Abstract
In this paper we propose a model for assessing the quality of educational collaborative virtual environments. The objective is to establish a theoretical model that highlights a number of relevant sets of requirements in relation to the quality in educational collaborative virtual environments. It is intended to apply the model during the lifecycle of product development and in the selection of the environment in order to support the learning/teaching process.

Abstract
The collaborative virtual worlds can be used to achieve different purposes according to the intended use. The design of 3D collaborative virtual environments for learning has been an important research field for several years. However, the research in this specific field has shown that in most of the cases, the environments do not have appropriate technical characteristics. The design of the educational collaborative virtual environments goal is to produce environments that encourage users to achieve effective learning. In this sense, the current study presents a design model for development of 3D Educational Collaborative Virtual Environments. This model is based on the engineering software techniques and methods. It is supported by a spiral cycle that allows us to develop applications. The process is divided into a set of activities that are being carried out throughout each cycle, producing several work products, with the aim to provide each team member a set of guidelines and tools necessary for to make intelligent decisions about what they do. The model is composed by five steps, namely: Conception, Analysis, Design, Implementation and Evaluation. Each step contains a set of diagrams to support the developer team in their tasks. With this model, the applications are developed in a series of incremental releases, that is, the final system is constructed, based on the refined prototype. These steps include activities that enable to quantify the quality of Educational Collaborative Virtual Environments (ECVE). It is based on the Quantitative Evaluation Framework developed by Escudeiro (Escudeiro, 2007) and allow us to have a degree of freedom in the selection of quality criteria. Thus, we can obtain a single quantitative value of quality for any domain in analysis, i.e., we can adapt it in any domain and valence.

Abstract
The importance of entrepreneurship education from elementary school through college is now recognized as an important aspect of children's education. At the level of basic education, the development of entrepreneurial activities using Information and Communication Technologies, specifically three-dimensional virtual worlds, is seen as an area with potential for exploration. The research presented herein is a model that allows the development of entrepreneurial activities in virtual worlds with children attending primary education. This model allows the preparation, monitoring and development of entrepreneurship education activities in virtual worlds, including safe interaction in virtual worlds between the children and the community. For this, we identified a set of requirements that would allow the teaching and learning of entrepreneurship in virtual worlds, from which a technological model was implemented through an application, EMVKids (after the Portuguese expression "Empreendedorismo em Mundos Virtuais com Criancas", entrepreneurship with children in virtual worlds).

Abstract
Current times are denoting unprecedented indicators of scientific data production, and the involvement of the wider public (the crowd) on research has attracted increasing attention. Drawing on review of extant literature, this paper outlines some ways in which crowdsourcing and mass collaboration can leverage the design of intelligent systems to keep pace with the rapid transformation of scientific work. A systematic literature review was performed following the guidelines of evidence-based software engineering and a total of 148 papers were identified as primary after querying digital libraries. From our review, a lack of methodological frameworks and algorithms for enhancing interactive intelligent systems by combining machine and crowd intelligence is clearly manifested and we will need more technical support in the future. We lay out a vision for a cyberinfrastructure that comprises crowd behavior, task features, platform facilities, and integration of human inputs into AI systems. © Springer Nature Switzerland AG 2018.

Abstract
Technological evolution impacts the research and development of new solutions, as well as consumers’ expectations and behaviors. With the advent of the new millennium, collaboration systems and technologies were introduced to support ordinary cooperative work and inter-dependent, socially and culturally mediated practices as integral units of everyday life settings. Nevertheless, existing classification systems are limited in scope to analyze technological developments and capture the intellectual structure of a field, understood as an abstraction of the collective knowledge of its researchers and their socially mediated activities. Ten years after the introduction of Mittleman et al.’s taxonomy, we build upon earlier work and adopt this classification scheme to provide a descriptive, taxonomy-based analysis of four distinct venues focused on collaborative computing research: ACM CSCW, ACM GROUP, ECSCW, and CRIWG. The proposal consists of achieving evidence on technical attributes and impacts towards characterizing the evolution of socio-technical systems via (and for) taxonomic modeling. This study can also constitute an important step towards the emergence of new, potentially more valid and robust evaluation studies combining Grounded Theory with alternative methods and techniques. © Springer Nature Switzerland AG 2018.

Abstract
The increasing amount of scholarly literature and the diversity of dissemination channels are challenging several fields and research communities. A continuous interplay between researchers and citizen scientists creates a vast set of possibilities to integrate hybrid, crowd-machine interaction features into crowd science projects for improving knowledge acquisition from large volumes of scientific data. This paper presents SciCrowd, an experimental crowd-powered system under development “from the ground up” to support data-driven research. The system combines automatic data indexing and crowd-based processing of data for detecting topic evolution by fostering a knowledge base of concepts, methods, and results categorized according to the particular needs of each field. We describe the prototype and discuss its main implications as a mixed-initiative approach for leveraging the analysis of academic literature. © Springer Nature Switzerland AG 2018.