93 resultados para Mobile-Learning
Resumo:
Esta dissertação tem como referência o trabalho que realizamos no Ministério Público de Pernambuco, onde temos a oportunidade de observar e conhecer os serviços de acolhimento institucional para crianças e adolescentes, bem como da atuação de outros atores que atuam nesta área de medida protetiva, no Estado de Pernambuco. Aqui articularemos esta prática com os achados históricos, teóricos e legais vigentes em busca de estabelecer estratégias e ações de intervenção, para atuarmos neste contexto que geograficamente tem pontos de articulações distantes. Buscamos neste estudo analisar todos os lados de uma construção para a execução de uma plataforma que possa oferecer um serviço de capacitação em e-learning aos profissionais que atuam na área protetiva. Para isto precisaremos identificar o perfil destes profissionais e reconhecer que se, mesmo diante das dificuldades de sua profissão, eles se dispõem em participar de capacitação on-line voltado para a prática. Analisou-se o blog como um sistema adequado para se trabalhar com e-learning recorrendo à literatura para identificar quais os critérios, parâmetros e indicadores que faz um blog institucional de qualidade. Dando corpo ao nosso intento, faremos um percurso pelas teorias da aprendizagem buscando consistência para investigarmos se novas abordagens como o Conectivismo, responde ou não aos nossos anseios de formação continuada na prática e pela prática.
Resumo:
Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.
Resumo:
The present study investigates peer to peer oral interaction in two task based language teaching classrooms, one of which was a self-declared cohesive group, and the other a self- declared less cohesive group, both at B1 level. It studies how learners talk cohesion into being and considers how this talk leads to learning opportunities in these groups. The study was classroom-based and was carried out over the period of an academic year. Research was conducted in the classrooms and the tasks were part of regular class work. The research was framed within a sociocognitive perspective of second language learning and data came from a number of sources, namely questionnaires, interviews and audio recorded talk of dyads, triads and groups of four students completing a total of eight oral tasks. These audio recordings were transcribed and analysed qualitatively for interactions which encouraged a positive social dimension and behaviours which led to learning opportunities, using conversation analysis. In addition, recordings were analysed quantitatively for learning opportunities and quantity and quality of language produced. Results show that learners in both classes exhibited multiple behaviours in interaction which could promote a positive social dimension, although behaviours which could discourage positive affect amongst group members were also found. Analysis of interactions also revealed the many ways in which learners in both the cohesive and less cohesive class created learning opportunities. Further qualitative analysis of these interactions showed that a number of factors including how learners approach a task, the decisions they make at zones of interactional transition and the affective relationship between participants influence the amount of learning opportunities created, as well as the quality and quantity of language produced. The main conclusion of the study is that it is not the cohesive nature of the group as a whole but the nature of the relationship between the individual members of the small group completing the task which influences the effectiveness of oral interaction for learning.This study contributes to our understanding of the way in which learners individualise the learning space and highlights the situated nature of language learning. It shows how individuals interact with each other and the task, and how talk in interaction changes moment-by-moment as learners react to the ‘here and now’ of the classroom environment.