Examinando por Autor "Castellanos, María Eugenia"

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Evaluation of stress and anxiety levels on science and engineering undergraduate students in spain when facing written assessments guides for positive interventions
(Elsevier, 2024-11) Simón de Blas, Clara; Rojas, Karina; García Sipols, Ana E.; Hernández Alonso, Sonia; Castellanos, María Eugenia; Cano, Javier; Córdoba, Claudia
Anxiety and stress disorders are increasingly common, especially among undergraduate students, significantly affecting their family, social, and academic lives. The isolation from restrictive measures during the COVID-19 pandemic in Spain has further exacerbated mental health issues. The disruption of in-person teaching has also impacted traditional learning and evaluation processes, increasing stress and anxiety levels in students. Based on this background, this study aims to analyze the incidence of these disorders among undergraduates and their relationship with various academic, demographic, and family factors, considering the influence of COVID-19. Our results were obtained from a survey conducted among first- and second-year URJC students who are enrolled in an experimental degree program. The statistical analysis provides guidelines for positive interventions to increase student motivation, which further leads to academic success. Results show that women exhibit higher levels of stress and a greater prevalence of anxiety compared to men. The study highlights the influence of specific factors on anxiety levels among students, proposing direct lines of action that enhance positive feelings concerning academic tasks
MoCAS: A Mobile Collaborative Tool for Learning Scope of Identifiers in Programming Courses
(2016) Paredes-Velasco, Maximiliano; Serrano-Cámara, Luis Miguel; Velázquez-Iturbide, J. Ángel; Alcover, Carlos-María; Castellanos, María Eugenia
This article presents an instructional framework for collaborative learning, called CIF and aimed at the analysis level of Bloom’s taxonomy, as well as a mobile collaborative tool called MoCAS that supports CIF. MoCAS is aimed at the domain of scope of identifiers in programming learning, which is a topic present in programming courses in engineering studies. The specification and development of MoCAS were explicitly driven by pedagogical goals and by the atomic actions declared in CIF as simple items of collaborative activities. Furthermore, CIF and MoCAS were evaluated in an actual educational context with respect to students’ performance and motivation. Students using CIF and MoCAS obtained statistically significant higher grades than students studying in an individual or collaborative basis but not using MoCAS. In addition, we measured statistically significant measures indicating that students instructed with CIF and MoCAS were more motivated than students instructed collaboratively but not using CIF or MoCAS. In addition to CIF andMoCAS,and the evaluation results, the experiences here reported exemplify several software engineering practices: the design of an educational system based on knowledge of the target domain (namely, Bloom’s taxonomy) and the evaluation of users’ satisfaction (mainly, students’ motivation).
Model uncertainty quantification in Cox regression
(Wiley, 2023) García-Donato, Gonzalo; Cabras, Stefano; Castellanos, María Eugenia
WeconsidercovariateselectionandtheensuingmodeluncertaintyaspectsinthecontextofCoxregression.Theperspectivewetakeisprobabilistic,andwehandleit within a Bayesian framework. One of the critical elements in variable/modelselection is choosing a suitable prior for model parameters. Here, we derive theso-called conventional prior approach and propose a comprehensive implemen-tation that results in an automatic procedure. Our simulation studies and realapplications show improvements over existing literature. For the sake of repro-ducibility but also for its intrinsic interest for practitioners, a web applicationrequiring minimum statistical knowledge implements the proposed approach.