Towards Automatic Competence Assignment of Learning Objects

Research output: Contribution to journalConference articleResearchpeer review

Authors

Research Organisations

External Research Organisations

  • Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
View graph of relations

Details

Original languageEnglish
Pages (from-to)401-406
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7563 LNCS
Publication statusPublished - 2012
Event7th European Conference on Technology Enhanced Learning, EC-TEL 2012 - Saarbrucken, Germany
Duration: 18 Sept 201221 Sept 2012

Abstract

Competence-annotations assist learners to retrieve and better understand the level of skills required to comprehend learning objects. However, the process of annotating learning objects with competence levels is a very time consuming task; ideally, this task should be performed by experts on the subjects of the educational resources. Due to this, most educational resources available online do not enclose competence information. In this paper, we present a method to tackle the problem of automatically assigning an educational resource with competence topics. To solve this problem, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. Results show that automatically assigned competences are coherent and may be applied to automatically enhance learning objects metadata.

Keywords

    Automatic Competence Classification, Competences, E-Learning, Metadata Generation

ASJC Scopus subject areas

Cite this

Towards Automatic Competence Assignment of Learning Objects. / Kawase, Ricardo; Siehndel, Patrick; Nunes, Bernardo Pereira et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7563 LNCS, 2012, p. 401-406.

Research output: Contribution to journalConference articleResearchpeer review

Kawase, R, Siehndel, P, Nunes, BP, Fisichella, M & Nejdl, W 2012, 'Towards Automatic Competence Assignment of Learning Objects', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7563 LNCS, pp. 401-406. https://doi.org/10.1007/978-3-642-33263-0_34
Kawase, R., Siehndel, P., Nunes, B. P., Fisichella, M., & Nejdl, W. (2012). Towards Automatic Competence Assignment of Learning Objects. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7563 LNCS, 401-406. https://doi.org/10.1007/978-3-642-33263-0_34
Kawase R, Siehndel P, Nunes BP, Fisichella M, Nejdl W. Towards Automatic Competence Assignment of Learning Objects. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2012;7563 LNCS:401-406. doi: 10.1007/978-3-642-33263-0_34
Kawase, Ricardo ; Siehndel, Patrick ; Nunes, Bernardo Pereira et al. / Towards Automatic Competence Assignment of Learning Objects. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2012 ; Vol. 7563 LNCS. pp. 401-406.
Download
@article{4249c0a2ffdb482d8599d9d4fb992ff5,
title = "Towards Automatic Competence Assignment of Learning Objects",
abstract = "Competence-annotations assist learners to retrieve and better understand the level of skills required to comprehend learning objects. However, the process of annotating learning objects with competence levels is a very time consuming task; ideally, this task should be performed by experts on the subjects of the educational resources. Due to this, most educational resources available online do not enclose competence information. In this paper, we present a method to tackle the problem of automatically assigning an educational resource with competence topics. To solve this problem, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. Results show that automatically assigned competences are coherent and may be applied to automatically enhance learning objects metadata.",
keywords = "Automatic Competence Classification, Competences, E-Learning, Metadata Generation",
author = "Ricardo Kawase and Patrick Siehndel and Nunes, {Bernardo Pereira} and Marco Fisichella and Wolfgang Nejdl",
note = "Funding information: Acknowledgement. This research has been co-funded by the European Commission within the eContentplus targeted project OpenScout, grant ECP 2008 EDU 428016 (cf. http://www.openscout.net) and by CAPES (Process no 9404-11-2).; 7th European Conference on Technology Enhanced Learning, EC-TEL 2012 ; Conference date: 18-09-2012 Through 21-09-2012",
year = "2012",
doi = "10.1007/978-3-642-33263-0_34",
language = "English",
volume = "7563 LNCS",
pages = "401--406",

}

Download

TY - JOUR

T1 - Towards Automatic Competence Assignment of Learning Objects

AU - Kawase, Ricardo

AU - Siehndel, Patrick

AU - Nunes, Bernardo Pereira

AU - Fisichella, Marco

AU - Nejdl, Wolfgang

N1 - Funding information: Acknowledgement. This research has been co-funded by the European Commission within the eContentplus targeted project OpenScout, grant ECP 2008 EDU 428016 (cf. http://www.openscout.net) and by CAPES (Process no 9404-11-2).

PY - 2012

Y1 - 2012

N2 - Competence-annotations assist learners to retrieve and better understand the level of skills required to comprehend learning objects. However, the process of annotating learning objects with competence levels is a very time consuming task; ideally, this task should be performed by experts on the subjects of the educational resources. Due to this, most educational resources available online do not enclose competence information. In this paper, we present a method to tackle the problem of automatically assigning an educational resource with competence topics. To solve this problem, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. Results show that automatically assigned competences are coherent and may be applied to automatically enhance learning objects metadata.

AB - Competence-annotations assist learners to retrieve and better understand the level of skills required to comprehend learning objects. However, the process of annotating learning objects with competence levels is a very time consuming task; ideally, this task should be performed by experts on the subjects of the educational resources. Due to this, most educational resources available online do not enclose competence information. In this paper, we present a method to tackle the problem of automatically assigning an educational resource with competence topics. To solve this problem, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. Results show that automatically assigned competences are coherent and may be applied to automatically enhance learning objects metadata.

KW - Automatic Competence Classification

KW - Competences

KW - E-Learning

KW - Metadata Generation

UR - http://www.scopus.com/inward/record.url?scp=84885237010&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-33263-0_34

DO - 10.1007/978-3-642-33263-0_34

M3 - Conference article

AN - SCOPUS:84885237010

VL - 7563 LNCS

SP - 401

EP - 406

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SN - 0302-9743

T2 - 7th European Conference on Technology Enhanced Learning, EC-TEL 2012

Y2 - 18 September 2012 through 21 September 2012

ER -

By the same author(s)