Constrangimentos no posto do motorista de ônibus urbano segundo a visão macroergonômica

Este estudo trata da abordagem macroergonômica participativa para a identificação das demandas ergonômicas dos motoristas de ônibus urbano da cidade de Joinville, com utilização da metodologia participativa da Análise Macroergonômica do Trabalho (AMT) (GUIMARÃES, 2001c) e ferramental proposto no Design Macroergonômico (DM) (FOGLIATTO E GUIMARÃES, 1999). O estudo de caso foi realizado em uma empresa privada de transporte coletivo da cidade de Joinville. A aplicação da metodologia permitiu identificar as demandas ergonômicas prioritárias levantadas pelos motoristas de ônibus urbano da cidade de Joinville e os itens de design do seu posto de trabalho através da fase de apreciação. As demandas ergonômicas, bem como os itens de design foram comparados através do Teste Exato de Fisher com determinadas características da população constatando-se algumas associações significativas entre a satisfação dos motoristas e as variáveis que compõe cada construto. Estes resultados possibilitaram a formulação de recomendações que viabilize, em estudos futuros, a introdução de melhorias para o aumento da qualidade de vida dos motoristas. Os estudos também permitiram identificar uma afinidade da metodologia participativa com os motoristas de ônibus urbano, em que as mudanças podem ocorrer de forma gradativa e experiencial através de protótipos no caso das demandas referentes à posto de trabalho e físico ambiental, ou através de possíveis adaptações no conteúdo da tarefa do motorista no caso das demandas referentes à organização do trabalho. Tudo isto vislumbrando o atendimento, por ordem de importância, dos itens de demanda ergonômica levantados. Por fim, concluiu-se que para os motoristas de Joinville alguns fatores referentes a organização do trabalho estão entre os principais causadores dos constrangimentos aos quais são expostos enquanto executam sua tarefa, seguido por fatores físicos ambientais e posto do trabalho.

Combining collaborative and content-based filtering to recommend research papers

The number of research papers available today is growing at a staggering rate, generating a huge amount of information that people cannot keep up with. According to a tendency indicated by the United States’ National Science Foundation, more than 10 million new papers will be published in the next 20 years. Because most of these papers will be available on the Web, this research focus on exploring issues on recommending research papers to users, in order to directly lead users to papers of their interest. Recommender systems are used to recommend items to users among a huge stream of available items, according to users’ interests. This research focuses on the two most prevalent techniques to date, namely Content-Based Filtering and Collaborative Filtering. The first explores the text of the paper itself, recommending items similar in content to the ones the user has rated in the past. The second explores the citation web existing among papers. As these two techniques have complementary advantages, we explored hybrid approaches to recommending research papers. We created standalone and hybrid versions of algorithms and evaluated them through both offline experiments on a database of 102,295 papers, and an online experiment with 110 users. Our results show that the two techniques can be successfully combined to recommend papers. The coverage is also increased at the level of 100% in the hybrid algorithms. In addition, we found that different algorithms are more suitable for recommending different kinds of papers. Finally, we verified that users’ research experience influences the way users perceive recommendations. In parallel, we found that there are no significant differences in recommending papers for users from different countries. However, our results showed that users’ interacting with a research paper Recommender Systems are much happier when the interface is presented in the user’s native language, regardless the language that the papers are written. Therefore, an interface should be tailored to the user’s mother language.

Ontology-based metadata for e-learning content

Nowadays, the popularity of the Web encourages the development of Hypermedia Systems dedicated to e-learning. Nevertheless, most of the available Web teaching systems apply the traditional paper-based learning resources presented as HTML pages making no use of the new capabilities provided by the Web. There is a challenge to develop educative systems that adapt the educative content to the style of learning, context and background of each student. Another research issue is the capacity to interoperate on the Web reusing learning objects. This work presents an approach to address these two issues by using the technologies of the Semantic Web. The approach presented here models the knowledge of the educative content and the learner’s profile with ontologies whose vocabularies are a refinement of those defined on standards situated on the Web as reference points to provide semantics. Ontologies enable the representation of metadata concerning simple learning objects and the rules that define the way that they can feasibly be assembled to configure more complex ones. These complex learning objects could be created dynamically according to the learners’ profile by intelligent agents that use the ontologies as the source of their beliefs. Interoperability issues were addressed by using an application profile of the IEEE LOM- Learning Object Metadata standard.