Towards Automatic Competence Assignment of Learning Objects

Ricardo Kawase presenting @ECTEL2012. (picture taken by Maren Scheffel)

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.

Ricardo Kawase, Peter Brusilovsky and Mikhail Fominykh. (picture taken by Maren Scheffel)

Venue: ECTEL2012

Authors: Ricardo Kawase, Patrick Siehndel, Bernardo Pereira Nunes, Marco Fisichella and Wolfgang Nejdl

PDF: kawase-ectel2012

Unsupervised Auto-tagging for Learning Object Enrichment

Ricardo Kawase presenting @ECTEL2011

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.

Venue: ECTEL2011

Authors:  Ernesto Diaz-Aviles, Marco Fisichella, Ricardo Kawase, Wolfgang Nejdl, Avaré Stewart

Award: ECTEL2011 Best Paper

PDF:  diaz-ectel2011