La reforma de la gobernanza de la educación superior en la práctica. Puesta en práctica de los objetivos políticos en la gestión universitaria

La millora de la governança és un dels temes més importants de l'agenda política de l'ensenyament superior en la Comissió Europea. Després de molts anys treballant en aquest camp, l'experiència demostra que en el cas d'Europa és important considerar la diversitat de sistemes educatius i de tipologies d'institucions d'ensenyament superior a l'hora d'analitzar els models de governança que s'han d'aplicar en cada cas. Això comporta que no hi hagi un model de governança ideal per a cada institució o sistema d'ensenyament superior i que l'estratègia variï segons els objectius i el tipus de cada institució d'ensenyament superior. La globalització creixent de l'ensenyament superior i la crisi econòmica afecten directament les institucions, cosa que les obliga a posicionar-se en aquest context. Implantar un model de governança adequat a l'estratègia institucional triada és essencial, si bé els models de bones pràctiques que es poden fer servir en un cas no són directament aplicables a un altre. La Comissió Europea treballa amb dues menes d'instruments en l'àrea de governança: d'una banda, els instruments polítics fomenten l'intercanvi de bones pràctiques, l'aprenentatge mutu entre governs, països i institucions i la creació d'eines per a la rendició de comptes; d'altra banda, els instruments financers mitjançant programes com Erasmus faciliten l'elaboració de projectes pilot i estudis que es presenten a iniciativa de consorcis transnacionals. Recentment va adquirint importància la necessitat de proporcionar més transparència quant a l'activitat i l'eficiència amb què treballen les institucions d'ensenyament superior. Aquest article revisa les últimes tendències de la governança a Europa, posant un èmfasi especial en la necessitat de preservar la diversitat de sistemes i tipus d'institucions amb les eines de transparència per a líders institucionals i donant exemples dels projectes de cooperació més rellevants en matèria de governança.Governance is one of the most important higher education policy reform areas in the European Commission. The experience of many years in this field shows that in Europe the diversity of both higher education systems and higher education institution typologies is an important aspect to consider when assessing which governance model to apply in each case. Due to this diversity, there is no ideal governance model for each institution and/or higher education system, and the most appropriate strategy to follow varies depending on the mission and typology of each higher education institution. The increasing tendency towards the globalization of higher education and the economic crisis are impacting directly on universities, forcing them to position themselves in this context. Implementing an appropriate governance model according to the corporate strategy of choice is essential, although good practice models in one case might not be directly applicable to another. The European Commission works in parallel with two types of governance instruments: firstly, policy instruments to promote the exchange of good practice and mutual learning between and among governments, countries and institutions. In recent years, the need for transparency regarding how different higher education institutions perform in the various fields where they operate is of increasing interest. Moreover, financial instruments available through programmes such as Erasmus facilitate the development of pilot projects and studies presented at the initiative of transnational consortia. This article reviews the latest trends in higher education governance in Europe, with special emphasis on the need to preserve the diversity of higher education systems and institutions through transparency tools, showing relevant examples of cooperation projects for improving governance practices.La mejora de la gobernanza es uno de los temas más importantes de la agenda política de la educación superior en la Comisión Europea. Tras muchos años trabajando en este campo, la experiencia demuestra que en el caso de Europa es importante considerar la diversidad de sistemas educativos y de tipologías de instituciones de educación superior a la hora de analizar los modelos de gobernanza que han de aplicarse en cada caso. Esto conlleva que no exista un modelo de gobernanza ideal para cada institución y/o sistema de educación superior, y que la estrategia varíe según los objetivos y el tipo de cada institución de educación superior. La creciente globalización de la educación superior y la crisis económica están afectando directamente a las instituciones, obligándolas a posicionarse en este contexto. Implantar un modelo de gobernanza adecuado a la estrategia institucional elegida es esencial, si bien los modelos de buenas prácticas que pueden usarse en un caso no son directamente aplicables a otro. La Comisión Europea trabaja con dos tipos de instrumentos en el área de gobernanza: por un lado, los instrumentos políticos fomentan el intercambio de buenas prácticas, el aprendizaje mutuo entre gobiernos, países e instituciones, y la creación de herramientas para la rendición de cuentas. Por otro lado, los instrumentos financieros a través de programas como Erasmus facilitan la elaboración de proyectos piloto y estudios que se presentan a iniciativa de consorcios transnacionales. Recientemente está cobrando importancia la necesidad de proporcionar mayor transparencia en cuanto a la actividad y la eficiencia con la que trabajan las instituciones de educación superior. Este artículo revisa las últimas tendencias de la gobernanza en Europa, con especial énfasis en la necesidad de preservar la diversidad de sistemas y tipos de instituciones a través de las herramientas de transparencia para líderes institucionales, dando ejemplos de los proyectos de cooperación más relevantes en materia de gobernanza

La evaluación de postgrados internacionales en la Unión Europea. Ejemplos de buenas prácticas de programas europeos

Peer reviewedDesde 1999 la Comisión Europea ha promovido varios programas de cooperación curricular entre instituciones de educación superior ayudando a acelerar el proceso de reforma del EEES. Si bien existen programas educativos de diversos tipos, Erasmus Mundus es uno de los que más impacto ha tenido en la comunidad universitaria europea. Desde 2004 este programa promueve y financia la existencia de másteres internacionales organizados por tres universidades de tres estados europeos diferentes, como mínimo. El objetivo último es potenciar la existencia de másteres que no podrían tener esa calidad sin la cooperación entre instituciones, así como generar un valor añadido europeo. A su vez, esto requiere un gran grado de coordinación y planificación entre las universidades no sólo en aspectos académicos sino también organizativos. Actualmente se está impulsando la creación de titulaciones dobles o conjuntas entre varias universidades, incluyendo la cooperación transnacional. Sin embargo, la ENQA y sus agencias no han propuesto hasta el momento una serie de criterios específicos para evaluar la complejidad añadida de estas titulaciones en cuanto al grado de integración y coordinación que requieren. En este contexto, tanto la ENQA como los distintos ministerios de educación superior observan la experiencia de Erasmus Mundus como un ejemplo de buenas (y malas) prácticas del que se pueden extraer conclusiones muy relevantes para poder evaluar la calidad de titulaciones interuniversitarias, ya sean nacionales o transnacionales. En este artículo se revisan los criterios más relevantes de la evaluación del programa Erasmus Mundus en la UE, así como el proceso de evaluación y las experiencias más relevantes tras cinco años de convocatorias de este programa.Des de 1999, la Comissió Europea ha promogut diversos programes de cooperació curricular entre institucions d'educació superior, cosa que ha ajudat a accelerar el procés de reforma de l'EEES. Si bé hi ha programes educatius de diversos tipus, Erasmus Mundus és un dels que ha tingut més impacte en la comunitat universitària europea. Des de 2004, aquest programa promou i finança l'existència de màsters internacionals organitzats per tres universitats de tres estats europeus diferents, com a mínim. L'objectiu últim és potenciar l'existència de màsters que no podrien tenir aquesta qualitat sense la cooperació entre institucions, així com generar un valor afegit europeu. Al seu torn, això requereix un gran grau de coordinació i planificació entre les universitats no solament en aspectes acadèmics, sinó també organitzatius. Actualment, s'està impulsant la creació de titulacions dobles o conjuntes entre diverses universitats, incloent-hi la cooperació transnacional. Tanmateix, l'ENQA i les seves agències no han proposat fins al moment una sèrie de criteris específics per avaluar la complexitat afegida d'aquestes titulacions quant al grau d'integració i coordinació que requereixen. En aquest context, tant l'ENQA com els diferents ministeris d'educació superior observen l'experiència d'Erasmus Mundus com un exemple de bones (i dolentes) pràctiques del qual es poden extreure conclusions molt rellevants per a poder avaluar la qualitat de titulacions interuniversitàries, tant nacionals com transnacionals. En aquest article es revisen els criteris més rellevants de l'avaluació del programa Erasmus Mundus a la UE, així com el procés d'avaluació i les experiències més rellevants després de cinc anys de convocatòries d'aquest programa.Since 1999, the European Commission has promoted many cooperation programmes for higher education institutions which have contributed to push forward European higher education area reforms. While there are many different education programmes, Erasmus Mundus is among those that have had the most impact on European higher education. Since 2004, this programme has promoted and provided funding for implementing international Master's organised by at least 3 institutions from three different EU countries. The main aim is to promote high quality Master's, as well as to generate a European added value. This requires major coordination and planning effort between universities, not only in academic but also in organisational aspects. Nowadays the creation of transnational double or joint degrees is being especially promoted. However, ENQA and its European evaluation agencies have not yet proposed specific criteria for evaluating their added complexity. In this context, both ENQA and higher education ministries see the Erasmus Mundus programme as a valuable demonstration of good (and bad) practices, from which relevant conclusions can be drawn to properly evaluate inter-university degrees, both national and international. This paper reviews the most relevant criteria of Erasmus Mundus as well as the evaluation procedure, and the most relevant conclusions after five years of calls for proposals on this programme

EDA-PSO: a hybrid paradigm combining Estimation of Distribution Algorithms and particle swarm optimization

Estimation of Distribution Algorithms (EDAs) is an evolutionary computation optimization paradigm that relies the evolution of each generation on calculating a probabilistic graphical model able to reflect dependencies among variables out of the selected individuals of the population. This showed to be able to improve results with GAs for complex problems. This paper presents a new hybrid approach combining EDAs and particle swarm optimization, with the aim to take advantage of EDAs capability to learn from the dependencies between variables while profiting particle swarm’s optimization ability to keep a sense of ”direction” towards the most promising areas of the search space. Experimental results show the validity of this approach with widely known combinatorial optimization problems

Evolutionary computation based on Bayesian classifiers

Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by introducing a new evolutionary computation method based on the learning and later simulation of a Bayesian classifier in every generation. In the method we propose, at each iteration the selected group of individuals of the population is divided into different classes depending on their respective fitness value. Afterwards, a Bayesian classifier—either naïve Bayes, semi naive Bayes, tree augmented naive Bayes or a similar one—is learned to model the corresponding supervised classification problem. The simulation of the latter Bayesian classifier provides individuals that form the next generation. Experimental results are presented to compare the performance of this new method with different types of EDAs and GAs. The problems chosen for this purpose are combinatorial optimization problems which are commonly used in the literature

Selection of human embryos for transfer by Bayesian classifiers

In this work we approach by Bayesian classifiers the selection of human embryos from images. This problem consists of choosing the embryos to be transferred in human-assisted reproduction treatments, which Bayesian classifiers address as a supervised classification problem. Different Bayesian classifiers capable of taking into account diverse dependencies between variables of this problem are tested in order to analyse their performance and validity for building a potential decision support system. The analysis by receiver operating characteristic (ROC) proves that the Bayesian classifiers presented in this paper are an appropriated and robust approach for this aim. From the Bayesian classifiers tested, the tree augmented naïve Bayes, k-dependence Bayesian and naïve Bayes classifiers showed to perform almost as well as the semi naïve Bayes and selective naïve Bayes classifiers

Gaussian-Stacking multiclassifiers for human embryo selection

Infertility is currently considered an important social problem that has been subject to special interest by medical doctors and biologists. Due to ethical reasons, different legislative restrictions apply in every country on human assisted reproduction techniques such as in-vitro fertilization (IVF). An essential problem in human assisted reproduction is the selection of suitable embryos to transfer in a patient, for which the application of artificial intelligence as well as data mining techniques can be helpful as decision-support systems. In this chapter we introduce a new multi-classification system using Gaussian networks to combine the outputs (probability distributions) of standard machine learning classification algorithms. Our method proposes to consider these outputs as inputs for a superior-level and to apply a stacking scheme to provide a meta-level classification result. We provide a proof of the validity of the approach by employing this multi-classification technique to a complex real medical problem: The selection of the most promising embryo-batch for human in-vitro fertilization treatments

Inexact graphmatch ing for model-based recognition: evaluation and comparison of optimization algorithms

A method for segmentation and recognition of image structures based on graph homomorphisms is presented in this paper. It is a model-based recognition method where the input image is over-segmented and the obtained regions are represented by an attributed relational graph (ARG). This graph is then matched against a model graph thus accomplishing the model-based recognition task. This type of problem calls for inexact graph matching through a homomorphism between the graphs since no bijective correspondence can be expected, because of the over-segmentation of the image with respect to the model. These arch for the best homomorphism is carried out by optimizing an objective function based on similarities between object and relational attributes defined on the graphs. The following optimization procedures are compared and discussed: deterministic tree search, for which new algorithms are detailed, genetic algorithms and estimation of distribution algorithms. In order to assess the performance of these algorithms using real data, experimental results on supervised classification of facial features using face images from public databases are presented

Inexact graphmatch ing by means of estimation of distribution algorithms

Estimation of distribution algorithms (EDAs) are a quite recent topic in optimization techniques. They combine two technical disciplines of soft computing methodologies: probabilistic reasoning and evolutionary computing. Several algorithms and approaches have already been proposed by different authors, but up to now there are very few papers showing their potential and comparing them to other evolutionary computational methods and algorithms such as genetic algorithms (GAs). This paper focuses on the problem of inexact graph matching which is NP-hard and requires techniques to find an approximate acceptable solution. This problem arises when a nonbijective correspondence is searched between two graphs. A typical instance of this problem corresponds to the case where graphs are used for structural pattern recognition in images. EDA algorithms are well suited for this type of problems. This paper proposes to use EDA algorithms as a new approach for inexact graph matching. Also, two adaptations of the EDA approach to problems with constraints are described as two techniques to control the generation of individuals, and the performance of EDAs for inexact graph matching is compared with the one of GAs

Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains

This paper introduces a evolutionary computation method that applies Bayesian classifiers to optimization problems. This approach is based on Estimation of Distribution Algorithms (EDAs)in which Bayesian or Gaussian networks are applied to the evolution of a population of individuals (i.e. potential solutions to the optimization problem) in order to improve the quality of the individuals of the next generation. Our new approach, called Evolutionary Bayesian Classifier-based Optimization Algorithm (EBCOA), employs Bayesian classifiers instead of Bayesian or Gaussian networks in order to evolve individuals to a fitter population. In brief, EBCOAs are characterized by applying Bayesian classification techniques – usually applied to supervised classification problems – to optimization in continuous domains. We propose and review in this paper different Bayesian classifiers for implementing our EBCOA method, focusing particularly on EBCOAs applying naïve Bayes, semi-naïve Bayes, and tree augmented naïve Bayes classifiers. This work presents a deep study on the behaviour of these algorithms with classical optimization problems in continuous domains. The different parameters used for tuning the performance of the algorithms are discussed, and a comprehensive overview of their influence is provided. We also present experimental results to compare this new method with other state of the art approaches of the evolutionary computation field for continuous domains such as Evolutionary Strategies (ES) and Estimation of Distribution Algorithms (EDAs)