E-Learning through Gaming

2011 ◽  
pp. 141-165
Author(s):  
Jeroen Geuze ◽  
Egon L. van den Broek
Keyword(s):  
Child Development ◽  
Knowledge Base ◽  
Intelligent Tutoring ◽  
Theoretical Framework ◽  
Board Game ◽  
E Learning

A generic theoretical framework on teaching children to negotiate is presented, founded on Piaget’s child development and Thompson and Hastie’s negotiation theories and validated through an experiment. The framework was implemented as CLIPS knowledge base, the back-end of an Intelligent Tutoring Agent (ITA). Negotiation skills were assessed through an online JAVA implementation of the board game Settlers of Catan (SoC). The CLIPS knowledge base was connected by JCLIPS to SoC. The ITA was thoroughly tested and found to be robust, with an excellent multithread handling. After installing a client, SoC can be played over Internet against other artificial or/and human players. The integrated ITA helps children to improve their negotiation skills and helps science to improve the theoretical framework, which makes it unique in its kind.

scholarly journals Intelligent Tutoring System

2020 ◽  
pp. 121-144
Author(s):  
Abhishek Singh Rathore ◽  
Siddhartha Kumar Arjaria
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
The Internet ◽  
Tutoring System ◽  
Time Data ◽  
Self Regulated Learning ◽  
Formal And Informal Learning ◽  
Real Time Data ◽  
E Learning ◽  
Teaching Learning

With digitization, a rapid growth is seen in educational technology. Different formal and informal learning contents are available on the internet. Intelligent tutoring system provides personalized e-learning to the learners. Different attributes like historical data, real-time data, behavioral, and cognitive are usually used for personalization. Based on the personalization, the intelligent tutoring system aims to provide easy and effective understanding. Recent research highlights the effect of learner's behavior and emotions on effective teaching-learning process. This chapter provides a brief description of the intelligent tutoring system, current developments, instructional techniques, proposed solution, and future recommendations. The emphasis of the study is to provide insights on self-regulated learning.

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

Authentic and Situated Learning with the Use of an Adaptive Search Engine and a QR-Code in Mobile Mode

2013 ◽  
Vol 4 (3) ◽  
pp. 19-30
Author(s):  
Orlando De Pietro
Keyword(s):  
Learning Environment ◽  
Knowledge Base ◽  
Search Engine ◽  
Situated Learning ◽  
Authentic Learning ◽  
Virtual Learning Environment ◽  
Qr Code ◽  
Adaptive Search ◽  
Learning Platform ◽  
E Learning

The paper presents a technique of interrogation, in mobile technology, of a knowledge base contained in an e-Learning platform. The query is done through the integrated use of an adaptive search engine (ASE) and QR codes. The QR-Code are used to label objects in real environments of cultural matter (eg: museums, art galleries, archaeological sites, etc.). These objects can then be analyzed in more detail during a real learning activity (situated learning and authentic learning). The knowledge base of the virtual learning environment (VLE), is interrogated through a mobile device (smartphone or tablet) which, through an appropriate decoder software, interprets the instructions contained in a QR label previously positioned on the object actually observed by the learner (eg, during a visit to a museum). ASE combines to the data contained in the QR-Code those relating to the user who performs the query (previously recognized via the log-in on the e-Learning platform), therefore, extracts information about the observed object, adapting to the profile of the user-learner. With the help of these tools the learner can expand his real learning experience, while interacting with a virtual learning environment. In this way, situated and authentic learning activities can be upgraded and virtualized, maturing a significative educational experience with the use of intuitive and user friendly digital tools.

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.

scholarly journals The Acquisition of Context Data of Study Process and their Application in Classroom and Intelligent Tutoring Systems

2015 ◽  
Vol 18 (1) ◽  
pp. 27-32 ◽  
Author(s):  
Janis Bicans
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Contextual Information ◽  
Sensor Data ◽  
Tutoring Systems ◽  
Handheld Device ◽  
Other Information ◽  
E Learning ◽  
Educational Paradigm ◽  
Study Process

Abstract Over the last decade, researchers are investigating the potential of the educational paradigm shift from the traditional “one-size-fits all” teaching approach to an adaptive and more personalized study process. Availability of fast mobile connections along with the portative handheld device evolution, like phones and tablets, enable teachers and learners to communicate and interact with each other in a completely different way and speed. The mentioned devices not only deliver tutoring material to the learner, but might also serve as sensors to provide data about the learning process itself, e.g., learning conditions, location, detailed information on learning of tutoring material and other information. This sensor data put into the context of the study process can be widely used to improve student experience in the classroom and e-learning by providing more precise and detailed information to the teacher and/or an intelligent tutoring system for the selection of an appropriate tutoring strategy. This paper analyses and discusses acquisition, processing, and application scenarios of contextual information.

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