Coromoto León

Computational thinking (CT) encompassing abstraction, problem decomposition, and algorithmic reasoning—has been linked to executive functions such as cognitive control and working memory, which rely heavily on the dorsolateral prefrontal cortex (DLPFC), suggesting its key role as a neurofunctional substrate for CT in adults. However, direct evidence linking DLPFC activity to CT remains limited. The present study examined the combined effects of cognitive instruction and anodal transcranial direct current stimulation (a-tDCS) over the left DLPFC on CT. A total of 146 university students viewed either a short instructional video on algorithmic problem-solving strategies or a structurally matched control video but without CT content. Prior to the instructional manipulation, participants received either a-tDCS or sham. Crucially, the study examined whether CT performance was moderated by motivational traits, including approach-avoidance motivation and regulatory focus.Active tDCS improved CT performance, but its effects depended on both motivational profile and instructional context. Participants with stronger approach motivation trait benefited from active stimulation only when CT instruction was not provided, whereas those with lower approach motivation showed the greatest improvement when active stimulation was combined with CT instruction. These findings indicate that the cognitive effects of DLPFC stimulation are shaped by the interaction between individual motivational characteristics and instructional context, highlighting the importance of tailoring educational interventions to learners' motivational profiles.

Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an approach to generating synthetic instances that are tailored to perform well with respect to a target algorithm belonging to a predefined portfolio but are also diverse with respect to their features. Our approach uses a novelty search algorithm with a linearly weighted fitness function that balances novelty and performance to generate a large set of diverse and discriminatory instances in a single run of the algorithm. We consider two definitions of novelty: (1) with respect to discriminatory performance within a portfolio of solvers; (2) with respect to the features of the evolved instances. We evaluate the proposed method with respect to its ability to generate diverse and discriminatory instances in two domains (knapsack and bin-packing), comparing to another well-known quality diversity method, Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and an evolutionary algorithm that only evolves for discriminatory behaviour. The results demonstrate that the novelty search method outperforms its competitors in terms of coverage of the space and its ability to generate instances that are diverse regarding the relative size of the “performance gap” between the target solver and the remaining solvers in the portfolio. Moreover, for the Knapsack domain, we also show that we are able to generate novel instances in regions of an instance space not covered by existing benchmarks using a portfolio of state-of-the-art solvers. Finally, we demonstrate that the method is robust to different portfolios of solvers (stochastic approaches, deterministic heuristics, and state-of-the-art methods), thereby providing further evidence of its generality.

RoblockLLy is an Educational Robotics simulator designed for primary and secondary school students whose goal is to increase their interest in STEM. In the particular case of Computer Science, it allows developing Computational Thinking skills. It has been designed with ease of use in mind. This free tool is available through a web browser and does not need a complex installation or specific hardware requirements, allowing Educational Robotics to be introduced to a wide range of users by working on practical projects that will help them understand key concepts of robotics and programming. The effectiveness of RoblockLLy has been validated based on motivation, usability and user experience criteria. The tool was validated with 212 secondary school students (12-16 years old). Specifically, motivation was measured with the Intrinsic Motivation Inventory (IMI), usability with the System Usability Scale (SUS) and user experience with the User Experience Questionnaire (UEQ). Generally speaking the results demonstrate that students perceived RoblockLLy as a novel and interesting tool. The ratings for usability were predominantly positive, although a few students indicated a preference for expert assistance. The overall rating of the user experience was positive as well, yet notable differences in attitudes toward motivation and usability were observed between genders.

Bone age assessment is essential for evaluating skeletal maturity, especially in cases where chronological age is uncertain, such as in unaccompanied foreign minors. Traditional methods, such as the Greulich-Pyle atlas and the Tanner-Whitehouse system, while effective, often result in variability due to manual interpretation and are not fully adapted to ethnically diverse populations. Although automated systems are available, they are typically proprietary, expensive, and require programming expertise, which limits their accessibility. This paper introduces an open-source domain-specific language specifically designed for bone age estimation, offering a balance between flexibility and ease of use. The system provides a straightforward, user-friendly syntax, allowing healthcare professionals without programming knowledge to perform accurate bone age assessments. It integrates well-established bone age methodologies, such as the Greulich-Pyle and Tanner-Whitehouse systems, ensuring flexibility in its application. The system is developed using a test-driven development approach, which guarantees both accuracy and reproducibility in bone age assessments. By offering a customizable, open-source solution, this system reduces costs and eliminates the reliance on proprietary software. Its ability to simplify the bone age estimation process makes it a valuable tool in clinical and forensic settings, particularly for professionals involved in the assessment of unaccompanied foreign minors and other populations where accurate bone age determination is crucial.

Almost all programming languages allow array and object properties to appear on the left side of an assignment, as in a[2] = 4 and a.x = 2. Curiously, none of the conventional programming languages permit modifying a function’s behavior through assignments, such as in f(2) = 4. It seems the elements of a function’s domain have traditionally been considered read-only, rather than true properties of the function. This paper presents an exploratory experience in a compiler course, where students, under the guidance of the instructor, were tasked with implementing a programming language that extends assignment expressions to allow the modification of a function on any element of its domain. This proposal raises the level of abstraction for function modification, compared to what is currently available in many modern languages. One notable consequence of this extension is that by incorporating left-side function assignment and realizing that all data structures can be represented by function subclasses, we immediately get objects, arrays, maps, classes, class inheritance, polymorphism, and access symmetry. into a minimal language. Since all data structures are treated as functions, it not only becomes natural to extend them with functional default behaviors, but also to manipulate them using the algebra of functions. At the same time, the language maintains access symmetry among all data structures including functions, objects, maps and arrays, removing the need for special access syntaxis and using everywhere a functional notation.

The ability to generate example instances from a domain is important in order to benchmark algorithms and to generate data that covers an instance-space in order to train machine-learning models for algorithm selection. Quality-Diversity (QD) algorithms have recently been shown to be effective in generating diverse and discriminatory instances with respect to a portfolio of solvers in various combinatorial optimisation domains. However these methods all rely on defining a descriptor which defines the space in which the algorithm searches for diversity: this is usually done manually defining a vector of features relevant to the domain. As this is a limiting factor in the use of QD methods, we propose a meta-QD algorithm which uses an evolutionary algorithm to search for a nonlinear 2D projection of an original feature-space such that applying novelty-search method in this space to generate instances improves the coverage of the instance-space. We demonstrate the effectiveness of the approach by generating instances from the Knapsack domain, showing the meta-QD approach both generates instances in regions of an instance-space not covered by other methods, and also produces significantly more instances.

Emotions are essential drivers of the learning process, influencing motivation, performance, and problem-solving abilities. In the field of Computer Science, students often struggle with negative emotions during programming activities, impacting their performance and project quality. To address this challenge, there is a growing need to introduce Computational Thinking skills at the pre-university level. This study focuses on understanding the emotions experienced by primary and secondary school students during Computational Thinking activities, involving both plugged and unplugged tools. A significant performance difference was observed between primary and secondary education levels, with the latter outperforming the former. The research identifies specific associations between concepts and emotions, highlighting age-related differences with younger students exhibiting more positive emotions. While gender-based disparities in Computer Science perception exist in secondary education, there are no corresponding distinctions in emotional responses. The study reveals a gender-based effect, with girls showing reduced emotional responses and lower Computational Thinking performance than boys. In summary, this research underscores the profound role of emotions in learning, providing essential insights for tailoring educational strategies considering gender-specific and programming concept-related factors. It also connects lower emotional reactions to inferior results, emphasizing the importance of heightened emotional engagement.

This article examines the effectiveness and interest generated among primary and secondary education students through activities aimed at developing Computational Thinking skills, in the context of the coronavirus disease 2019 pandemic. The shift to online or hybrid learning models posed a significant challenge for educators, particularly those lacking digital skills. The study sought to answer several research questions, including the impact of online versus in‐person teaching on preuniversity students and gender differences in Computer Science perception, and Computational Thinking skills performance. The study employed a four‐phase methodology, consisting of pre‐ and posttraining measurements of Computer Science perception and Computational Thinking skills development through specific activities delivered in‐person or online. The results indicate that in‐person training is more effective for developing Computational Thinking skills, particularly at the secondary education level. Furthermore, there is a need to focus on maintaining girls' interest in Computer Science during primary school, as interest levels tend to decline significantly in secondary school. These findings have significant implications for Engineering Education in the context of digital transformation and the increasing importance of Computational Thinking skills in various fields of engineering. This study highlights the importance of developing Computational Thinking skills among preuniversity students and the need for effective training methods to achieve this goal and underscore the significance of investing in Engineering Education to prepare the next generation of engineers for the rapidly changing digital landscape.

Emotions affect how we acquire knowledge, being one of the causes of the demotivation generated at the time of studying a new field. Computer Science does not always pique the interest of young people, so we carry out an analysis of emotions that are present in primary and secondary school students, around 8-9 years old and 12-13 years old, who engage in Computational Thinking activities, considering the educational level, gender, and type of intervention, to understand why this lack of interest. The sessions were based on 1 hour of face-to-face class in which activities related to Computational Thinking were carried out. The instrument used to measure emotions was the Developmental Channels Questionnaire which includes 13 different emotions that students must answer using the Likert scale from 0 to 10. The emotions felt have been mostly positive and ambiguous, while negative emotions have a low intensity, particularly in primary education. Regarding the educational level, there are differences between girls and boys only in the secondary education, while in the primary they are no significant. Also, girls show an evolution when carrying out this type of activity, while boys do not change.