Publications | DAISSy

agents Ambient Assisted Living ambient intelligence Assessment communities of practice distance education Distance Learning E-learning Education intelligent agents learning activities Learning Design learning objects Lifelong learning ontologies Ontology Open and Distance Learning participatory design personalized learning policy programming languages Project and portfolio management information systems Protege Sensor Networks Social Networking System Social Semantic Web Ubiquitous computing εξ αποστάσεως εκπαίδευση οντολογίες προγραμματισμός

Show all

2012

Panagiotopoulos, Ioannis; Kalou, Aikaterini; Pierrakeas, Christos; Kameas, Achilles

Adult Student Modeling for Intelligent Distance Learning Systems (Proceeding)

Halkidiki, Greece, 2012.

(Abstract | BibTeX | Tags: intelligent tutoring systems, Learner Model, Ontology, personalized learning, stereotypes)

@proceedings{528,
title = {Adult Student Modeling for Intelligent Distance Learning Systems},
author = {Ioannis Panagiotopoulos and Aikaterini Kalou and Christos Pierrakeas and Achilles Kameas},
year = {2012},
date = {2012-01-01},
journal = {Special Issue on AIAI 2012 of the International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications (EISEEC)},
address = {Halkidiki, Greece},
abstract = {

One of the most important components in a learning support system is the learner model, as it contains useful information about an individual such as learning preferences and academic performance. The goal of the research presented in this paper is to define how a learner model can be distributed with the help of semantic web technologies, based on stereotypes as a useful mechanism for the initialization of an intelligent learning system. These stereotypes have been derived from an empirical study on a sample of adult learners at a distance learning University, while the proposed model also reflects features from several standards for a learner modeling. Finally, a web application is presented, in order to evaluate the learner model and test the automatic categorization of learners into stereotypes according to their basic characteristics.

},
keywords = {intelligent tutoring systems, Learner Model, Ontology, personalized learning, stereotypes},
pubstate = {published},
tppubtype = {proceedings}
}

<div style="text-align: justify;">One of the most important components in a learning support system is the learner model, as it contains useful information about an individual such as learning preferences and academic performance. The goal of the research presented in this paper is to define how a learner model can be distributed with the help of semantic web technologies, based on stereotypes as a useful mechanism for the initialization of an intelligent learning system. These stereotypes have been derived from an empirical study on a sample of adult learners at a distance learning University, while the proposed model also reflects features from several standards for a learner modeling. Finally, a web application is presented, in order to evaluate the learner model and test the automatic categorization of learners into stereotypes according to their basic characteristics.</div>

Panagiotopoulos, Ioannis; Kalou, Aikaterini; Pierrakeas, Christos; Kameas, Achilles

An Ontology-Based Model for Student Representation in Intelligent Tutoring Systems for Distance Learning (Proceeding)

Springer Berlin Heidelberg, Halkidiki, Greece, 2012.

(Abstract | Links | BibTeX | Tags: intelligent tutoring systems, Ontology, personalized learning, stereotypes, student model)

@proceedings{67,
title = {An Ontology-Based Model for Student Representation in Intelligent Tutoring Systems for Distance Learning},
author = {Ioannis Panagiotopoulos and Aikaterini Kalou and Christos Pierrakeas and Achilles Kameas},
url = {http://link.springer.com/chapter/10.1007/978-3-642-33409-2_31},
year = {2012},
date = {2012-01-01},
journal = {8th Artificial Intelligence Applications and Innovations (AIAI 2012).},
volume = {IFIP AICT 381},
publisher = {Springer Berlin Heidelberg},
address = {Halkidiki, Greece},
abstract = {

An Intelligent Tutoring System (ITS) offers personalized education to each student in accordance with his/her learning preferences and his/her background. One of the most fundamental components of an ITS is the student model, that contains all the information about a student such as demographic information, learning style and academic performance. This information enables the system to be fully adapted to the student. Our research work intends to propose a student model and enhance it with semantics by developing (or via) an ontology in order to be exploitable effectively within an ITS, for example as a domain-independent vocabulary for the communication between intelligent agents. The ontology schema consists of two main taxonomies: (a) studenttextquoterights academic information and (b) studenttextquoterights personal information. The characteristics of the student that have been included in the student model ontology were derived from an empirical study on a sample of students.

},
keywords = {intelligent tutoring systems, Ontology, personalized learning, stereotypes, student model},
pubstate = {published},
tppubtype = {proceedings}
}

An Intelligent Tutoring System (ITS) offers personalized education to each student in accordance with his/her learning preferences and his/her background. One of the most fundamental components of an ITS is the student model, that contains all the information about a student such as demographic information, learning style and academic performance. This information enables the system to be fully adapted to the student. Our research work intends to propose a student model and enhance it with semantics by developing (or via) an ontology in order to be exploitable effectively within an ITS, for example as a domain-independent vocabulary for the communication between intelligent agents. The ontology schema consists of two main taxonomies: (a) studenttextquoterights academic information and (b) studenttextquoterights personal information. The characteristics of the student that have been included in the student model ontology were derived from an empirical study on a sample of students.

http://link.springer.com/chapter/10.1007/978-3-642-33409-2_31