Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

User experience (UX) is a topic widely discussed in the software industry. The elaboration of UX requirements is an activity that practitioners face difficulties during the software specification. On the other hand, user stories (US) are a well-known artifacts to describe software requirements. Frequently, acceptance criteria (AC) complement the US specification by giving more details about the require-ment scope, functionality, and quality requirements of the software. Taking into account US/AC relevance, ACUX (Acceptance Criteria of User eXperience) guide was proposed to support software teams to the writing of UX requirements during the elaboration of AC. In this paper, a case study carried out with software teams from two startups investigated the use of ACUX. The study aims to ob-serve the changes that the guide adoption employed in the teams’ routine concer-ned on UX requirements elaboration. The findings revealed that ACUX im-proved team communication and played the role of a reminder for the developers memory about the relevant aspects of UX to be considered in the requirement specification.

Yogasana, often simply referred to as “asana”, is a Sanskrit term that encompasses the physical postures or poses practiced in yoga. Traditionally, there are almost 90 classical Yogasanas, each with its unique benefits and variations. It's very important to identify the postures and benefits of each asana. Classifications of Yogasana can have a significant impact on the practice and promotion of yoga, making it more accessible, educational, and safe for people of all skill levels. In this research work, we have automatically identified different Yogasanas from images using a deep neural network. For our work we have considered 10 very popular categories of Yogasana and built a dataset of volume 800 images. Furthermore, the data augmentation (DA) technique is utilized to increase the volume of data by including slightly modified replicas of existing data. Xception, combined with a self-attention network, has been employed to classify various Yogasanas based on diverse images. The model attains an impressive validation accuracy of 99%, surpassing the performance of all other contemporary state-of-the-art models.

In infants, teaching strategies focus on play and experiential activities; The development of scientific competencies based on STEAM involves a series of pedagogical approaches, so the purpose of this work was to implement a mini-ecosystem as a teaching strategy. 20 children between 4 and 5 years old from the educational institution “Dulces Brotecitos” participated. The study is developed within the qualitative paradigm, as a case study over a period of 2 months for the operation of the mini-ecosystem and 3 months for its implementation. Non- participant observation, recording of photos and videos were used; and field notebook to analyse the results. In the findings, four and five years old children spontaneously and flexibly use their senses to observe different phenomena and objects of everyday life, they also raise hypotheses and make inferences to solve problems. The various experiences presented to infants activate their interest in science, such as robotics. The STEAM approach, acronym for Science, Technology, Engineering, Art and Mathematics, aims to integrate these disciplines into the educational process and encourage the development of each of them (Pahnke, Janna ; O’Donnell, Carol; Bascopé). The approach of STEAM learning sessions at an early age favors the development of skills in these subjects, which will then facilitate subsequent learning (Zollman, 2012). The objective of this project was to analyse the development of scientific thinking skills in children aged 4 and 5 at the Initial level, under the STEAM approach, working in a Mini Ecosystem, in which the sectors are implemented: the pedagogical garbage dump, games to build and sensory tables. The work with parents, community entities and private companies contributed to the implementation of a mini-laboratory, as part of the mini-ecosystem.

This study focuses on evaluating the influence of mentoring in technological tools on women operating in the textile sector in the high Andean areas of Cusco, Puno, Tacna, Moquegua and Arequipa in southern Peru. To measure this impact, surveys were conducted before and after the virtual mentorships, focusing on the acquisition of basic level technology skills, such as Google search, use of Canva and social media management. The results indicate that the mentorships have influenced the development of technology skills among the participants. Pre-mentoring surveys revealed a generalized lack in most of these skills, with limited knowledge in the use of tools such as search engines and graphic design platforms. However, post- mentoring surveys showed improvements in these areas. This study highlights the importance of mentoring in the development of basic technological skills, empowering women in the textile sector in the high Andean regions. These results suggest that mentoring can play a key role in closing technology gaps and improving the active participation of this demographic group in the digital economy and society in general.

This research paper investigates the challenges faced by diploma-level IT students at Al-Baha University in understanding programming concepts, writing code, and detecting errors. It also examines the effectiveness of e-learning platform W3Schools in enhancing students' programming skills. Through a comparative analysis of students using e-learning resources versus those employing traditional learning methods, the study demonstrates the impact of the digital approach in addressing common programming challenges. Both pre- and post-experiment surveys showed positive gains in the Interactive Group, and the statistical data also confirmed a high level of significance for all the programming skills measured. The significance of this paper lies in how the research demonstrates that e-learning can reduce the barriers faced by programming students and contribute to the literature on learning techniques in the context of today's technological landscape. Consequently, the results provide a compelling argument for the necessity of incorporating interactive e-learning resources into the programming curricula to improve student performance and enhance confidence in their coding skills. The findings of this study offer valuable insights into the use of digital approaches in education and suggest a potential way to overcome common learning challenges on a large scale. This work aims to contribute to the ongoing discussion on the impact and effectiveness of digital practices in education. Future research recommendations focus on assessing the long-term value of e-learning in enhancing the learning experience for programming students and better preparing them for future technologies.

The terms Functional Requirement and Non-Functional Requirement (NFR) are intertwined concepts but aim at different purposes. In a nutshell, functional requirements state what an entity must do by referring to its features and capabilities (functions), while non-functional requirements state quality aspects that this entity must achieve. The present paper discusses the main terms and relationships for the Non-Functional Requirements (NFRs) domain ontology, which is located at the top-domain level in the context of a five-tier ontological architecture for specifying knowledge. In this ontological context, the term NFR does not represent a concrete entity (particular thing), but rather a quality-, constraint-related human assertion of a certain aspect of the entity to be measured and evaluated. Also, an NFR as an assertion can deal with an entity category (thing category) as a universal abstraction. As a consequence, the quality aspect attributed, for example, to a particular entity –both as a class and its individuals– can refer to its parts, such as the properties and/or capabilities (powers) of the entity, or the entity as a whole. To better understand the terms and relationships of the NFRs ontology, some grounding in the semantics of the foundational concepts such as Thing, Thing Category, and Assertion is needed. Ultimately, to validate the applicability of the ontological conceptualization of NFRs, three practical cases carried out in particular situations of measurement and evaluation projects are discussed, and the benefits of this ontological proposal are also highlighted.

Dengue is a vector-borne tropical disease that is widely distributed throughout the world. The clinical management of dengue consists of diagnosis and treatment of the disease. The development of approaches to aid decision-making in this disease is necessary to reduce morbidity and mortality rates. Despite the existence of guidelines for clinical management, the diagnosis and treatment of dengue remain a challenge. To address this problem, we aimed to develop methodologies, models, and approaches to support decision-making regarding the clinical management of this infection. We developed several works to meet the proposed objectives. First, we reviewed the latest trends in dengue modeling using machine learning (ML) techniques. Then, we proposed a decision support system for dengue diagnosis using fuzzy cognitive maps (FCM). Subsequently, we developed an autonomous cycle of data analysis tasks (ACODAT) to support both diagnosis and treatment of the disease. Also, we presented a methodology based on FCM and optimization algorithms to generate prescriptive models in various science domains. Finally, we proposed three federated learning approaches to ensure the security and privacy of data related to the clinical management of dengue. These strategies were evaluated using diverse datasets with signs, symptoms, laboratory tests, and information related to disease treatment. The results showed the ability of the developed methodologies and models to predict the disease, classify patients according to severity, evaluate the behavior of severity-related variables, and recommend treatments based on World Health Organization (WHO) guidelines.

Nowadays, the teaching of mathematics has been modified as electronic instruments advance. Throughout history, teaching has evolved in an impressive way. Today, education is not immune to all these changes, and therefore, it relies on current technology. One of the most important branches of this evolution in mathematics education is virtual reality. In Mexico, there are several lines of research to develop mathematical skills. This is why this work conducts an investigation into the implementation of virtual reality applied to mathematics education with a pedagogical focus and supported by a multidisciplinary group. This is achieved through an ecosystem model that helps create, implement, and provide feedback on current technology. This work proposes a case study, its implementation, and the acquisition of implementation results, which are subsequently analysed.

Virtual Educational Robotics (VER) consists of using virtual platforms where students can design, build, program, and operate robots, serving as a replacement for traditional educational robotics that utilizes physical kits. This paper proposes the design and implementation of an educational strategy that utilizes this type of technology to determine its impact on the teaching and learning processes in selected mathematical topics within secondary education in Mexico. A quasi-experimental research study was conducted to assess the impact of virtual educational robotics on specific topics in Analytical Geometry in secondary education in Mexico. A pretest and post-test were administered to two groups (experimental and control), and it was found that the experimental group achieved better results in the specific topics of Analytical Geometry. Although the overall mean of the control group was slightly higher, it can be concluded that the use of VEXcode VR improved learning compared to traditional didactic strategies.

In this research, we focus on addressing fattening management and animal health in rotational grazing within the framework of precision farming. Our approach leverages advanced technologies like Industry 4.0 and artificial intelligence to optimize agricultural and livestock processes. Our objective was to develop methodologies, models, and approaches supporting decision-making in productivity management and animal health. We pursued sub-objectives including developing a precision livestock farming architecture, creating knowledge models for animal health and herding management, and crafting meta-intelligent models for autonomous grazing and animal health management. Through a series of research articles, we achieved significant milestones. These include autonomous data analysis cycles for beef production, weight identification models using machine learning systems for monitoring cattle fattening using fuzzy classification, and multi-objective optimization models for maximizing weight gain in rotational grazing. We also introduced autonomous data analysis for self-supervision of animal fattening, management systems for cattle fattening, and the use of meta-learning in cattle weight identification for anomaly detection. Our methodologies and models demonstrated strong decision-making capabilities in managing livestock production processes, particularly in fattening and animal health management in rotational grazing, encompassing monitoring, diagnosis, and process optimization.