Predicting User Locations and Trajectories

Location-based services usually recommend new locations based on the user’s current location or a given destination. However, human mobility involves to a large extent routine behavior and visits to already visited locations. In this paper, we show how daily and weekly routines can be modeled with basic prediction techniques. We compare the methods based on their performance, entropy and correlation measures. Further, we discuss how location prediction for everyday activities can be used for personalization techniques, such as timely or delayed recommendations.

Authors: Eelco Herder, Patrick Siehndel and Ricardo Kawase

PDF: herder-umap2014

To the Point: A Shortcut to Essential Learning

The volume of information on the Web is constantly growing. Consequently, finding specific pieces of information becomes a harder task. Wikipedia, the largest online reference Website is beginning to witness this phenomenon. Learners often turn to Wikipedia in order to learn facts regarding different subjects. However, as time passes, Wikipedia articles get larger and specific information gets more difficult to be located. In this work, we propose an automatic annotation method that is able to precisely assign categories to any textual resource. Our approach relies on semantic enhanced annotations and Wikipedia’s categorization schema. The results of a user study shows that our proposed method provides solid results for classifying text and provides a useful support for locating information. As implication, our research will help future learners to easily identify desired learning topics of interest in large textual resources.

Authors: Ricardo Kawase, Patrick Siehndel and Bernardo Pereira Nunes

PDF: kawase-icalt2014

A Topic Extraction Process for Online Forums

Ricardo Kawase presenting @ICALT2014 (picture taken by Mikhail Fominykh)

Forums play a key role in the process of knowledge creation, providing means for users to exchange ideas and to collaborate. However, educational forums, along several others online educational environments, often suffer from topic disruption. Since the contents are mainly produced by participants (in our case learners), one or a few individuals might change the course of the discussions. Thus, realigning the discussed topics of a forum thread is a task often conducted by a tutor or moderator. In order to support learners and tutors to harmonically align forum discussions that are pertinent to a given lecture or course, in this paper, we present a method that combines semantic technologies and a statistical method to find and expose relevant topics to be discussed in online discussion forums.

Authors: Bernardo Pereira Nunes, Alexander Arturo Mera Caraballo, Ricardo Kawase, Besnik Fetahu, Marco Antonio Casanova and Gilda Helena Bernardino De Campos

PDF: nunes-icalt2014