Learning Cultural Humility Through Stories and Global Service-Learning

Service-learning experiences are often utilized by nursing programs in efforts to increase the cultural competence of nursing students. Through the use of sharing story, the concepts of cultural competence and cultural humility can be explained for students preparing for upcoming intercultural experiences. This case study describes the experience of nursing students and university faculty on their first service-learning trip to rural Kenya and how the intercultural issues were navigated there as students developed characteristics of cultural humility. This story is now being shared in preparations for subsequent international trips with nursing students and can be a model for programs wanting to prepare for service-learning experiences

Global dimension in engineering education : promoting global learning in Spanish universities

La iniciativa ‘Dimensión Global en los Estudios Tecnológicos’ (GDEE) es una red que pretende mejorar el conocimiento, la comprensión crítica y los valores actitudinales de los estudiantes y de los postgraduados de las universidades científicotecnológicas en relación al Desarrollo Humano Sostenible (DHS). El objetivo es promover la integración del DHS como tema transversal en el currículo, mediante la mejora de las competencias de los profesores y a través de su participación y la de los estudiantes en iniciativas relacionadas con el DHS. La iniciativa empezó como un proyecto de colaboración entre un consorcio de universidades europeas y ONGs financiado por EuropeAid. Esta contribución presenta y discute la experiencia europea GDEE, profundizando las barreras y oportunidades encontradas, centrándose especialmente en la replicabilidad potencial de esta iniciativa. Estos resultados se complementan con la caracterización y el análisis comparativo del perfil académico de una comunidad de profesores implicados en actividades promovidas por GDEE.Peer ReviewedPostprint (author's final draft

Recursive Percentage based Hybrid Pattern Training for Supervised Learning

Supervised learning algorithms, often used to find the I/O relationship in data, have the tendency to be trapped in local optima as opposed to the desirable global optima. In this paper, we discuss the RPHP learning algorithm. The algorithm uses Real Coded Genetic Algorithm based global and local searches to find a set of pseudo global optimal solutions. Each pseudo global optimum is a local optimal solution from the point of view of all the patterns but globally optimal from the point of view of a subset of patterns. Together with RPHP, a Kth nearest neighbor algorithm is used as a second level pattern distributor to solve a test pattern. We also show theoretically the condition under which finding several pseudo global optimal solutions requires a shorter training time than finding a single global optimal solution. As the difficulty of curve fitting problems is easily estimated, we verify the capability of the RPHP algorithm against them and compare the RPHP algorithm with three counterparts to show the benefits of hybrid learning and active recursive subset selection. The RPHP shows a clear superiority in performance. We conclude our paper by identifying possible loopholes in the RPHP algorithm and proposing possible solutions