermedia Systems (AEHSs) incorporating intelligent methods. In this chapter, the authors present technologies and techniques used in the primary modules of IESs and survey corresponding patents. They present issues and problems involving specific IES modules as well as the overall IES. The authors discuss solutions offered for such issues by Artificial Intelligence methods and patents. They also discuss categorization aspects of patents related to IESs and briefly present the work described in some representative patents. Lastly, the authors outline future research directions regarding IESs.

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

Affect Recognition for Web 2.0 Intelligent E-Tutoring Systems

2014 ◽  
pp. 188-215 ◽  
Author(s):  
Oryina Kingsley Akputu ◽  
Kah Phooi Seng ◽  
Yun Li Lee
Keyword(s):  
Machine Vision ◽  
Facial Expressions ◽  
Teaching And Learning ◽  
Learning Systems ◽  
Affective States ◽  
Feedback Information ◽  
Tutoring Systems ◽  
Video Lecture ◽  
Significant Performance ◽  
Case Based

This chapter describes how a machine vision approach could be utilized for tracking learning feedback information on emotions for enhanced teaching and learning with Intelligent Tutoring Systems (ITS). The chapter focuses on analyzing learners’ emotions to show how affective states account for personalization or traceability for learning feedback. The chapter achieves this goal in three ways: (1) by presenting a comprehensive review of adaptive educational learning systems, particularly inspired by machine vision approaches; (2) by proposing an affective model for monitoring learners’ emotions and engagement with educational learning systems; (3) by presenting a case-based technique as an experimental prototype for the proposed affective model, where students’ facial expressions are tracked in the course of studying a composite video lecture. Results of the experiments indicate the superiority of such emotion-aware systems over emotion-unaware ones, achieving a significant performance increment of 71.4%.

Ideas on the Future of Intelligent Web-Based E-Learning

2018 ◽  
pp. 2274-2287
Author(s):  
Utku Kose
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Current Status ◽  
Learning Approaches ◽  
Learning Tools ◽  
Web Based ◽  
The Future ◽  
Teachers And Students ◽  
Important Approach ◽  
E Learning

With the outstanding improvements in technology, the number of e-learning applications has increased greatly. This increment is associated with awareness levels of educational institutions on the related improvements and the power of communication and computer technologies to ensure effective and efficient teaching and learning experiences for teachers and students. Consequently, there is a technological flow that changes the standards of e-learning processes and provides better ways to obtain desired educational objectives. When we consider today's widely used technological factors, Web-based e-learning approaches have a special role in directing the educational standards. Improvements among m-learning applications and the popularity of the Artificial Intelligence usage for educational works have given great momentum to this orientation. In this sense, this chapter provides some ideas on the future of intelligent Web-based e-learning applications by thinking on the current status of the literature. As it is known, current trends in developing Artificial Intelligence-supported e-learning tools continue to shape the future of e-learning. Therefore, it is an important approach to focus on the future. The author thinks that the chapter will be a brief but effective enough reference for similar works, which focus on the future of Artificial Intelligence-supported distance education and e-learning.

Affect Recognition for Web 2.0 Intelligent E-Tutoring Systems

2018 ◽  
pp. 338-368
Author(s):  
Oryina Kingsley Akputu ◽  
Kah Phooi Seng ◽  
Yun Li Lee
Keyword(s):  
Machine Vision ◽  
Facial Expressions ◽  
Teaching And Learning ◽  
Learning Systems ◽  
Affective States ◽  
Feedback Information ◽  
Tutoring Systems ◽  
Video Lecture ◽  
Significant Performance ◽  
Case Based

This chapter describes how a machine vision approach could be utilized for tracking learning feedback information on emotions for enhanced teaching and learning with Intelligent Tutoring Systems (ITS). The chapter focuses on analyzing learners' emotions to show how affective states account for personalization or traceability for learning feedback. The chapter achieves this goal in three ways: (1) by presenting a comprehensive review of adaptive educational learning systems, particularly inspired by machine vision approaches; (2) by proposing an affective model for monitoring learners' emotions and engagement with educational learning systems; (3) by presenting a case-based technique as an experimental prototype for the proposed affective model, where students' facial expressions are tracked in the course of studying a composite video lecture. Results of the experiments indicate the superiority of such emotion-aware systems over emotion-unaware ones, achieving a significant performance increment of 71.4%.

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