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 -