Impact of the intrinsic motivation on the academic performance through voluntary assignments: An empirical analysis

The students academic performance is a key aspect for all agents involved in a higher education quality program. However, there is no unanimity on how to measure it. Some professionals choose assessing only cognitive aspects while others lean towards assessing the acquisition of certain skills. The need to train increasingly adapted professionals in order to respond to the companies’ demands and being able to compete internationally in a global labour market requires a kind of training that goes beyond memorizing. Critical and logical thinking are amongst written language skills demanded in the field of Social Sciences. The objective of this study is to empirically demonstrate the impact of voluntary assignments on the academic performance of students. Our hypothesis is that students who complete high quality voluntary assignments are those more motivated and, therefore, those with higher grades. An experiment with students from the "Financial Accounting II" during the academic year of 2012/13 at the Business and Economics School of the UCM was carried out. A series of voluntary assessments involving the preparation of accounting essays were proposed in order to develop skills and competencies as a complement to the lessons included in the curriculum of the subject. At the end of the course, the carrying-out or not of the essay together with its critical, reflective quality and style, were compared. Our findings show a relationship between the voluntarily presented papers of quality and the final grade obtained throughout the course. These results show that the students intrinsic motivation is a key element in their academic performance. On the other hand, the teachers role focuses on being a motivating element through the learning process.

La visión artificial, un nuevo aliado para el análisis de imágenes artísticas

En este estudio se evalúa el rendimiento de los métodos de Bag-of-Visualterms (BOV) para la clasificación automática de imágenes digitales de la base de datos del artista Miquel Planas. Estas imágenes intervienen en la ideación y diseño de su producción escultórica. Constituye un interesante desafío dada la dificultad de la categorización de escenas cuando éstas difieren más por los contenidos semánticos que por los objetos que contienen. Hemos empleado un método de reconocimiento basado en Kernels introducido por Lazebnik, Schmid y Ponce en 2006. Los resultados son prometedores, en promedio, la puntuación del rendimiento es aproximadamente del 70%. Los experimentos sugieren que la categorización automática de imágenes basada en métodos de visión artificial puede proporcionar principios objetivos en la catalogación de imágenes y que los resultados obtenidos pueden ser aplicados en diferentes campos de la creación artística.