Invirtiendo las clases de laboratorio en Ingeniería Informática: Un enfoque ágil

En este artículo describimos nuestra experiencia al aplicar la metodología de clase invertida en la asignatura Arquitectura e Integración de Sistemas Software, de segundo curso del grado de Ingeniería del Software. Varios aspectos caracterizan este estudio frente a los trabajos relacionados. En primer lugar, la metodología fue aplicada en las clases prácticas de la asignatura, donde conseguimos aumentar el tiempo dedicado a la resolución de ejercicios en 24 minutos de media. En segundo lugar, la volatilidad del temario hizo necesario desarrollar una aproximación ágil a la metodología, en la que los profesores debían ser capaces de elaborar vídeos docentes de calidad en sus propios despachos y en unos pocos minutos. Este artículo resume algunas de las muchas lecciones aprendidas en relación a la elaboración del material. En tercer lugar, el estudio destaca también por el tamaño, habiéndose realizado a lo largo de dos cursos académicos, 2017 y 2018, involucrando a un total de 434 alumnos y 6 profesores. Los resultados del estudio, respaldados por un sólido análisis estadístico de los datos, demuestran la idoneidad de esta metodología para ser aplicada en las clases de laboratorio del área de Ingeniería del software.In this paper, we report our experience on flipping a second-year undergraduate course on software architecture and integration, taught in a Software Engineering degree. Several aspects characterize this study with respect to related works. In the first place, the methodology was applied in the practical classes of the course, where we managed to increase the time dedicated to exercises solving in 24 minutes on average. Second, due to the constant updates in tools and software used in the course, we applied an agile approach to the methodology, in which lecturers had to produce high-quality teaching videos in their own offices just in minutes. This paper summarizes some of the many lessons learned in this regard. Third, the study also stands out for its size, having been conducted over two academic courses, 2017 and 2018, involving a total amount of 434 students and 6 lecturers. The results of the study, backed by a solid statistical analysis of the data, demonstrate the suitability of this methodology to be applied in the laboratory classes in the Software Engineering area

Aprendiendo arquitectura software a partir de proyectos de código abierto en GitHub

La enseñanza de arquitectura del software supone todo un reto. Los conceptos teóricos son a menudo muy abstractos y los problemas arquitectónicos sólo son claramente visibles en aplicaciones de cierta envergadura. El reto es aún mayor cuando estos conceptos se enseñan en las primeras etapas del grado cuando los conocimientos de diseño y programación del alumnado aún son limitados. Para abordar este reto, inspirados por una propuesta llevada a cabo en la Delft University, decidimos adoptar un enfoque novedoso: enseñar arquitectura del software a través del análisis, evaluación y documentación de la arquitectura de proyectos existentes alojados en la plataforma GitHub. Para ello, fue necesario adaptar el método original, empleado a nivel de máster, a la asignatura objeto del estudio impartida durante el segundo curso de grado. Para evaluar este enfoque realizamos un total de 258 encuestas a estudiantes de dos cursos consecutivos. Los resultados del estudio, respaldados por un sólido análisis estadístico de los datos, demuestran la idoneidad de este método para la enseñanza de arquitectura del software en los primeros cursos de grado.Teaching software architecture is a challenge. Theoretical concepts are often very abstract and architectural problems are only clearly visible in applications of a certain magnitude. The challenge is even greater when these concepts are taught in the early stages of the bachelor’s degree when the students’ knowledge of design and programming is still limited. To address this challenge, inspired by a proposal carried out at Delft University, we decided to adopt a novel approach: teaching software architecture by analysing, evaluating and documenting the architecture of existing projects hosted on the GitHub platform. To do so, it was necessary to adapt the original method, used in a master course, to the course under study, taught during the second year of the bachelor’s degree. To evaluate this approach we conducted a total of 258 student surveys in two consecutive years. The results of the study, supported by a robust statistical analysis of the data, demonstrate the suitability of this method for teaching software architecture in the first years of the bachelor’s degree.Trabajo parcialmente financiado por el Dpto de Lenguajes y Sistemas de la Universidad de Sevilla y los proyectos TED2021-131023B-C21, TED2021-131023B-C22, PID2021-126227NB-C21, PID2021-126227NB-C22 financiados por MCIN/AEI/10.13039/501100011033/FEDER y por la Unión Europea NextGenerationEU/PRTR

XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP

Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques` limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques` predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs

Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques

EDUCORE project: a clinical trial, randomised by clusters, to assess the effect of a visual learning method on blood pressure control in the primary healthcare setting

<p>Abstract</p> <p>Background</p> <p>High blood pressure (HBP) is a major risk factor for cardiovascular disease (CVD). European hypertension and cardiology societies as well as expert committees on CVD prevention recommend stratifying cardiovascular risk using the SCORE method, the modification of lifestyles to prevent CVD, and achieving good control over risk factors. The EDUCORE (Education and Coronary Risk Evaluation) project aims to determine whether the use of a cardiovascular risk visual learning method - the EDUCORE method - is more effective than normal clinical practice in improving the control of blood pressure within one year in patients with poorly controlled hypertension but no background of CVD;</p> <p>Methods/Design</p> <p>This work describes a protocol for a clinical trial, randomised by clusters and involving 22 primary healthcare clinics, to test the effectiveness of the EDUCORE method. The number of patients required was 736, all between 40 and 65 years of age (n = 368 in the EDUCORE and control groups), all of whom had been diagnosed with HBP at least one year ago, and all of whom had poorly controlled hypertension (systolic blood pressure ≥ 140 mmHg and/or diastolic ≥ 90 mmHg). All personnel taking part were explained the trial and trained in its methodology. The EDUCORE method contemplates the visualisation of low risk SCORE scores using images embodying different stages of a high risk action, plus the receipt of a pamphlet explaining how to better maintain cardiac health. The main outcome variable was the control of blood pressure; secondary outcome variables included the SCORE score, therapeutic compliance, quality of life, and total cholesterol level. All outcome variables were measured at the beginning of the experimental period and again at 6 and 12 months. Information on sex, age, educational level, physical activity, body mass index, consumption of medications, change of treatment and blood analysis results was also recorded;</p> <p>Discussion</p> <p>The EDUCORE method could provide a simple, inexpensive means of improving blood pressure control, and perhaps other health problems, in the primary healthcare setting;</p> <p>Trial registration</p> <p>The trial was registered with ClinicalTrials.gov, number NCT01155973 [<url>http://ClinicalTrials.gov</url>].</p

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Experiencias de aprendizaje

Libro de experiencias de aprendizaje del grupo de investigación Giteca y de los semilleros de investigación en la que se visualizan las diferentes experiencias lideradas por instructores y aprendices en las diferentes áreas y líneas de formación.Book of learning experiences of the Giteca research group and the research hotbeds in which the different experiences led by instructors and apprentices in the different areas and lines of training are visualized.Propagación in vitro como un camino de aprendizaje para la formación profesional integral -- Experiencias significativas de aprendizaje, laboratorio de hematología y parasitología animal del Complejo Tecnológico para la Gestión Agroempresarial CTPGA-SENA -- Experiencias significativas adquiridas por aprendices en el área de SENNOVA, Complejo Tecnológico para la Gestión Agroempresarial. Regional – Antioquia -- El papel de la prensa escrita en el desarrollo de la competencia textual -- Aprendiendo a Emprender con un emprendedor -- Ven y te cuento sobre ADSI -- Observaciones fenológicas del cultivo de cacao (Theobroma cacao) en los municipios de Tarazá, El Bagre y Caucasia dentro de la formación del programa SENA emprende rural -- Tejiendo sueños desde la formación -- Forraje verde hidropónico como alternativa para disminuir la expansión de la frontera agrícola en el Putumayo -- La importancia del saber hacer para ser competente en el sector agrícola -- Experiencia significativa de aprendizaje semilleros de investigación -- La investigación como ente transformador de pensamientos -- Piscícola Paraguay; Mi Sueño, Mi Proyecto de Vida! -- Estrategia de aprendizaje a través de la investigación y la empresa aplicando un programa de Responsabilidad Social Empresarial –RSE -- Matemática aplicada para procesos agroindustriales de panificaciónna85 página

Does your accurate process predictive monitoring model give reliable predictions?

The evaluation of business process predictive monitoring models usually focuses on accuracy of predictions. While accuracy aggregates performance across a set of process cases, in many practical scenarios decision makers are interested in the reliability of an individual prediction, that is, an indication of how likely is a given prediction to be eventually correct. This paper proposes a first definition of business process prediction reliability and shows, through the experimental evaluation, that metrics that include features defining the variability of a process case often give a better prediction reliability indication than metrics that include the probability estimation computed by the machine learning model used to make predictions alone