Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013 |
| Pages | 149-153 |
| Number of pages | 5 |
| Publication status | Published - 2013 |
| Event | 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013 - Beijing, China Duration: 15 Jul 2013 → 18 Jul 2013 |
Publication series
| Name | Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013 |
|---|
Abstract
A competence is the effective performance in a domain at different levels of proficiency. Educational institutions apply competences to understand whether a person has a particular level of ability or skill. Educational resource enriched with competence information allows learners identifying, on a fine-grained level, which resources to study with the aim to reach a specific competence target. 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 levels. To solve these problems, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. We demonstrate the quality of the proposed methods through an evaluation on real world data with an additional user study. Results show that the automatic competence level assignment achieves 84% precision on ground truth data. The key implications of our approach are: first, it effectively facilitates experts in the arduous task of competence assignment and second, it directly supports learners to retrieve proper leveled material.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
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Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013. 2013. p. 149-153 6601890 (Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Automatic competence leveling of learning objects
AU - Kawase, Ricardo
AU - Siehndel, Patrick
AU - Nunes, Bernardo Pereira
AU - Fisichella, Marco
PY - 2013
Y1 - 2013
N2 - A competence is the effective performance in a domain at different levels of proficiency. Educational institutions apply competences to understand whether a person has a particular level of ability or skill. Educational resource enriched with competence information allows learners identifying, on a fine-grained level, which resources to study with the aim to reach a specific competence target. 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 levels. To solve these problems, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. We demonstrate the quality of the proposed methods through an evaluation on real world data with an additional user study. Results show that the automatic competence level assignment achieves 84% precision on ground truth data. The key implications of our approach are: first, it effectively facilitates experts in the arduous task of competence assignment and second, it directly supports learners to retrieve proper leveled material.
AB - A competence is the effective performance in a domain at different levels of proficiency. Educational institutions apply competences to understand whether a person has a particular level of ability or skill. Educational resource enriched with competence information allows learners identifying, on a fine-grained level, which resources to study with the aim to reach a specific competence target. 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 levels. To solve these problems, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. We demonstrate the quality of the proposed methods through an evaluation on real world data with an additional user study. Results show that the automatic competence level assignment achieves 84% precision on ground truth data. The key implications of our approach are: first, it effectively facilitates experts in the arduous task of competence assignment and second, it directly supports learners to retrieve proper leveled material.
UR - http://www.scopus.com/inward/record.url?scp=84885200129&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2013.47
DO - 10.1109/ICALT.2013.47
M3 - Conference contribution
AN - SCOPUS:84885200129
SN - 9780769550091
T3 - Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
SP - 149
EP - 153
BT - Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
T2 - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
Y2 - 15 July 2013 through 18 July 2013
ER -