Details
| Original language | English |
|---|---|
| Title of host publication | Towards Ubiquitous Learning - 6th European Conference on Technology Enhanced Learning, EC-TEL 2011, Proceedings |
| Pages | 83-96 |
| Number of pages | 14 |
| Publication status | Published - 28 Sept 2011 |
| Event | 6th European Conference on Technology Enhanced Learning, EC-TEL 2011 - Palermo, Italy Duration: 20 Sept 2011 → 23 Sept 2011 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 6964 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
An online presence is gradually becoming an essential part of every learning institute. As such, a large portion of learning material is becoming available online. Incongruently, it is still a challenge for authors and publishers to guarantee accessibility, support effective retrieval and the consumption of learning objects. One reason for this is that non-annotated learning objects pose a major problem with respect to their accessibility. Non-annotated objects not only prevent learners from finding new information; but also hinder a system's ability to recommend useful resources. To address this problem, commonly known as the cold-start problem, we automatically annotate specific learning resources using a state-of-the-art automatic tag annotation method: α-TaggingLDA, which is based on the Latent Dirichlet Allocation probabilistic topic model. We performed a user evaluation with 115 participants to measure the usability and effectiveness of α-TaggingLDA in a collaborative learning environment. The results show that automatically generated tags were preferred 35% more than the original authors' annotations. Further, they were 17.7% more relevant in terms of recall for users. The implications of these results is that automatic tagging can facilitate effective information access to relevant learning objects.
Keywords
- Cold-Start, LDA, Metadata Generation, Recommender Systems, User Study
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Towards Ubiquitous Learning - 6th European Conference on Technology Enhanced Learning, EC-TEL 2011, Proceedings. 2011. p. 83-96 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6964 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Unsupervised Auto-tagging for Learning Object Enrichment
AU - Diaz-Aviles, Ernesto
AU - Fisichella, Marco
AU - Kawase, Ricardo
AU - Nejdl, Wolfgang
AU - Stewart, Avaré
PY - 2011/9/28
Y1 - 2011/9/28
N2 - An online presence is gradually becoming an essential part of every learning institute. As such, a large portion of learning material is becoming available online. Incongruently, it is still a challenge for authors and publishers to guarantee accessibility, support effective retrieval and the consumption of learning objects. One reason for this is that non-annotated learning objects pose a major problem with respect to their accessibility. Non-annotated objects not only prevent learners from finding new information; but also hinder a system's ability to recommend useful resources. To address this problem, commonly known as the cold-start problem, we automatically annotate specific learning resources using a state-of-the-art automatic tag annotation method: α-TaggingLDA, which is based on the Latent Dirichlet Allocation probabilistic topic model. We performed a user evaluation with 115 participants to measure the usability and effectiveness of α-TaggingLDA in a collaborative learning environment. The results show that automatically generated tags were preferred 35% more than the original authors' annotations. Further, they were 17.7% more relevant in terms of recall for users. The implications of these results is that automatic tagging can facilitate effective information access to relevant learning objects.
AB - An online presence is gradually becoming an essential part of every learning institute. As such, a large portion of learning material is becoming available online. Incongruently, it is still a challenge for authors and publishers to guarantee accessibility, support effective retrieval and the consumption of learning objects. One reason for this is that non-annotated learning objects pose a major problem with respect to their accessibility. Non-annotated objects not only prevent learners from finding new information; but also hinder a system's ability to recommend useful resources. To address this problem, commonly known as the cold-start problem, we automatically annotate specific learning resources using a state-of-the-art automatic tag annotation method: α-TaggingLDA, which is based on the Latent Dirichlet Allocation probabilistic topic model. We performed a user evaluation with 115 participants to measure the usability and effectiveness of α-TaggingLDA in a collaborative learning environment. The results show that automatically generated tags were preferred 35% more than the original authors' annotations. Further, they were 17.7% more relevant in terms of recall for users. The implications of these results is that automatic tagging can facilitate effective information access to relevant learning objects.
KW - Cold-Start
KW - LDA
KW - Metadata Generation
KW - Recommender Systems
KW - User Study
UR - http://www.scopus.com/inward/record.url?scp=80053112348&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23985-4_8
DO - 10.1007/978-3-642-23985-4_8
M3 - Conference contribution
AN - SCOPUS:80053112348
SN - 9783642239847
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 83
EP - 96
BT - Towards Ubiquitous Learning - 6th European Conference on Technology Enhanced Learning, EC-TEL 2011, Proceedings
T2 - 6th European Conference on Technology Enhanced Learning, EC-TEL 2011
Y2 - 20 September 2011 through 23 September 2011
ER -