A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts

Um Modelo para a visualização de conhecimento baseado em imagens semânticas

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia e Gestão do ConhecimentoOs avanços no processamento e gerenciamento eletrônico de documentos têm gerado um acúmulo grande de conhecimento que tem excedido o que os usuários comuns podem perceber. Uma quantidade considerável de conhecimento encontra-se explicitado em diversos documentos armazenados em repositórios digitais. Em muitos casos, a possibilidade de acessar de forma eficiente e reutilizar este conhecimento é limitada. Como resultado disto, a maioria do conhecimento não é suficientemente explorado nem compartilhado, e conseqüentemente é esquecido em um tempo relativamente curto. As tecnologias emergentes de visualização e o sistema perceptual humano podem ser explorados para melhorar o acesso a grandes espaços de informação facilitando a detecção de padrões. Por outro lado, o uso de elementos visuais que contenham representações do mundo real que a priori são conhecidos pelo grupo-alvo e que fazem parte da sua visão de mundo, permite que o conhecimento apresentado por meio destas representações possa facilmente ser relacionados com o conhecimento prévio dos indivíduos, facilitando assim a aprendizagem. Apesar das representações visuais terem sido usadas como suporte para a disseminação do conhecimento, não têm sido propostos modelos que integrem os métodos e técnicas da engenharia do conhecimento com o uso das imagens como meio para recuperar e visualizar conhecimento. Neste trabalho apresenta-se um modelo que visa facilitar a visualização do conhecimento armazenado em repositórios digitais usando imagens semânticas. O usuário, através das imagens semânticas, pode recuperar e visualizar o conhecimento relacionado às entidades representadas nas regiões das imagens. As imagens semânticas são representações visuais do mundo real as quais são conhecidas previamente pelo grupo alvo e possuem mecanismos que permitem identificar os conceitos do domínio representados em cada região. O modelo proposto apóia-se no framework para visualização do conhecimento proposto por Burkhard e descreve as interações dos usuários com as imagens. Um protótipo foi desenvolvido para demonstrar a viabilidade do modelo usando imagens no domínio da anatomia, a Foundational Model of Anatomy e a Unified Medical Language System como conhecimento do domínio e o banco de dados da Scientific Electronic Library Online como repositório de documento.Advances in processing and electronic document management have generated a great accumulation of knowledge that is beyond what ordinary users can understand. A considerable amount of knowledge is explained in various documents stored in digital repositories. In many cases, the ability to eficiently access and reuse this knowledge is limited. As a result, most knowledge is not exploited or shared, and therefore it is forgotten in a relatively short time. The emerging technologies of visualization and the human perceptual system can be exploited to improve access to large information spaces facilitating the patterns detection. Moreover, the use of visual elements that contain representations of the real world that are known a priori by the target group and that are part of his world view, allows that the knowledge presented by these representations can be easily related to their prior knowledge, thereby facilitating learning. Despite visual representations have been used to support knowledge dissemination, no models have been proposed to integrate knowledge engineering methods and techniques with the use of images as a medium to retrieve and display knowledge. This work presents a model that aims to facilitate the visualization of the knowledge stored in digital repositories using semantic images. Through the semantic images, the user can retrieve and visualize the knowledge related to the entities represented in the image regions. The semantic images are visual representations of the real world which are known in advance by the target group and have mechanisms to identify domain concepts represented in each region. The proposed model is based on the framework for visualization of knowledge proposed by Burkhard and describes the interactions of users with the images. A prototype was eveloped to demonstrate the feasibility of the model using archetypes in the field of anatomy, using the Foundational Model of Anatomy and the Unifiled Medical Language System as knowledge domain and the database of the Scientific Electronic Library Online as a document repository