Using the system dynamics paradigm in teaching and learning technological university subjets

2nd International Conference on Education and New Learning Technologies

Motores térmicos de vapor, gas y de combustión interna a través de PBL (Project Based Learning)

Nivel educativo: Grado. Duración (en horas): De 31 a 40 horas

Iraupen luzeko irakasleriaren garapenaren inpaktu-mailak: kontzepzioetan, hezkuntza hurbilketan eta kultura instituzionalean

[EUS]Unibertsitateko irakasleriaren garapenaren(IG) kontzeptu konprentsibotik abiatuta, doktorego tesi honek iraupen luzeko IG programen inpaktua du aztergai, bai maila indibidualean (kontzepzio eta hurbilketan) eta baita maila organizazional zein instituzionalean ere. Azterketa hau burutzeko metodologia aktiboen (arazoetan, proiektuetan eta kasuetan oinarritutako ikaskuntza) ERAGIN programaren lehendabiziko promozioa hartuko da kasu gisa. Iraupen luzeko estrategiaren (350 ordu) bidez eta ko-mentoria taldeen funtzionamenduan oinarrituz, ikerlan enpirikoak IG-ak irakasleriaren ikas-irakaskuntza kontzepzioetan eta hurbilketan izandako inpaktuaz ageriko ebidentziak ematen ditu, baina baita ikas-irakaskuntzaren inguruan ikertzeko (scholarship of teaching and learning) eta irakaskuntza eremuetan liderra izateko gaitasunaz ere. Honako alderdiok aldaketa organizazionalean murgiltzen gaituzte eta curriculum hibridoaren pausokako gauzapenaren alde lan egiten dute.

Efficient learning of decomposable models with a bounded clique size

The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Aprendizaje basado en proyectos aplicado a la asignatura Ingeniería del software : guía del docente y guía del estudiante

Este informe recoge las guías del docente y del estudiante para la puesta en marcha, seguimiento continuo y evaluación de la asignatura Ingeniería del Software del segundo curso del Grado en Ingeniería Informática. Todo ello basado en metodologías activas, concretamente la metodología de Aprendizaje Basado en Proyectos (ABP, o PBL de Project Based Learning). El trabajo publicado en este informe es el resultado obtenido por los autores dentro del programa de formación del profesorado en metodologías activas (ERAGIN), auspiciado por el Vicerrectorado de Calidad e Innovación Docente de la Universidad del País Vasco (UPV/EHU).

Taller de aprendizaje Cooperativo para ingeniería

Se presentan herramientas y material de apoyo para aplicar la metodología de aprendizaje cooperativo en la docencia universitaria, con una orientación especial a ingeniería.

Aprendizaje basado en proyectos en Diseño y Construcción de Sistemas Digitales

Duración (en horas): Más de 50 horas. Destinatario: Estudiante y Docente

¿Cómo afecta la introducción de las metodologías del Aprendizaje Cooperativo y del Aprendizaje Basado en Proyectos en la opinión de los/las estudiantes sobre su profesorado

XX1 CUIEET - Congreso Universitario de Innovación Educativa en las Enseñanzas Técnicas, Valencia, 2013.

The Computer Input/Output Subsystem Education in an undergraduate introductory course: a Multiperspective Study

240 p.

Lehen Hezkuntzako ingeleseko testuliburuak. Euskal Autonomia Erkidegoan erabiltzen diren materialen analisia

[eus] Ikerketa honen bidez, Euskal Autonomia Erkidegoan erabiltzen diren Lehen Hezkuntzako lehenengo zikloko ingeleseko testuliburuak hizkuntzen ikaskuntzaren teoriei, Europako Erreferentzia Marko Bateratuari eta Curriculum dokumentuei egokitzen zaizkien baieztatu nahi izan da. Horretarako, aipatutako teorietan eta dokumentuetan oinarrituta, sei aldagai diseinatu eta, horiek erabiliz, Euskal Autonomia Erkidegoan erabiltzen diren sei testuliburu aztertu dira. Ikerketatik ondorioztatzen da aztertutako testuliburuak ez datozela bat legediaren ikuspegi komunikatiboarekin ezta atazetan oinarritutako ikaskuntzarekin ere. Hala ere, hizkuntzaren inputa testuinguruan ematen dute eta, Curriculumean eskatzen den bezala, aspektu kulturalak lantzen dituzte.

The use of role-play games in teaching: The International Climate Negotiation Game

This paper presents a role-play game designed by the authors, which focuses on international climate negotiations. The game has been used at a university with students all drawn from the same course and at summer schools with students from different levels (undergraduate, master’s and doctoral students and post-doctoral researchers) and different knowledge areas (economics, law, engineering, architecture, biology and others). We discuss how the game fits into the process of competence-based learning, and what benefits games, and role-play games in particular, have for teaching. In the game, students take on the role of representatives of national institutions and experience at first hand a detailed process of international negotiation concerned with climate change.