Name
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. |
Year | Venue | Keywords |
|---|---|---|
2011 | EC-TEL | cold-start problem,annotate specific learning resource,available online,automatic tagging,major problem,non-annotated learning object,relevant learning object,effective information access,new information,object enrichment,unsupervised auto-tagging,effective retrieval,cold start,recommender systems |
Field | DocType | Volume |
Recommender system,Latent Dirichlet allocation,Collaborative learning,Semi-supervised learning,Active learning (machine learning),Information retrieval,Computer science,Usability,Learning object,Topic model | Conference | 6964 |
ISSN | Citations | PageRank |
0302-9743 | 13 | 0.70 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ernesto Diaz-Aviles | 1 | 228 | 20.08 |
| Marco Fisichella | 2 | 80 | 12.38 |
| Ricardo Kawase | 3 | 100 | 9.99 |
| Wolfgang Nejdl | 4 | 6633 | 556.13 |
| Avaré Stewart | 5 | 111 | 10.56 |