Students often exhibit deficits in essential problem-solving skills, such as abstraction, analysis, pattern recognition, and decomposition. These skills are particularly crucial for those pursuing Computer Programming (CP) courses. Computational Thinking (CT) can play a key role in mitigating these deficits when introduced early in the curriculum. To systematically define how CT should be developed across different school years, we propose an ontology-based framework for CT curriculum design. Our ontology consists of four interconnected layers: (1) a structured description of the CT and CP domains; (2) a mapping of CT/CP concepts to specific school years; (3) identification of appropriate Learning Resources (LR) for each stage; and (4) integration of Neuroeducation Guidelines, linking them to the selected LR. To illustrate the applicability of this approach, we present a case study demonstrating its practical implementation, with a detailed curriculum plan for years 1 and 2. This structured methodology aims to provide educators with a clear, research-backed framework to enhance CT education from an early age.

Researchers (in the areas of Linguistics, History, Humanities, ...) often find their work difficult when they want to analyze and investigate a topic or an event, as the information is spread across various platforms and in different formats. These difficulties are also felt when it is necessary to gather different documents to create a corpus. This problem moved us to create a systematic approach to building an online corpus. Our approach aims to: extract information from news and comments available in online newspapers or social networks using web scraping techniques; process it using Natural Language Processing techniques; store it in a repository; and finally present it on an online platform. To validate our approach we collect news about a very current topic, the War in Ukraine. In the case study that is described to illustrate the proposal the news were extracted from two Portuguese newspapers and the social network Reddit. The corpus created can be consulted and explored in a web platform called GUCO, that is also presented. With this project we intend to overcome the problems mentioned and make a contribution to the area of Digital Humanities.

The use of games to promote Computational Thinking (CT) is a popular approach, acknowledged for its well-established and significant benefits. On the other hand, the impact of Virtual Reality (VR) on motivation is fully documented in the educational literature. The aim of the project here reported is to combine VR with gaming to build an effective Learning Resource (LR) to develop CT skills in young people. Our objective is to create an engaging VR game, called Cosmic Observatory, that can be played without special glasses - accessible on desktop or non-immersive Virtual Reality devices. The effects concerning the reality virtualization will be created using the web interface. Cosmic Observatory challenges to develop CT skills, namely pattern recognition abilities, are described. The game architecture is also discussed.

Computational Thinking (CT) is increasingly recognized as a fundamental skill for all students. However, its integration into educational systems remains a challenge. In Portugal, CT is embedded within Mathematics and Information and Communication Technologies (ICT) curricula. However, many Mathematics teachers struggle to effectively teach CT concepts due to a lack of formal training. In ICT, limited instructional time—50 min classes every two weeks—hinders the development of CT skills. To address these limitations, a School Cluster implemented a pilot CT subject as a complementary component of the curriculum for students from the 3 ^rd to the 6 ^th grade. This paper presents the design, methodology, and implementation of this initiative, along with preliminary reflections on its effectiveness. The study discusses the challenges encountered, the viability of curricular integration, and the potential for expanding this initiative into a mandatory subject within the national curriculum.

In light of specific development needs, it is common to concurrently apply different technologies to build complex applications. Given that lowering risks, costs, and other negative factors, while improving their positive counterparts is paramount to a better development environment, it becomes relevant to find out what technologies work best for each intended purpose in a project. In order to reach these findings, it is necessary to analyse and study the technologies applied in these projects and how they interconnect and relate to each other. The theory behind Programming Cocktails (meaning the set of programming technologies - Ingredients - that are used to develop complex systems) can support these analysis. However, due to the sheer amount of data that is required to construct and analyse these Cocktails, it becomes unsustainable to manually obtain them. From the desire to accelerate this process comes the need for a tool that automates the data collection and its conversion into an appropriate format for analysis. As such, the project proposed in this paper revolves around the development of a web-scraping application that can generate Cocktail Identity Cards (CIC) from source code repositories hosted on GitHub. Said CICs contain the Ingredients (programming languages, libraries and frameworks) used in the corresponding GitHub repository and follow the ontology previously established in a larger research project to model each Programming Cocktail. This paper presents a survey of current Source Version Control Systems (SVCSs) and web-scrapping technologies, an overview of Programming Cocktails and its current foundations, and the design of a tool that can automate the gathering of CICs from GitHub repositories.

Source-to-source transpilation plays a pivotal role in modern software engineering by enabling code migration, feature adoption, and cross-language interoperability without sacrificing semantic integrity. The contributions discussed in this paper can be split into two. The first is a comprehensive literature review that aims at defining what transpilers are, traces their historical evolution from early Fortran/COBOL preprocessors to more recent tools like Babel and TypeScript, and examines key parsing methodologies, AST representations, and transformation strategies. The second is an experimental investigation which assesses several popular transpilers - selected by GitHub popularity and unique language-pair capabilities, when applied to an equivalent code snippet designed to sum even numbers and identify the maximum element. The metrics evaluated were the execution time, CPU, memory consumption, output accuracy and usability.

This paper presents a comprehensive review of Decision Support Systems (DSS) for lower limb rehabilitation using Electrical Stimulation (ES), employing a rigorous two-part methodology. The first part involves a bibliometric analysis of articles from 1980 to 2023, while the second part is a systematic review of studies from 2019 to 2023, addressing six key research questions. The review identifies the main characteristics of DSS, such as data usage, sensitive data protection, reasoning techniques, and validation processes. It highlights the development focus on joint control systems, increasing interest in biofeedback and AI applications, and significant interest in FES-Cycling. Despite advancements, “decision support” remains in the early stages with simple architectures and limited data handling. Conversely, studies show advanced ES control models validated with neurological patients. This article emphasizes the need for sophisticated DSS that integrate data protection, reasoning methods, and patient monitoring to enhance rehabilitation outcomes and identifies significant gaps for future research.

As academic professionals, we frequently need to create different versions of our CVs for project applications, career evaluations, or competitions. These versions may be chronologically structured or skill-oriented, covering specific periods and written in various languages. Even when using a LaTeX document as a base, numerous modifications are required each time an updated CV version is requested for a specific purpose. The primary objective of the project reported in this paper is to design and implement a web-based system (CVTool) that simplifies the management of LaTeX CV content while ensuring flexibility. The CVTool is built on a domain-specific language that enables the creation of various filters, allowing for the automatic content adjustment while preserving the original format. Information is extracted from the LaTeX document, and users can specify the sections, dates, skills they want to highlight, and the language in which the CV should be generated. Since the approach relies on an internal data representation derived from the original LaTeX document, it offers users the flexibility to manage content efficiently and extract the necessary information with ease.

We developed a task-oriented dialogue framework structured as a Directed Acyclic Graph (DAG) of medical questions. The system integrates: (1) a systematic pipeline for transforming medical algorithms and guidelines into a clinical question corpus; (2) a cold-start mechanism based on hierarchical clustering to generate efficient initial questioning without prior patient information; (3) an expand-and-prune mechanism enabling adaptive branching and backtracking based on patient responses; (4) a termination logic to ensure interviews end once sufficient information is gathered; and (5) automated synthesis of doctor-friendly structured reports aligned with clinical workflows. Human-computer interaction principles guided the design of both the patient and physician applications. Preliminary evaluation involved five physicians using standardized instruments: NASA-TLX (cognitive workload), the System Usability Scale (SUS), and the Questionnaire for User Interface Satisfaction (QUIS). The patient application achieved low workload scores (NASA-TLX = 15.6), high usability (SUS = 86), and strong satisfaction (QUIS = 8.1/9), with particularly high ratings for ease of learning and interface design. The physician application yielded moderate workload (NASA-TLX = 26) and excellent usability (SUS = 88.5), with satisfaction scores of 8.3/9. Both applications demonstrated effective integration into clinical workflows, reducing cognitive demand and supporting efficient report generation. Limitations included occasional system latency and a small, non-diverse evaluation sample.

The daily activities of those involved in software development are inherently related to the technologies they use. Languages, libraries, frameworks, and tools tend to accumulate as projects evolve and change, effectively forming Programming Cocktails. Unfortunately, the burden of learning, using, and managing these technologies also tends to closely follow this growth, spawning a myriad of concepts that need to be handled concurrently. This complexity usually requires several factors to be analysed, in order to limit its negative effects. These factors range from security risks to costs and cognitive load, just to mention a few. This paper presents an ontology-based modelling framework that can be used to create an overview of Programming Cocktails. The instantiation of this ontology results in Cocktail Identity Cards, which can then be augmented with one or more of the previously mentioned factors. Finally, the paper also presents a first approach to the cognitive load analysis of Programming Cocktails, based on John Sweller’s Cognitive Load Theory.

Urban monitoring of public infrastructure (such as waste bins, road signs, vegetation, sidewalks, and construction sites) poses significant challenges due to the diversity of objects, environments, and contextual conditions involved. Current state-of-the-art approaches typically rely on a combination of IoT sensors and manual inspections, which are costly, difficult to scale, and often misaligned with citizens' perception formed through direct visual observation. This raises a critical question: Can machines now "see" like citizens and infer informed opinions about the condition of urban infrastructure? Vision-Language Models (VLMs), which integrate visual understanding with natural language reasoning, have recently demonstrated impressive capabilities in processing complex visual information, turning them into a promising technology to address this challenge. This systematic review investigates the role of VLMs in urban monitoring, with particular emphasis on zero-shot applications. Following the PRISMA methodology, we analyzed 32 peer-reviewed studies published between 2021 and 2025 to address four core research questions: (1) What urban monitoring tasks have been effectively addressed using VLMs? (2) Which VLM architectures and frameworks are most commonly used and demonstrate superior performance? (3) What datasets and resources support this emerging field? (4) How are VLM-based applications evaluated, and what performance levels have been reported?

In recent years, more attention has been paid to the security of the software supply chain (SSC). While at first SSC was just seen as the dependency of other libraries, today SSC is broader, considering all the environment where a software application is developed, from the actors, hardware and auxiliary tools. This work focuses on a specific part of software supply chain security: the tools used for continuous integration and continuous deployment (CI/CD) and their vulnerabilities and risks. These tools are widely used by organizations to accelerate their software development, testing, and delivery, making any security issue present in these tools problematic for the organization. This is especially true given that most tools are open-source, making these tools the primary targets for exploits. We will present a quick introduction to SSCS and CI/CD and provide a practical solution to detect risks and vulnerabilities in CI/CD tools, emphasizing the modular approach, allowing the system to easily scale to detect new risks and vulnerabilities, as well as to support new CI/CD tools.

The difficulties in teaching and learning computer programming remain a pressing issue to this day. Several studies and tools have been developed over the years to tackle this challenge from many different points-of-view. One of the biggest tools an educator has to support him in a classroom is the progressive explanation of how a source code is constructed and what effects each of its parts has on the overall result. Attempts to translate this live-directed tool to an on-line experience is usually time-consuming and lacking in features. In order to tackle this concern, a tool to create piecewise source code writing demonstrations was developed - Stepwise Source. The main idea behind this application is to allow step-by-step explanation of a source code construction, along with any relevant annotations and automatically assessed challenges that an educator may add. By providing a dynamic platform for both students and lecturers, this software aims to improve the teaching and learning of computer programming, while trying to imitate the information flow of a live lecture, with the added benefit of student-directed pace of explanation. Through interactive guidance and automated assessment, this tool has the potential to foster a deeper understanding of computational principles and promote proficiency in programming skills.

Despite the growing emphasis on Computational Thinking (CT) as a foundational skill for all students, there is a notable lack of inclusive digital resources tailored to the needs of learners with Autism Spectrum Disorder (ASD). This study addresses that gap by introducing Authink, a web-based, game-like application that provides challenges inspired by daily routines, and features sensory customization, multimodal feedback, designed to support CT development in students with ASD. The goal of this study was to assess the accessibility, usability, and pedagogical effectiveness of Authink in inclusive school contexts. A pilot study was conducted with three students with ASD (ages 10-14), involving a pre-test and post-test based on an adapted version of the BCTt (Computational Thinking Test), observational data during gameplay, and a usability questionnaire. Results showed gains in CT performance in two of the three participants, along with a high level of engagement and acceptance across the group. Students particularly valued the customizable interface and the real-time visual guidance feature, which helped them plan and debug their solutions more effectively. We conclude that Authink is a promising, scalable contribution to technology-mediated inclusive education, and future work will aim to validate these findings with larger and more diverse populations.

Inclusive education requires pedagogical strategies and technological resources capable of addressing student diversity, particularly among those with neurodevelopmental conditions such as Autism Spectrum Disorder (ASD) and AttentionDeficit/Hyperactivity Disorder (ADHD). This paper analyzes the potential of technology—with a special focus on tools that foster Computational Thinking (CT)—as support for inclusive practices in school settings. Based on recent literature and the Portuguese educational framework, we explore the role of games, mobile applications, and immersive environments in supporting the cognitive, behavioral, and socio-emotional development of students with ASD and ADHD. We also discuss key challenges faced by teachers and schools, including limited continuous training and a lack of adapted digital resources. Findings suggest that CT, when mediated by accessible and engaging technologies, offers a promising path to promote autonomy, problem-solving, and active participation for students with special educational needs. Finally, we present two tools developed by our team—Authink and BEAM—designed to support inclusive education through technology.

A successful teaching effort is usually dependant on several factors. From the right environment, to a precisely worded exercise statement, it rests on the teacher’s shoulders the concoction of the most effective learning assets to their students. A significant part of this process lies on practise: students commonly solidify their knowledge by solving exercises. Creating new programming exercises, specially in high-demand environments such as large classrooms, is a repetitive and error-prone process, specially when stacked with other typical affairs that educators are required to attend to. Goliath, one of the two main contributions of this article, is a template-based, Artificial Intelligence (AI) supported exercise generator, that aims to facilitate the creation of exercise repositories. By using a Domain-Specific Language (DSL) to define exercise templates, combined with the automatic generation of different exercise types, educators can use Goliath’s features to improve their exercise repositories, both in size and variety. This systematic approach allows for greater control and automatisation than using a Large Language Model (LLM) directly, as the exercises’ main components can be pre-defined and pre-configured via their templates. Goliath, which is available online for free access, has been tested and its usability assessed. Combined with these functionalities, the content of the exercises themselves, the manner in which they are presented, and how they are rated for difficulty should also be considered in high regard when designing programming exercises. The Cognitive Load Theory (CLT) provides a conceptual foundation to understand problem-solving mechanisms that are commonly found in several aspects and situations of daily life, such as solving programming exercises. This foundation has been explored and systematically structured to construct the second main contribution of this article: guides to create exercise templates in Goliath founded on the Cognitive Load Theory, aiming to improve both teaching and learning computer programming.

The teaching-learning process is complex in nature, requiring many tasks and skills to achieve success in the construction of knowledge. As per any particular kind of cognitive development, teaching and learning Computer Programming is no different in this regard: tasks must be executed, sometimes repeatedly, and skills must be developed. Despite different approaches and methodologies, exercising what has been studied is proven to be effective in pretty much any teaching-learning process. Many tools have been developed throughout time to aid in the execution of this important task, sometimes approaching the problem from the students’ perspective, sometimes from the teachers’. This paper presents Goliath, a semi-automatic generator of Computer Programming exercises, whose functionality is based on Artificial Intelligence (AI) models, a Domain-Specific Language (DSL), and an online application that binds them together. Goliath’s goals are directed towards teachers (and indirectly, students) by aiming to lower the burden of repeatedly constructing exercises. This is achieved through the use of templates that allow for automatic variations of an exercise to be created instantly, while relying on a common foundation. Goliath is meant to be a facilitator, raising availability of exercise lists, while avoiding repetition and the common mistakes that accompany their construction.

This article introduces an immersive Virtual Reality (VR) application designed to assess the interaction capabilities of users with physical and cognitive limitations, including older adults and individuals with disabilities, as well as ICU patients. The VR application encompasses six tasks varying in complexity, each designed to evaluate different aspects of VR interaction skills, such as movements of the head, arms, and fingers, alongside more intricate activities like pick-and-place, pointing, and painting. The paper details the VR application’s specifications, including its system architecture, deployment framework, and data structure. The application’s efficacy was tested through three pilot studies in a retirement home setting. The analysis focused on examining correlations among various factors, including age, cognitive abilities (evaluated using the Mini-Mental Status Examination), and previous VR experience. The findings reveal significant correlations, illuminating the effects of age, cognitive capacity, and past VR interactions on task performance. The results emphasize the importance of accounting for user-specific attributes, prior experiences, and cognitive abilities in the design of VR-based therapeutic interventions.