Agents in m-Learning Systems Based on Intelligent Tutoring

2007 ◽  
pp. 578-587 ◽  
Author(s):  
Vlado Glavinic ◽  
Marko Rosic ◽  
Marija Zelic
Keyword(s):  
Intelligent Tutoring ◽  
Learning Systems

Intelligent Tutoring and Games (ITaG)

2010 ◽  
pp. 44-65 ◽  
Author(s):  
Danielle S. McNamara ◽  
G. Tanner Jackson ◽  
Art Graesser
Keyword(s):  
Conceptual Framework ◽  
Learning Environments ◽  
Functional Groups ◽  
Intelligent Tutoring ◽  
Learning Systems ◽  
Potential Solution ◽  
Learning Gains ◽  
Tutoring Systems ◽  
System A ◽  
Insight Into

Intelligent Tutoring Systems (ITSs) have been producing consistent learning gains for decades. The authors describe here a conceptual framework that provides a guide to how adding game-based features and components may improve the effectiveness of ITS learning environments by improving students’ motivation to engage with the system. A problem consistently faced by ITS researchers is the gap between liking and learning. ITSs effectively produce learning gains, but students often dislike interacting with the system. A potential solution to this problem lies in games. ITS researchers have begun to incorporate game-based elements within learning systems. This chapter aims to describe some of those elements, categorize them within functional groups, and provide insight into how elements within each category may affect various types of motivation.

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.

The Role of Affect in an Agent-Based Collaborative E-Learning System Used for Engineering Education

2009 ◽  
pp. 510-528
Author(s):  
Mohamed Ben Ammar ◽  
Mahmoud Neji ◽  
Adel M. Alimi
Keyword(s):  
Facial Expressions ◽  
Affective Computing ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning Systems ◽  
Learning System ◽  
Agent Based ◽  
Affective Behaviors ◽  
E Learning

Affective computing is a new artificial intelligence area that deals with the possibility of making computers able to recognize human emotions in different ways. This chapter represents an implemented framework, which integrates this new area with an intelligent tutoring system. The authors argue that tutor agents providing socially appropriate affective behaviors would provide a new dimension for collaborative learning systems. The main goal is to analyse learner facial expressions and show how affective computing could contribute to learning interactions, both by recognizing learner emotions during learning sessions and by responding appropriately.

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.

scholarly journals INTELLIGENT TUTORING SYSTEM FOR REAL ESTATE MANAGEMENT

2006 ◽  
Vol 10 (2) ◽  
pp. 113-130 ◽  
Author(s):  
Artûras Kaklauskas ◽  
Ruslanas Ditkevičius ◽  
Leonarda Gargasaite
Keyword(s):  
Decision Support ◽  
Real Estate ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning Systems ◽  
Domain Model ◽  
Tutoring System ◽  
Student Model ◽  
Testing Model ◽  
Real Estate Management

The review on the worldwide intelligent tutoring systems and their application possibilities is presented in the paper. The intelligent tutoring system for real estate management developed by the authors is described. This system is applied in Vilnius Gediminas Technical University, Department of Construction Economics and Property Management. Besides the common components ‐ student model, domain model, pedagogical model and graphical interface, the new developed system has testing model, decision support subsystem and database of computer learning systems. Domain model includes knowledge with the supplemental audio and video material for 63 modules being taught in Vilnius Gediminas Technical University. Student model enables to adapt to a learner needs and knowledge level. Decision support subsystem is used for all components of intelligent tutoring system giving them different level of intelligence. Database of computer learning systems enables using the following web‐based learning systems: construction, real estate, facilities management, international trade, ethics, innovation, sustainable development, building refurbishment, etc. Tutor and testing model provide a model of the teaching process and support transition to a new knowledge state. Graphic interface is used to create an effective system‐user dialogue.

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.

Assessment of Cognitive Load in Multimedia Learning Using Dual-Task Methodology

2002 ◽  
Vol 49 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Roland Brünken ◽  
Susan Steinbacher ◽  
Jan L. Plass ◽  
Detlev Leutner
Keyword(s):  
Cognitive Load ◽  
Dual Task ◽  
Cognitive Load Theory ◽  
Multimedia Learning ◽  
Learning Systems ◽  
Learning Materials ◽  
New Approach ◽  
Direct Assessment ◽  
Load Theory ◽  
Audiovisual Presentation

Abstract. In two pilot experiments, a new approach for the direct assessment of cognitive load during multimedia learning was tested that uses dual-task methodology. Using this approach, we obtained the same pattern of cognitive load as predicted by cognitive load theory when applied to multimedia learning: The audiovisual presentation of text-based and picture-based learning materials induced less cognitive load than the visual-only presentation of the same material. The findings confirm the utility of dual-task methodology as a promising approach for the assessment of cognitive load induced by complex multimedia learning systems.

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