UNIPS - ESCE – DSI - Artigos científicos
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- Moodle: um complemento ao ensino presencial : o caso de uma das licenciaturas da ESCE/IPSPublication . Reis, Leonilde; Mendes, Ana; Prata, Alcina
- Otimização de Práticas de Segurança da Informação: Utilização do Balanced Scorecard DesignerPublication . Ferreira, Durval; Reis, LeonildeA sociedade da informação e do conhecimento, suscita às organizações a realização de uma prática de gestão estratégica contínua como forma de criar valor para os stakeholders, que pode ser facilitada pela utilização das Tecnologias de Informação e Comunicação (TIC) e dos Sistemas de Informação (SI). As TIC não podem ser apontadas como a resolução para todos os problemas, mas podem contribuir claramente para potenciar a melhoria do desempenho organizacional. Na perspetiva interna da organização, a medição da performance consiste em criar processos que ajudem à tomada de decisão com base em factos. Com base na missão, e na descrição dos seus principais processos, na estratégia definida para a organização, e na norma ISO/IEC 27002:2005, foi iniciado um estudo para identificar problemas no domínio da segurança da informação na Mesio Serviços, Lda. Baseado na norma ISO/IEC 27004:2009 foi possível definir um conjunto de métricas que se consideraram relevantes para o estudo. Com recurso à metodologia de avaliação do desempenho Balanced Scorecard, foi criada a perspetiva de trabalho para a Segurança da Informação, e consequentemente os processos e os indicadores a aplicar e monitorizar, bem como as metas pretendidas com o trabalho de avaliação. A transposição do plano para a ferramenta de avaliação e medição da performance Balanced Scorecard Designer (BSC Designer), permitiu a avaliação em tempo real dos indicadores e das metas estipuladas para a otimização das políticas de segurança na organização, tornandose preponderante no acompanhamento e evolução do desempenho dos controlos pela monitorização, facto que possibilita a correção dos desvios em relação ao previsto de forma a atingir as metas e os objetivos definidos.
- The Design of Flexible Video-Based Crossmedia Informal Learning Contexts Beyond iTVPublication . Prata, Alcina; Chambel, Teresa
- Framework for classroom student grading with open-ended questions: a text-mining approachPublication . Vairinhos, Valter Martins; Pereira, Luís Agonia; Matos, Florinda; Nunes, HelenaThe purpose of this paper is to present a framework based on text-mining techniques to support teachers in their tasks of grading texts, compositions, or essays, which form the answers to open-ended questions (OEQ). The approach assumes that OEQ must be used as a learning and evaluation instrument with increasing frequency. Given the time-consuming grading process for those questions, their large-scale use is only possible when computational tools can help the teacher. This work assumes that the grading decision is entirely a teacher’s task responsibility, not the result of an automatic grading process. In this context, the teacher is the author of questions to be included in the tests, administration and results assessment, the entire cycle for this process being noticeably short: a few days at most. An attempt is made to address this problem. The method is entirely exploratory, descriptive and data-driven, the only data assumed as inputs being the texts of essays and compositions created by the students when answering OEQ for a single test on a specific occasion. Typically, the process involves exceedingly small data volumes measured by the power of current home computers, but big data when compared with human capabilities. The general idea is to use software to extract useful features from texts, perform lengthy and complex statistical analyses and present the results to the teacher, who, it is believed, will combine this information with his or her knowledge and experience to make decisions on mark allocation. A generic path model is formulated to represent that specific context and the kind of decisions and tasks a teacher should perform, the estimated results being synthesised using graphic displays. The method is illustrated by analysing three corpora of 126 texts originating in three different real learning contexts, time periods, educational levels and disciplines.
- Towards green IT: assessing the environmental impact of public administrationPublication . Landum, Manuel; Moura, M. M. M.; Reis, LeonildeThe Public Administration (PA) in Portugal is impacted by the constant acceleration and evolution of Information and Communication Technologies (ICT), where the Portuguese State, transposing European Union directives, has legislated and encouraged their use as instruments of added value for the PA. Establishing rigorous and sustainable governance is crucial, ensuring that the organization’s strategic objectives are aligned with ICT and, preferably, with Green IT practices. In this context, it is necessary to be able to estimate and subsequently evaluate the impact of strategic and operational choices on improving environmental quality. For such, this article presents a framework developed to optimize ICT management practices and indicators that, in this environmental area, support the forecasting and assessment of the impact of the choices made in the search for technologically advanced solutions in the Barreiro municipality. These choices enable, among other things, a reduction in energy consumption, implying a reduction in financial costs, resulting in environmental benefits, such as a reduction in CO2 emissions into the atmosphere or less deforestation. The results presented, obtained in a real-world context, make it possible to assess the progress of alignment with Green IT in this area, as well as the overall result.
- Exploring explainable AI techniques for plant disease classification in digital agriculturePublication . Porfírio, Rui Pedro; Madeira, Rui Neves; Santos, Pedro AlbuquerqueIntegrating smart farming technologies in agriculture is crucial to address the pressing challenges of food security, economic stability, and environmental sustainability. Solutions based on artificial intelligence (AI) for plant disease detection play a critical role in optimizing crop health and yield. However, the complexity and opacity of these AI models can hinder their acceptance and practical application by end-users. To address this issue, our ongoing research explores applying explainable AI (XAI) techniques to enhance the explainability of vision-based plant disease classification models. Our experiments assess the transparency of vision-based models by applying XAI techniques, such as LIME, Grad-CAM, and occlusion-based attribution, to visualize the reasoning behind model predictions. Additionally, we analyze inherently transparent machine learning models, such as k-Nearest Neighbors, through custom visualization graphics and examine how explainability varies with model accuracy. These findings highlight the role of XAI techniques in enhancing the transparency of predictions for crucial vision tasks in agriculture such as plant disease classification. Building on these results, we explore the potential integration of X AI techniques into third-party applications or prototypes, emphasizing how tailored visual and textual explanations can enhance the transparency and interpretability of plant disease classification models.
- Modeling absolute CO2–GDP decoupling in the context of the global energy transition: evidence from econometrics and explainable machine learningPublication . Teruel-Gutiérrez, Ricardo; Anunciação, Pedro Fernandes da; Teruel-Sánchez, RicardoThis study investigates the feasibility of absolute decoupling—where economies expand while CO2 (Carbon Dioxide) emissions decline in absolute terms—by identifying its key macro–energy drivers across 79 countries (2000–2025). We construct a comprehensive panel of energy-system indicators and estimate the probability of decoupling using two complementary classifiers: a penalized logistic regression and a gradient-boosted decision tree model (GBM). The non-parametric GBM significantly outperforms the linear baseline (ROC–AUC ~0.80 vs. 0.67), revealing complex non-linearities in the transition process. Explainable AI analysis (SHAP) demonstrates that decoupling is not driven by GDP growth rates alone, but primarily by sharp reductions in energy intensity and the active displacement of fossil fuels. Crucially, our results indicate that increasing renewable capacity is insufficient for absolute decoupling if the fossil fuel share does not simultaneously decline. These findings challenge passive “green growth” narratives, suggesting that current policies are inadequate; achieving climate targets requires targeted mechanisms for active fossil fuel phase-out rather than merely relying on renewable additions or economic modernization.