Traditional education vs modern education. What is the impact of teaching techniques' evolution on students learning process?

The main objective of this article is to focus on the analysis of teaching techniques, ranging from the use of the blackboard and chalk in old traditional classes, using slides and overhead projectors in the eighties and use of presentation software in the nineties, to the video, electronic board and network resources nowadays. Furthermore, all the aforementioned, is viewed under the different mentalities in which the teacher conditions the student using the new teaching technique, improving soft skills but maybe leading either to encouragement or disinterest, and including the lack of educational knowledge consolidation at scientific, technology and specific levels. In the same way, we study the process of adaptation required for teachers, the differences in the processes of information transfer and education towards the student, and even the existence of teachers who are not any longer appealed by their work due which has become much simpler due to new technologies and the greater ease in the development of classes due to the criteria described on the new Grade Programs adopted by the European Higher Education Area. Moreover, it is also intended to understand the evolution of students’ profiles, from the eighties to present time, in order to understand certain attitudes, behaviours, accomplishments and acknowledgements acquired over the semesters within the degree Programs. As an Educational Innovation Group, another key question also arises. What will be the learning techniques in the future?. How these evolving matters will affect both positively and negatively on the mentality, attitude, behaviour, learning, achievement of goals and satisfaction levels of all elements involved in universities’ education? Clearly, this evolution from chalk to the electronic board, the three-dimensional view of our works and their sequence, greatly facilitates the understanding and adaptation later on to the business world, but does not answer to the unknowns regarding the knowledge and the full development of achievement’s indicators in basic skills of a degree. This is the underlying question which steers the roots of the presented research.

Mejora de la enseñanza de la Geología mediante e-learning

Resumen: en este trabajo se presentan nuevas estrategias de enseñanza y aprendizaje a través de las nuevas tecnologías en su variante virtual a distancia (e-learning) implementadas en asignaturas relacionadas con la Geología. El objetivo básico fue acercar los aspectos geológicos a los estudiantes mediante el empleo de estas tecnologías. Se ha observado una mayor motivación y adquisición de conocimientos geológicos por parte del alumnado, que se ha traducido en una mejora en las calificaciones. Abstract: This paper deals with new teaching and learning approaches through the use of new technologies, mainly virtual distance learning (e-learning) in courses related to Geology. The main objective is to bring the geological aspects of Nature to students using these technologies. These new approaches have produced an increase in student motivation and acquisition of geological knowledge, accompanied by an improvement in their grades.

Applying concept mapping strategies to improve students’ lifelong learning in physical chemistry

Este artículo ofrece una reflexión sobre el papel de los mapas conceptuales en el actual escenario de la educación In the present paper, we carry out the application of concept mapping strategies to learning Physical Chemistry, in particular, of all aspect of Corrosion. This strategy is an alternative method to supplement examinations: it can show the teacher how much the students knew and how much they didn´t know; and the students can evaluate their own learning. Before giving tile matter on Corrosion, the teachers evaluated the previous knowledge of the students in the field and explained to the students how create the conceptual maps with Cmap tools. When the subject is finished, teachers are assessed the conceptual maps developed by students and therefore also the level of the students learning. Teachers verified that the concept mapping is quite suitable for complicated theorics as Corrosion and it is an appropriate tool for the consolidation of educational experiences and for improvement affective lifelong learning. By using this method we demonstrated that the set of concepts accumulated in the cognitive structure of every student in unique and every student has therefore arranged the concepts from top to bottom in the mapping field in different ways with different linking" phrases, although these are involved in the same learning task.

Microarray analysis of autoimmune diseases by machine learning procedures

—Microarray-based global gene expression profiling, with the use of sophisticated statistical algorithms is providing new insights into the pathogenesis of autoimmune diseases. We have applied a novel statistical technique for gene selection based on machine learning approaches to analyze microarray expression data gathered from patients with systemic lupus erythematosus (SLE) and primary antiphospholipid syndrome (PAPS), two autoimmune diseases of unknown genetic origin that share many common features. The methodology included a combination of three data discretization policies, a consensus gene selection method, and a multivariate correlation measurement. A set of 150 genes was found to discriminate SLE and PAPS patients from healthy individuals. Statistical validations demonstrate the relevance of this gene set from an univariate and multivariate perspective. Moreover, functional characterization of these genes identified an interferon-regulated gene signature, consistent with previous reports. It also revealed the existence of other regulatory pathways, including those regulated by PTEN, TNF, and BCL-2, which are altered in SLE and PAPS. Remarkably, a significant number of these genes carry E2F binding motifs in their promoters, projecting a role for E2F in the regulation of autoimmunity.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Evaluación para el aprendizaje en la asignatura sistemas operativos en modalidad online: un estudio cualitativo basado en la taxonomía de Bloom

La asignatura Sistemas Operativos presenta dificultades para su aprendizaje, pero poco se conoce acerca de las mismas, ya que no han sido determinadas ni estudiadas por la literatura. Asimismo, los trabajos existentes sobre la enseñanza y aprendizaje de Sistemas Operativos se limitan a proponer distintos enfoques para impartir la asignatura y en general no evalúan el aprendizaje de los estudiantes para comprobar la eficacia del método propuesto ni usan metodologías de investigación rigurosas. Por otra parte, la impartición de la asignatura Sistemas Operativos en modalidad online ha sido escasamente estudiada y podría tener dificultades adicionales a las de la modalidad presencial, ya que el contexto online impone una serie de restricciones tanto para el profesor como para el estudiante. En la presente tesis se ha llevado a cabo una evaluación formativa en la asignatura Sistemas Operativos, perteneciente al Grado de Ingeniería Informática de una universidad online. El objetivo inicial de la evaluación era descubrir las dificultades de los estudiantes para la comprensión de los conceptos de la asignatura. Posteriormente y, dada la buena aceptación de la evaluación por parte de los estudiantes, se ampliaron los objetivos del trabajo para explorar los efectos de la evaluación realizada sobre el aprendizaje. La evaluación formativa diseñada está basada en la taxonomía revisada de Bloom y sus principales objetivos son: (a) promover el aprendizaje significativo y (b) hacer a los estudiantes conscientes de su proceso de aprendizaje. La metodología de investigación utilizada es el estudio de caso cualitativo y la muestra está constituida por 9 estudiantes del total de 13 matriculados en la asignatura. Los datos cualitativos analizados proceden de las pruebas de evaluación formativa llevadas a cabo por los estudiantes durante la impartición de la asignatura. Los conceptos de sistemas operativos que han resultado más difíciles de comprender en el curso online estudiado han sido las interrupciones y los semáforos. Además, alrededor de estos conceptos se han identificado las dificultades específicas y sus posibles causas. Las dificultades descubiertas acerca de los semáforos corroboran las investigaciones existentes en el área de programación concurrente. El resto de las dificultades identificadas no habían sido determinadas por la literatura existente. En cuanto a los efectos de la evaluación formativa sobre el aprendizaje, la evidencia empírica muestra que ésta ha provocado en los estudiantes una reflexión profunda sobre los conceptos de la asignatura y sobre su propio proceso de aprendizaje. El estudio de caso presentado puede ayudar a los profesores del área de ingeniería a crear evaluaciones formativas en cursos online. La tesis, por tanto, realiza aportaciones relevantes en las áreas de enseñanza y aprendizaje de sistemas operativos, evaluación formativa, metodologías cualitativas y educación online. ABSTRACT Operating Systems is a difficult subject to learn; however little is known about said difficulties, as they have not been studied nor determined by the relevant literature. Existing studies on teaching and learning the subject of operating systems are limited to presenting different approaches for teaching the subject and generally do not evaluate students’ learning to verify the effectiveness of the proposed methods, nor do they use rigorous research methodologies. On the other hand, there are very few studies on teaching operating systems online, which may inherently present more difficulties than the in-person format, since an online context imposes a series of restrictions on both professors and students, such as not having face-to-face interaction for communications. This thesis studies a formative assessment of the subject of operating systems, as part of the Degree in Information Technology Engineering for an online university. The initial objective of this assessment was to determine the students’ difficulties in conceptual comprehension for this subject. Once students had accepted the assessment, the study’s objectives were expanded to include an investigation of the effects of the assessment on learning. The designed formative assessment was based on Revised Bloom’s Taxonomy with the following main objectives: (a) to promote meaningful learning and (b) (b) to make students aware of their learning process. The research methodology involves a qualitative case study with a sample consisting of 9 of the total 13 students registered for this course. The qualitative data analyzed comes from the formative assessment tests taken by these students during the course. The most difficult operating systems concepts for students in the online course were interrupts and semaphores. Additionally, the specific difficulties and their possible causes have been identified. The students’ comprehension difficulties with semaphores corroborate the existing research in the area of concurrent programming. The other identified difficulties were not discussed in the existing literature. Regarding the effects of the formative assessment on learning, the empirical evidence shows that it causes students to reflect carefully on the subject’s concepts as well as their own learning process. The presented case study can help professors in the area of engineering to create formative assessments for online courses. This thesis, therefore, makes relevant contributions to the areas of teaching and learning operating systems, formative assessment, qualitative methodologies, and online education.

Knowledge Management and Leadership in the Higher Education: A First Approach

Today, it is more and more important to develop competences in the learning process of the university students (that is to say, to acquire knowledge but also skills, abilities, attitudes and values). This is because professional practice requires that the future graduates design and market products, defend the interests of their clients, be introduced in the Administration or, even, in the Politics. Universities must form professionals that become social and opinion leaders, consultants, advisory, entrepreneurs and, in short, people with capacity to solve problems. This paper offers a tool to evaluate the application for the professor of different styles of management in the process of the student’s learning. Its main contribution consists on advancing toward the setting in practice of a model that overcomes the limitations of the traditional practices based on the masterful class, and that it has been applied in Portugal and Spain.

Use and abuse of audivisual media in the college classroom. Slides show and web pages

The multimedia development that has taken place within the university classrooms in recent years has caused a revolution at psychological level within the collectivity of students and teachers inside and outside the classrooms. The slide show applications have become a key supporting element for university professors, who, in many cases, rely blindly in the use of them for teaching. Additionally, ill-conceived slides, poorly structured and with a vast amount of multimedia content, can be the basis of a faulty communication between teacher and student, which is overwhelmed by the appearance and presentation, neglecting their content. The same applies to web pages. This paper focuses on the study and analysis of the impact caused in the process of teaching and learning by the slide show presentations and web pages, and its positive and negative influence on the student’s learning process, paying particular attention to the consequences on the level of attention within the classroom, and on the study outside the classroom. The study is performed by means of a qualitative analysis of student surveys conducted during the last 8 school Civil Engineering School at the Polytechnic University of Madrid. It presents some of the weaknesses of multimedia material, including the difficulties for students to study them, because of the many distractions they face and the need for incentives web pages offer, or the insignificant content and shallowness of the studies due to wrongly formulated presentations.

Visualizador de programas Java concurrentes

El presente trabajo fin de grado, que, a partir de ahora, denominaré TFG, consiste en elaborar una monitorización de programas concurrentes en lenguaje Java, para que se visualicen los eventos ocurridos durante la ejecución de los dichos programas. Este trabajo surge en el marco de la asignatura “Concurrencia” de la Escuela Técnica Superior de Ingeniería Informática de la Universidad Politécnica de Madrid, impartida por D. Julio Mariño y D. Ángel Herranz. El objetivo principal de este proyecto es crear una herramienta para el aprendizaje de la asignatura de concurrencia, facilitando la comprensión de los conceptos teóricos, de modo que puedan corregir los posibles errores que haya en sus prácticas. en este proyecto se expone el desarrollo de una librería de visualización de programas concurrentes programados en Java usando un formalismo gráfico similar al empleado en la asignatura. Además esta librería da soporte a los mecanismos de sincronización usados en las prácticas de la asignatura: la librería Monitor (desarrollada por los profesores de la asignatura, D. Ángel Herranz y D. Julio Mariño) y la librería JCSP (Universidad de Kent). ---ABSTRACT---This Bachelor Thesis addresses the problem of monitoring a Java program in order to trace and visualize a certain set of events produced during the execution of concurrent Java programs. This work originates in the subject "Concurrency" of the Computer Science and Engineering degree of our University. The main goal of this work is to have a tool that helps students learning the subject, so they can better understand the core concepts and correct common mistakes in the course practical work. We have implemented a library for visualizing concurrent Java programsusing a graphical notation similar to the one used in class, which supports the design of concurrent programs whose synchronization mechanisms are either monitors(using the Monitor package) or CSP(as implemented in the JCSP library from Kent University).

La Guerra de las Galaxias como metodología innovadora para mejorar la motivación

Numerosos estudiantes ven el estudio como algo aburrido, se sienten desmotivados. El docente no puede limitarse a aceptar la situación como un espectador. Es nuestra realidad y de la misma forma que cada estudiante debe responsabilizarse de su propia educación, los profesores tenemos que responsabilizarnos de cambiar ese sentimiento. Para ello se analizará en primer lugar el significado del término motivación y cuáles son los factores en los que los profesores podemos influir. Se verá que una de las formas de intervención es la innovación, por lo que también se analizará el vocablo y se discutirá qué constituye innovación y qué no. Por tanto, el docente se enfrenta a diario con la necesidad de encontrar nuevas formas de enseñar, que capten la atención de los alumnos. Se dice que no hay nada nuevo bajo el sol; sin embargo, hay que ser capaz de encontrar nuevos usos a con recursos que ya existen. Con el presente trabajo se pretende dar respuesta a esa necesidad. Se plantea el uso de una metodología que propone una innovación continua que consiste en establecer un paralelismo entre un relato o una historia y una unidad didáctica de la asignatura de Tecnología. Como historia se ha escogido la conocida película “La Guerra de las Galaxias” y el curso de referencia será el primer curso de Educación Secundaria Obligatoria (1º E.S.O.). En concreto se presenta un material adaptado con fotografías de la película, ejemplos inspirados en la saga y otras adaptaciones educativas. Con ello se consigue una sorpresa continua para el estudiante y así mantener su atención, facilitando su aprendizaje. Se tiene hecho parte del camino, aunque no todo. En este punto, se hace necesario introducir algunas medidas suplementarias, que refuercen y complementen esta metodología y que nos impidan apartarnos del objetivo pretendido: que los alumnos aprendan de manera significativa. ¿Quién dijo que estudiar es aburrido? A number of students see studying as something boring. The teacher cannot be a spectator. It is our reality and, so on one hand, the student has the responsibility of building his education and on the other, the teacher must change that feeling. In order to this, the term motivation will be analysed, and also which are the related aspects in which we have some influence. One of those ways of intervention is motivation. It is for that word, innovation will be analysed as well, discussing what innovation is and what is not. Therefore, teachers face, day by day, the need of finding new ways of teaching for students to pay attention in classes. As people say, there is nothing new under the sun; however, new uses for existing resources are required. This paper pretends to solve that problem. A continuous innovating methodology is set, consisting in establishing a parallelism between a story or tale and a didactic unit in Technology subject. As a story, the well-known film “Star Wars” is chosen, and as a reference course, the first course of Educación Secundaria Obligatoria (1st E.S.O.). Specifically, we introduce an adapted material with pictures from the movie, saga inspired examples and, some other educational adaptations. With it, students are continuously surprised and their attention grabbed, making his learning easier. Part of the problem is solved, but there is a long way to go. At this point, some supplementary steps are needed, in order to enforce and complement this methodology, and to avoid getting far away from the attempted objective: students learning in a significant way. Who said studying is annoying?

Fostering English-taught higher education programs in a Spanish university: the "TechEnglish" innovative project

In recent years, coinciding with adjustments to the Bologna process, many European universities have attempted to improve their international profile by increasing course offerings in English. According to the Institute of International Education (IIE), Spain has notably increased its English-taught higher education programs, ranking fifth in the list of European countries by number of English-taught Master's programs in 2013. This article presents the goals and preliminary results of an on-going innovative education project (TechEnglish) that aims to promote course offerings in English at the Technical University of Madrid (Universidad Politécnica de Madrid, UPM). The UPM is the oldest and largest of all Technical Universities in Spain. It offers graduate and postgraduate programs that cover all the engineering disciplines as well as architecture. Currently, the UPM has no specific bilingual/multilingual program to promote teaching in English, although there is an Educational Model Whitepaper (with a focus on undergraduate degrees) that promotes the development of activities like an International Semester or a unique shared curriculum. The TechEnglish project is an attempt to foster courses taught in English at 7 UPM Technical Schools, including students and 80 faculty members. Four tasks were identified: (1) to design a university wide framework to increase course offerings, (2) to identify administrative difficulties, (3) to increase visibility of courses offered, and (4) to disseminate the results of the project. First, to design a program we analyzed existing programs at other Spanish universities, and other projects and efforts already under way at the UPM. A total of 13 plans were analyzed and classified according to their relation with students (learning), professors (teaching), administration, course offerings, other actors/institutions within the university (e.g., language departments), funds and projects, dissemination activities, mobility plans and quality control. Second, to begin to identify administrative and organizational difficulties in the implementation of teaching in English, we first estimated the current and potential course offerings at the undergraduate level at the UPM using a survey (student, teacher and administrative demand, level of English and willingness to work in English). Third, to make the course offerings more attractive for both Spanish and international students we examined the way the most prestigious universities in Spain and in Europe try to improve the visibility of their academic offerings in English. Finally, to disseminate the results of the project we created a web page and a workspace on the Moodle education platform and prepared conferences and workshops within the UPM. Preliminary results show that increasing course offerings in English is an important step to promote the internationalization of the University. The main difficulties identified at the UPM were related to how to acknowledge/certify the departments, teachers or students involved in English courses, how students should register for the courses, how departments should split and schedule the courses (Spanish and English), and the lack of qualified personnel. A concerted effort could be made to increase the visibility of English-taught programs offered on-line.