Syftet med denna undersökning var att ge en överskådlig syn på hur elever tänker kring användandet av internet. Undersökningen belyser även skillnader för informationssökning privat och i skolan, samt skillnader mellan teoretiska och praktiska program. Detta gjordes med hjälp av en kvantitativ metod där respondenterna fick fylla i en enkät. Resultatet har visat på att elever generellt sett använder både Wikipedia och Google i sitt sökande efter information. Det förekommer även en del elever som tror att censur gäller på internet. Eleverna är även ytterst målmedvetna när de söker information och prioriterar högt att informationen skall vara pedagogiskt utformad. Slutsatserna som har dragits utifrån denna undersökning är att eleverna uppvisar typiska beteenden som stämmer överens med vad som kännetecknar Google generationen. Samt att det finns skillnader mellan programmen som är av vikt.

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Automação e Sistemas, Florianópolis, 2011 / Made available in DSpace on 2012-10-26T08:08:52Z (GMT). No. of bitstreams: 1 293606.pdf: 1473397 bytes, checksum: e6441ebfd3c12b3f7a56e231da67af55 (MD5) / A qualidade das interações entre agentes é um fator determinante na eficiência de um sistema multiagente. Uma importante ferramenta utilizada para melhorar a qualidade das interações é a reputação. Dentre os diversos modelos para a reputação, o modelo ForTrust destaca-se por sua excelente fundamentação teórica. Este modelo ainda não tem uma validação feita por meio de implementação e experimentação, carecendo inclusive de uma arquitetura computacional que o viabilize. Nesta direção, o presente trabalho apresenta uma arquitetura de implementação para o ForTrust usando a abordagem de Agentes e Artefatos. Nela, artefatos são estruturas passivas e orientadas a funções compartilhadas por um grupo de agentes como mecanismo de cooperação e coordenação na formação da reputação. A proposta de tais artefatos é demonstrada em alguns estudos de caso, em especial no auxílio dos patrulheiros da Wikipédia. A arquitetura apresentada neste trabalho se mostra de fácil utilização uma vez que obedece a uma série de requisitos específicos observados. Seu funcionamento é corroborado pela sua implementação no auxílio aos patrulheiros da Wikipédia.

As the fifth most visited website on the internet today, the user-generated encyclopaedia Wikipedia has attracted significant attention from researchers based across the academic disciplines. However, this previous work has generally neglected the multilingual aspects of the encyclopaedia, and downplayed the significance of translation within the collaborative processes of content creation that take place within the platform. Consequently, this doctoral thesis has set out to investigate the people and practices involved in the co-production and dissemination of knowledge across linguistic and cultural borders in the context of Wikipedia. By developing an analytical approach that combines narrative theory (Baker 2006) with insights drawn from the work of Henri Lefebvre (1974/1991) and Michel Foucault (1967/1986), I have examined the construction of 27 city-related articles published within the English- and French-language editions of the platform. This analysis has shown that Wikipedia volunteers make abundant use of source materials published in a diversity of languages other than that in which they are writing, and that the creation of these encyclopaedic articles involves a wide range of partly overlapping multilingual activities, a complex combination of translating, re-contextualising, summarising and synthesising. My study has also brought to light the multi-faceted negotiations that occur between the different participants involved in these processes of intersubjective knowledge production. This has drawn attention to Wikipedia as a space of discordant juxtaposition and creative simultaneity, and foregrounded the cacophony of opposing narrative positionings present within each article-focused community of multilingual produsers.

This portfolio contains three separate research essays and a reflection that are related through a common theme, that of ethos. The research papers include: (1) an essay on the pedagogical value of Wikipedia when teaching students about developing authorial ethos; (2) a research essay on the value of writing centers employing tutors whose writing is not yet proficient in the standards set for academic discourse; and (3) a research essay on the use of blogs in first-year composition courses as a means of fostering agency, ownership of ideas, audience awareness, and metadiscourse use. These papers represent the variety of research I undertook during the M.A. program as well as my research interests moving toward the future.

Syftet med denna undersökning är att studera svensklärares och samhällsvetenskapslärares förhåll-ningssätt till Wikipedia och därmed belysa en del av de utmaningar som skolan ställs inför det rå-dande medielandskapet. Frågeställningarna är inriktade på lärares attityder och resonemang kring Wikipedia, samt vilka konsekvenser lärarna anser att användningen av det får sin i undervisning. Eventuella skillnader mellan svensklärares och samhällsvetenskapslärares förhållningssätt gentemot Wikipedia tas också upp.Metoden för undersökningen är kvalitativa intervjuer. Dessa genomfördes med fyra lärare, två svensklärare och två samhällsvetenskapslärare, från en och samma gymnasieskola. Resultatet ger en bild av hur diskursen kring Wikipedia kan se ut i skolan och hur de olika lärarna resonerar kring det. Lärarna ser olika för- och nackdelar med Wikipedia i undervisning och dessa behandlas i analysen. Analysen visar att det finns en konflikt mellan de traditioner som tidigare gett skolan och lärarna makten att kontrollera och distribuera kunskap. Nya kulturella förutsättningar och faktumet att vi lever i en deltagarkultur resulterar i att fler människor kan sprida kunskap och information, via ex-empelvis Wikipedia. I synnerhet en av lärarna poängterar brister i Wikipedias pålitlighet och en annan ser demokratiaspekten av det som en viktig aspekt. Olika för-och nackdelar med Wikipedias form tas också upp, t.ex. vad dess hypertextualitet kan ha för pedagogiska konsekvenser.Slutsatsen är att lärarna inte har något enhetligt förhållningssätt gentemot Wikipedia. Wikipedia verkar dessutom, trots att det är ett nytt fenomen, harmonisera väl med traditionella skoluppgifter som handlar om att reproducera fakta, vilket också pekar mot en relativt snäv kunskapssyn.

Latent Dirichlet Allocation (LDA) is widely used for automatic discovery of latent topics in document corpora. However, output from analysis using an LDA topic model suffers from a lack of identifiability between topics not only across corpora, but across runs of the algorithm. The output is also isolated from enriching information from knowledge sources such as Wikipedia and is difficult for humans to interpret due to a lack of meaningful topic labels. This thesis introduces two methods for probabilistic explicit topic modeling that address these issues: Latent Dirichlet Allocation with Static Topic-Word Distributions (LDA-STWD), and Explicit Dirichlet Allocation (EDA). LDA-STWD directly substitutes precomputed counts for LDA topic-word counts, leveraging existing Gibbs sampler inference. EDA defines an entirely new explicit topic model and derives the inference method from first principles. Both of these methods approximate topic-word distributions a priori using word distributions from Wikipedia articles, with each article corresponding to one topic and the article title being used as a topic label. By this means, LDA-STWD and EDA overcome the nonidentifiability, isolation, and unintepretability of LDA output. We assess the effectiveness of LDA-STWD and EDA by means of three tasks: document classification, topic label generation, and document label generation. Label quality is quantified by means of user studies. We show that a competing non-probabilistic explicit topic model handily beats both LDA-STWD and EDA as a dimensionality reduction technique in a document classification task. Surprisingly, we find that topic labels from another approach using LDA and post hoc topic labeling (called LDA+Lau) are on one corpus preferred over topic labels prespecified from Wikipedia. Finally, we show that LDA-STWD improves substantially upon the performance of the state of the art in document labeling.

Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. Word sense disambiguation is the task of automatically assigning the most appropriate meaning to a polysemous word within a given context. Generally the problem of resolving ambiguity in literature has revolved around the famous quote “you shall know the meaning of the word by the company it keeps.” In this thesis, we investigate the role of context for resolving ambiguity through three different approaches. Instead of using a predefined monolingual sense inventory such as WordNet, we use a language-independent framework where the word senses and sense-tagged data are derived automatically from Wikipedia. Using Wikipedia as a source of sense-annotations provides the much needed solution for knowledge acquisition bottleneck. In order to evaluate the viability of Wikipedia based sense-annotations, we cast the task of disambiguating polysemous nouns as a monolingual classification task and experimented on lexical samples from four different languages (viz. English, German, Italian and Spanish). The experiments confirm that the Wikipedia based sense annotations are reliable and can be used to construct accurate monolingual sense classifiers. It is a long belief that exploiting multiple languages helps in building accurate word sense disambiguation systems. Subsequently, we developed two approaches that recast the task of disambiguating polysemous nouns as a multilingual classification task. The first approach for multilingual word sense disambiguation attempts to effectively use a machine translation system to leverage two relevant multilingual aspects of the semantics of text. First, the various senses of a target word may be translated into different words, which constitute unique, yet highly salient signal that effectively expand the target word’s feature space. Second, the translated context words themselves embed co-occurrence information that a translation engine gathers from very large parallel corpora. The second approach for multlingual word sense disambiguation attempts to reduce the reliance on the machine translation system during training by using the multilingual knowledge available in Wikipedia through its interlingual links. Finally, the experiments on a lexical sample from four different languages confirm that the multilingual systems perform better than the monolingual system and significantly improve the disambiguation accuracy.

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