Abstract
Open and networked education at Portuguese Open University is made up of a set of idiosyncrasies that are not immediately perceptible by new professors coming from face-to-face universities or by evaluators from A3ES (Agency for the Evaluation and Accreditation of Higher Education). This work presents the essential concepts of Distance Education with the fewest words and avoiding synonyms. In the teaching-learning process, the teacher’s role in digital contexts is detailed and the notion of learning is discussed. A teaching taxonomy is presented with an emphasis on practice in virtual communities. © 2023, Edicoes Universitarias Lusofonas. All rights reserved.

Abstract
Data quality is essential for a correct understanding of the concepts they represent. Data mining is especially relevant when data with inferior quality is used in algorithms that depend on correct data to create accurate models and predictions. In this work, we introduce the issue of errors of identifiers in an anonymous database. The work proposes a quality evaluation approach that considers individual attributes and a contextual analysis that allows additional quality evaluations. The proposed quality analysis model is a robust means of minimizing anonymization costs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The administrative management of a basketball club, even of small size, presents several challenges, with one of the most critical being the efficient organization of all generated information and documentation. In order to meet all the requirements surrounding sports management, the use of information systems in sports is indispensable for the success of an organization. This research involves modeling an information system in the form of a web application for the administrative content management of a sports club, ensuring the efficient organization and availability of all its information. This activity includes identifying the necessary resources and investment, developing a prototype, and evaluating potential impacts.;A gestão administrativa de um clube de basquetebol, mesmo que de pequena dimensão, apresenta vários desafios, sendo que um dos mais críticos é a organização eficiente de toda a informação e documentação gerada. De forma a responder a todas as exigências que rodeiam a gestão desportiva, o uso de sistemas de informação no desporto é indispensável para o sucesso de uma organização. Esta investigação passa por efetuar a modelação de um sistema de informação, em forma de aplicação web, para gestão do conteúdo administrativo de um clube desportivo, de modo a garantir a organização e disponibilização de toda a sua informação de forma eficiente. Esta atividade deverá compreender a identificação dos recursos e investimento necessários, o desenvolvimento de um protótipo e a avaliação dos potenciais impactos.

Abstract
Machine Learning is a rising concept in today's society. In the past decade, ML-based systems have become part of people's daily routines, and their usage has been disseminated through diverse sectors. This evolution is supported by the exponential increase in data created worldwide. Feature Engineering is a critical process focused on transforming data into suitable inputs for Machine Learning algorithms. This work explores the Feature Engineering process by developing a baseline for its implementation. Hence, a pipeline of Feature Engineering techniques and their taxonomy is proposed, along with a set of R scripts to implement. The validity of the code is then demonstrated through its application to a real-world dataset.;MachineLearning é um conceito em crescente evolução na sociedade atual. Na última década, os sistemas baseados em ML tornaram-se parte do quotidiano da população e a sua aplicação tem vindo a disseminar-se por diversos setores. Este crescimento é suportado pelo aumento exponencial da quantidade de dados gerados a nível mundial. FeatureEngineering surge, assim, como um processo chave que permite transformar dados em inputs adequados para os algoritmos de MachineLearning. O presente trabalho pretende explorar o processo de FeatureEngineering, com vista a desenvolver uma base de suporte à sua implementação. Por conseguinte, é proposta uma pipeline de técnicas de FeatureEngineering em paralelo com a sua taxonomia, juntamente com um conjunto de scripts R, para as implementar. A validade do código é, posteriormente, demonstrada através da sua aplicação a um conjunto de dados reais.

Abstract
Maturity models define a series of levels, each representing an increased complexity in information systems. Data Science appears in the Business Intelligence (BI) and Business Analytics (BA) literature. This work applies the _IABE maturity model, which includes two additional levels: Data Engineering (DE) at the bottom and Business Experimentation (BE) at the top. This study uses the _IABE model for students' modeling in the ModEst project. For this purpose, the Public Administration organism is the Directorate-General for Statistics of Education and Science (DGEEC) of the Portuguese Education Ministry. DGEEC provided vast data on two million students per year in the Portuguese school system, from pre-scholar to doctoral programs. This work presents the comprehensible _IABE maturity model to extract new knowledge from the DGEEC dataset. The method applied is _IABE, where after the DE level, wh-questions are formulated and answered with the most appropriate techniques at each maturity level. This work's novelty is applying the maturity model _IABE to a unique dataset for the first time. Wh-questions are stated at the BI level using data summarization; at the BA level, predictive models are performed, and counterfactual approaches are presented at the BE level. © 2023 by the authors. Licensee ESJ, Italy.

Abstract
Social impact assessment is a fundamental process to verify the achievement of the objectives of interventions and, consequently, to validate investments in the social area. Generally, this process is based on the analysis of the average effects of the intervention, which does not allow a detailed understanding of the individualization of these effects. Causal machine learning methods mark an evolution in causal inference, as they allow for a more heterogeneous assessment of the effects of interventions. Applying these methods to evaluate the impact of social projects and programs offers the advantage of improving the selection of target audiences and optimizing and personalizing future interventions. In this chapter, in a non-technical way, the authors explore classical causal inference methods to estimate average effects and new causal machine learning methods to evaluate heterogeneous effects. They address adapting the Uplift Modeling method to assess social interventions. They also address the advantages, limitations, and research needs for using these new techniques in social intervention. © 2023, IGI Global. All rights reserved.