The use of Crowdsourcing to solve public problems is called Citizen-Sourcing and shows the potential to increase citizen participation in the context of e-government. Successful implementations of Citizen-Sourcing applications require the citizen to continuously engage with each other and with the government through these applications. In general, citizens expect, among other things, that the government responds to their comments in an application by immediately solving the problems pointed out or indicating when and how they would be solved. In a literature review, we could not find any model for adequately assessing the quality of the service provided by local e-governments for citizens in Citizen-Sourcing scenarios. Hence, we propose an approach to analyze the quality of the government's response to the citizen through Citizen-Sourcing applications. To validate the proposed approach, we have conducted a case study using real data collected from Colab.re, a Citizen-Sourcing application very popular in Brazil. Our main contributions are a novel approach to assess the quality of interaction between local e-government and citizen and a detailed discussion regarding a local e-government's action in the platform.

The growing practice of accumulating personal data to generate predictions about users, leverages the need for mechanisms that allow people a more effective control of their data.An emerging field of studies called Human-Data Interaction (HDI), proposes the inclusion of human at the center of the data flow, providing mechanisms for citizens to interact explicitly with the collected data.Researches in HDI have discussed ways to offer Transparency Enhancing Tools (TETs), i.e., tools that support people on HDI issues related to privacy and personal data protection.Many works conducted about TETs focuses on usability issues, exploring aspects such as efficiency, user satisfaction and ease of learning.In this work, on the other hand, we aim to assess the communicability of HDI mechanisms in TETs.Hence, we applied the Semiotic Inspection Method (SIM) to investigate if and how HDI concepts are applied in two different TETs used for personal data management.We triangulated results from the study with findings from another investigation about communicability issues carried out in the same domain, but by observing and interviewing users.

In this paper, we report the developments of a Computational Thinking Acquisition project carried out in pilot Brazilian schools. The project is a branch of a successful, more than a decade old project in the USA. We present and discuss the factors that led to specific cultural appropriation and diversification of the North American experience. In particular, we explain the kind of technology that has been developed in South America compared to the one developed and used in the USA, and propose that the lessons we have learned in the project's short history in Brazil can already seed the reflection of IT and Education researchers.

Using game design and programming to foster computational thinking acquisition has proved to be a successful strategy in recent years. In previous research with AgentSheets, we concluded that the semiotic richness of this visual programming environment, specifically designed to support computational thinking acquisition, could be explored more extensively to the benefit of learners. In particular, we realized that there are some additional representations of AgentSheets games and simulations that are not presented as programming tools in the interface, and yet they communicate new relevant meanings to the users. This paper reports on research where we artificially introduced such representations in a small follow-up experiment with selected participants from our previous research experiment. Our goal was to investigate the impact of such additional representations on program comprehension and modification tasks. To this end we contrasted empirical evidence of their performance in the two tasks with their verbal account of experience with AgentSheets. We used a combination of discourse analysis and inspections using Semiotic Engineering methods and the Cognitive Dimensions of Notations framework. Our findings go in two directions. First, we observed that additional representations have allowed participants to expand and correct previous learning. Therefore such representations can support new teaching strategies in computational thinking acquisition programs with AgentSheets. Second, we learned that the combination of methods we used to analyze empirical data - discourse analysis with semiotic and cognitive inspection techniques - can be used systematically in other research contexts, holding the promise of insightful results.