Abstract

From a digital culture perspective, this article has as main objective to assess two contemporary qualitative research methods in the field of education with distinct theoretical orientations: the cartographic method as a way of tracing trajectories in research-intervention with a theoretical basis in the biology of knowledge, enactive cognition and inventive cognition; and the cartographic method as a means of identifying and mapping the controversies linked to the different associations between human and non-human actors with a theoretical basis in actor-network theory (ANT). With their own specificities, both methods have been fruitful in the development of qualitative research in the field of education, in the context of digital culture, and more recently, in the hybrid culture of atopic habitation, mainly because they also relate to equally consistent theories and aspects of human cognition, making it possible to detect traces and clues in the fluid associations between actors enhanced by different digital technologies (DT), including data mining and learning analytics. From the Brazilian perspective on the topic, this article approaches the experience of the cartographic method of research intervention as well as the cartography of controversies as tools for developing qualitative research in education. These different forms of the cartographic method have inspired the construction of didactic-pedagogical experiences based on theoretical approaches linked to cognition, producing inventive methodologies and interventionist pedagogical practices. These methodologies and practices, which will be discussed at length in this article, have been developed and validated by the Research Group in Digital Education at Unisinos University at different levels and in varied educational settings.

Abstract
O artigo tem como objetivo discutir uma proposta de formação continuada de professores em contexto híbrido e multimodal, em sua potencialidade de mobilizar a ressignificação das práticas pedagógicas por meio de projetos de aprendizagem gamificados. A proposta integra as ações da pesquisa “A cidade como espaço de aprendizagem: games e gamificação na constituição de Espaços de Convivência Híbridos, Multimodais, Pervasivos e Ubíquos para o desenvolvimento da Cidadania”, que tem como objetivo contribuir para com a mudança da cultura escolar, tendo em vista os atuais sujeitos de aprendizagem, os meios com os quais interagem e suas formas de aprender. A abordagem da pesquisa é qualitativa, do tipo descritiva e inspirada no método cartográfico de pesquisa intervenção proposto por Kastrup (2007; 2008), Passos, Kastrup e Escóssia (2009) e Passos, Kastrup e Tedesco (2014). Os resultados evidenciados pelos movimentos que os professores articulam nas escolas, indicam que a proposta tem contribuído significativamente para aproximar professores da educação básica, pesquisadores e professores do ensino superior enquanto co-criadores de saberes acerca da docência, potencializando a construção de práticas pedagógicas agregativas e reticulares e contribuindo para a mudança da cultura escolar!

Abstract
Este artigo apresenta parte de uma pesquisa, em andamento, financiada pelo Edital CAPES/FAPITEC/SE N° 10/2016 - Programa de Estímulo a Mobilidade e ao Aumento da Cooperação Acadêmica da Pós-Graduação em Sergipe. Esta pesquisa, desenvolvida em rede, tem como finalidade a promoção de ações colaborativas e cooperativas, na perspectiva da compreensão sobre como a cidade pode se constituir em espaços de convivência híbridos e multimodais de aprendizagem no âmbito da gamificação na educação na era da mobilidade. Participam desta investigação três universidades brasileiras Universidade Federal de Sergipe (UFS), Universidade do Vale do Rio dos Sinos (Unisinos) e Universidade Federal de Minas Gerais (UFMG) e seus respectivos programas de pós-graduação, linhas e grupos de pesquisa. Como resultado, parcial, observamos um gap existente entre o mundo das crianças e adolescentes e o mundo da escola (o que envolve a formação docente em diferentes níveis), que apesar de seu caráter histórico, precisa problematizar as dimensões da instabilidade tecnológica vivenciadas pela juventude.

Abstract

Abstract
Deterministic Synchronous Multichannel Extension (DSME) is a prominentMAC behavior first introduced in IEEE 802.15.4e supporting deterministic guarantees using its multisuperframe structure. DSME also facilitates techniques like multi-channel and Contention Access Period (CAP) reduction to increase the number of available guaranteed timeslots in a network. However, any tuning of these functionalities in dynamic scenarios is not explored in the standard. In this paper, we present a multisuperframe tuning technique called DynaMO which tunes the CAP reduction and Multisuperframe Order in an effective manner to improve flexibility and scalability, while guaranteeing bounded delay. We also provide simulations to prove that DynaMO with its dynamic tuning feature can offer up to 15-30% reduction in terms of latency in a large DSME network.

Abstract
Deterministic Synchronous Multichannel Extension (DSME) is a prominent MAC behavior first introduced in IEEE 802.15.4e. It can avail deterministic and best effort Service using its multisuperframe structure. RPL is a routing protocol for wireless networks with low power consumption and generally susceptible to packet loss. These two standards were designed independently but with the common objective to satisfy the requirements of IoT devices in terms of limited energy, reliability and determinism. A combination of these two protocols can integrate real-time QoS demanding and largescale IoT networks. In this paper, we propose a new multi-channel, multi-timeslot scheduling algorithm called Symphony that provides QoS efficient schedules in DSME networks. In this paper we provide analytical and simulation based delay analysis for our approach against some state of the art algorithms. In this work, we show that integrating routing with DSME can improve reliability by 40 % and by using Symphony, we can reduce the network delay by 10-20% against the state of the art algorithms.