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

Answering Confucius: The Reason Why We Complicate

Ricardo Kawase presenting @ECTEL2013

Learning is a level-progressing process. In any field of study, one must master basic concepts to understand more complex ones. Thus, it is important that during the learning process learners are presented and challenged with knowledge which they are able to comprehend (not a level below, not a level too high). In this work we focus on language learners. By gradually improving (complicating) texts, readers are challenged to learn new vocabulary. To achieve such goals, in this paper we propose and evaluate the ‘complicator’ that translates given sentences to a chosen level of higher degree of difficulty. The ‘complicator’ is based on natural language processing and information retrieval approaches that perform lexical replacements. 30 native English speakers participated in a user study evaluating our methods on an expert-tailored dataset of children books. Results show that our tool can be of great utility for language learners who are willing to improve their vocabulary.

Authors: Bernardo Pereira Nunes, Stella Pedrosa, Ricardo Kawase, Mohammad Alrifai, Ivana Marenzi, Stefan Dietze and Marco Antonio Casanova

PDF: nunes-ectel2013

Automatic Competence Leveling of Learning Objects

Ricardo Kawase presenting @ICALT2013

A competence is the effective performance in a domain at different levels of proficiency. Educational institutions apply competences to understand whether a person has a particular level of ability or skill. Educational resource enriched with competence information allows learners identifying, on a fine-grained level, which resources to study with the aim to reach a specific competence target. 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 levels. To solve these problems, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. We demonstrate the quality of the proposed methods through an evaluation on real world data with an additional user study. Results show that the automatic competence level assignment achieves 84% precision on ground truth data. The key implications of our approach are: first, it effectively facilitates experts in the arduous task of competence assignment and second, it directly supports learners to retrieve proper leveled material.

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

PDF: kawase-icalt2013a

As Simple as It Gets – A Sentence Simplifier for Different Learning Levels and Contexts

This paper presents a text simplification method that transforms complex sentences into simplified forms. Our method uses NLP-techniques to simplify the text based on the target audience context, improving its overall understandability. We evaluate our approach in two aspects: grammatical structure and understandability. In both aspects, our approach achieved good results, showing its applicability to the learning process.

Authors: Bernardo Pereira Nunes, Ricardo Kawase, Patrick Siehndel, Marco A. Casanova and Stefan Dietze

PDF: nunes-icalt2013

Content-Based Movie Recommendation within Learning Contexts

A good movie is like a good book. As a good book can serve entertaining and learning purposes, so does a movie. In addition to that, movies are in general more engaging and reach a wider audience. In this work, we present and evaluate a method that overcomes the challenge of generating recommendations among heterogeneous resources. In our case, we recommend movies in the context of a learning object. We evaluate our method with 60 participants that judged the relevance of the recommendations. Results show that, in over 74% of the cases the recommendations are in fact related to the given learning object, outperforming a text-based recommendation approach. The implications of our work can take learning outside the classroom and invoke it during the joy of watching a movie.

Authors: Ricardo Kawase, Bernardo Pereira Nunes, Patrick Siehndel

PDF: kawase-icalt2013b

Finding relevant missing references in learning courses.

Reference sites play an increasingly important role in learning processes. Teachers use these sites in order to identify topics that should be covered by a course or a lecture. Learners visit online encyclopedias and dictionaries to find alternative explanations of concepts, to learn more about a topic, or to better understand the context of a concept. Ideally, a course or lecture should cover all key concepts of the topic that it encompasses, but often time constraints prevent complete coverage. In this paper, we propose an approach to identify missing references and key concepts in a corpus of educational lectures. For this purpose, we link concepts in educational material to the organizational and linking structure of Wikipedia. Identifying missing resources enables learners to improve their understanding of a topic, and allows teachers to investigate whether their learning material covers all necessary concepts.

Authors:  Patrick Siehndel, Ricardo Kawase, Asmelash Teka Hadgu and Eelco Herder.

PDF: siehndel-www13-lile13

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

Annotation Tool for Enhancing E-Learning Courses

One of the most popular forms of learning is through reading and for years we have used hard copy documents as the main material to learn. With
the advent of the Internet and the fast development of new technologies, new tools have been developed to assist the learning process. However,
reading is still the main learning method that is an individual activity. In this paper we propose a highlighting tool that enables the reading
and learning process to become a collaborative and shared activity. In other words, the highlighting tool supports the so-called active-reading,
a well-known and efficient means of learning. The highlighting tool brings to the digital environment the same metaphor of the traditional
highlight marker and puts it in a social context. It enables users to emphasize certain portions of digital learning objects. Furthermore, it
provides students, tutors, course coordinators and educational institutions new possibilities in the teaching and learning process. In this work
we expose the first quantitative and qualitative results regarding the use of the highlight tool by over 750 students through 8 weeks of
courses.

Venue: ICWL2012

Authors:  Bernardo Pereira Nunes, Ricardo Kawase, Stefan Dietze, Gilda Helena Bernardino de Campos and Wolfgang Nejdl

Boosting Retrieval of Digital Spoken Content

Every day, the Internet expands as millions of new multimedia objects are uploaded in the form of audio, video and images. While traditional text-based content is indexed by search engines, this indexing cannot be applied to audio and video objects, resulting in a plethora of multimedia content that is inaccessible to a majority of online users. To address this issue, we introduce a technique of automatic, semantically enhanced, description generation for multimedia content. The objective is to facilitate indexing and retrieval of the objects with the help of traditional search engines. Essentially, the technique generates static Web pages automatically, which describe the content of the digital audio and video objects. These descriptions are then organized in such a way as to facilitate locating corresponding audio and video segments. The technique employs a combination of Web services and concurrently provides description translation and semantic enhancement. Thorough analysis of the click-data, comparing accesses to the digital content before and after automatic description generation, suggests a significant increase in the number of retrieval items. This outcome, however is not limited to the terms of visibility, but in supporting multilingual access, additionally decreases the number of language barriers.

Venue: KES (Selecte Papers) 2012

Authors:  Bernardo Pereira Nunes, Alexander Mera, Marco A. Casanova and Ricardo Kawase

PDF: nunes-kes(selected)2012