Adaptive integral assessment package for the A2UN@ project

This paper presents a first approach of Evaluation Engine Architecture (EEA) as proposal to support adaptive integral assessment, in the context of a virtual learning environment. The goal of our research is design an evaluation engine tool to assist in the whole assessment process within the A2UN@ project, linking that tool with the other key elements of a learning design (learning task, learning resources and learning support). The teachers would define the relation between knowledge, competencies, activities, resources and type of assessment. Providing this relation is possible obtain more accurate estimations of student's knowledge for adaptive evaluations and future recommendations. The process is supported by usage of educational standards and specifications and for an integral user modelling

Assessment and reuse of contents in the competence-based educational platform InterMediActor

This paper describes a failure alert system and a methodology for content reuse in a new instructional design system called InterMediActor (IMA). IMA provides an environment for instructional content design, production and reuse, and for students’ evaluation based in content specification through a hierarchical structure of competences. The student assessment process and information extraction process for content reuse are explained.

QuesTInSitu: from tests to routes for assessment in situ activities

Test-based assessment tools are mostly focused on the use of computers. However, advanced Information and Communication Technologies, such as handheld devices, opens up the possibilities of creating new assessment scenarios, increasing the teachers’ choices to design more appropriate tests for their subject areas. In this paper we use the term Computing-Based Testing (CBT) instead of Computer-Based Testing, as it captures better the emerging trends. Within the CBT context, the paper is centred on proposing an approach for “Assessment in situ” activities, where questions have to be answered in front of a real space/location (situ). In particular, we present the QuesTInSitu software implementation that includes both an editor and a player based on the IMS Question and Test Interoperability specification and GoogleMaps. With QuesTInSitu teachers can create geolocated questions and tests (routes), and students can answer the tests using mobile devices with GPS when following a route. Three illustrating scenarios and the results from the implementation of one of them in a real educational situation show that QuesTInSitu enables the creation of innovative, enriched and context-aware assessment activities. The results also indicate that the use of mobile devices and location-based systems in assessment activities facilitates students to put explorative and spatial skills into practice and fosters their motivation, reflection and personal observation.

Applying recommendations to aligncompetences, methodology and assessment in Telematics, Computing and Electronic Engineering courses

The alignment between competences, teaching-learning methodologies and assessment is a key element of the European Higher Education Area. This paper presents the efforts carried out by six Telematics, Computer Science and Electronic Engineering Education teachers towards achieving this alignment in their subjects. In a joint work with pedagogues, a set of recommended actions were identified. A selection of these actions were applied and evaluated in the six subjects. The cross-analysis of the results indicate that the actions allow students to better understand the methodologies and assessment planned for the subjects, facilitate (self-) regulation and increase students’ involvement in the subjects.

E-learning initiatives in forensic interpretation: report on experiences from current projects and outlook

This paper reports on the purpose, design, methodology and target audience of E-learning courses in forensic interpretation offered by the authors since 2010, including practical experiences made throughout the implementation period of this project. This initiative was motivated by the fact that reporting results of forensic examinations in a logically correct and scientifically rigorous way is a daily challenge for any forensic practitioner. Indeed, interpretation of raw data and communication of findings in both written and oral statements are topics where knowledge and applied skills are needed. Although most forensic scientists hold educational records in traditional sciences, only few actually followed full courses that focussed on interpretation issues. Such courses should include foundational principles and methodology - including elements of forensic statistics - for the evaluation of forensic data in a way that is tailored to meet the needs of the criminal justice system. In order to help bridge this gap, the authors' initiative seeks to offer educational opportunities that allow practitioners to acquire knowledge and competence in the current approaches to the evaluation and interpretation of forensic findings. These cover, among other aspects, probabilistic reasoning (including Bayesian networks and other methods of forensic statistics, tools and software), case pre-assessment, skills in the oral and written communication of uncertainty, and the development of independence and self-confidence to solve practical inference problems. E-learning was chosen as a general format because it helps to form a trans-institutional online-community of practitioners from varying forensic disciplines and workfield experience such as reporting officers, (chief) scientists, forensic coordinators, but also lawyers who all can interact directly from their personal workplaces without consideration of distances, travel expenses or time schedules. In the authors' experience, the proposed learning initiative supports participants in developing their expertise and skills in forensic interpretation, but also offers an opportunity for the associated institutions and the forensic community to reinforce the development of a harmonized view with regard to interpretation across forensic disciplines, laboratories and judicial systems.

In vivo assessment of use-dependent brain plasticity--beyond the "one trick pony" imaging strategy.

This article has been written as a comment to Dr Thomas and Dr Baker's article "Teaching an adult brain new tricks: A critical review of evidence for training-dependent structural plasticity in humans". We deliberately expand on the key question about the biological substrates underlying use-dependent brain plasticity rather than reiterating the authors' main points of criticism already addressed in more general way by previous publications in the field. The focus here is on the following main issues: i) controversial brain plasticity findings in voxel-based morphometry studies are partially due to the strong dependency of the widely used T1-weighted imaging protocol on varying magnetic resonance contrast contributions; ii) novel concepts in statistical analysis allow one to directly infer topological specificity of structural brain changes associated with plasticity. We conclude that iii) voxel-based quantification of relaxometry derived parameter maps could provide a new perspective on use-dependent plasticity by characterisation of brain tissue property changes beyond the estimation of volume and cortical thickness changes. In the relevant sections we respond to the concerns raised by Dr Thomas and Dr Baker from the perspective of the proposed data acquisition and analysis strategy.

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

The european higher education area at work: Lights and shadows defining continuous assessment

The aim of this paper is to analyse how learning assessment, particularly the Continuous Assessment system, has been defined in the Public Administration and Management Diploma Course of the University of Barcelona (Spain). This course was a pioneering experiment at this university in implementing the guidelines of the European Higher Education Area (EHEA), and thus represents a good case study for verifying whether one of the cornerstones of the EHEA has been accomplished with success. Using data obtained from the Teaching Plans elaborated by the lecturers of each subject, we are able to establish that the CA system has been progressively accepted to such an extent that it is now the assessment formula used by practically all of the lecturers, conforming in this way to the protocols laid down by the Faculty of Law in which this diploma course is taught. Nevertheless, we find that high dispersion exists in how Continuous Assessment is actually defined. Indeed, it seems that there is no unified view of how Continuous Assessment should be performed. This dispersion, however, seems to diminish over time and raises some questions about the advisability of agreement on criteria, considering the potential which CA has as a pedagogical tool. Moreover, we find that the Unique Assessment system, which students may also apply for, is an option chosen only by a minority, with lecturers usually defining it as merely a theoretical and/or practical test, of little innovation in relation to traditional tests.

De la perception à l'apprentissage [From perception to learning]

Dans le domaine de la perception, l'apprentissage est contraint par la présence d'une architecture fonctionnelle constituée d'aires corticales distribuées et très spécialisées. Dans le domaine des troubles visuels d'origine cérébrale, l'apprentissage d'un patient hémi-anopsique ou agnosique sera limité par ses capacités perceptives résiduelles, mais un déficit de reconnaissance visuelle de nature apparemment perceptive, peut également être associé à une altération des représentations en mémoire à long terme. Des réseaux neuronaux distincts pour la reconnaissance - cortex temporal - et pour la localisation des sons - cortex pariétal - ont été décrits chez l'homme. L'étude de patients cérébro-lésés confirme le rôle des indices spatiaux dans un traitement auditif explicite du « where » et dans la discrimination implicite du « what ». Cette organisation, similaire à ce qui a été décrit dans la modalité visuelle, faciliterait les apprentissages perceptifs. Plus généralement, l'apprentissage implicite fonde une grande partie de nos connaissances sur le monde en nous rendant sensible, à notre insu, aux règles et régularités de notre environnement. Il serait impliqué dans le développement cognitif, la formation des réactions émotionnelles ou encore l'apprentissage par le jeune enfant de sa langue maternelle. Le caractère inconscient de cet apprentissage est confirmé par l'étude des temps de réaction sériels de patients amnésiques dans l'acquisition d'une grammaire artificielle. Son évaluation pourrait être déterminante dans la prise en charge ré-adaptative. [In the field of perception, learning is formed by a distributed functional architecture of very specialized cortical areas. For example, capacities of learning in patients with visual deficits - hemianopia or visual agnosia - from cerebral lesions are limited by perceptual abilities. Moreover a visual deficit in link with abnormal perception may be associated with an alteration of representations in long term (semantic) memory. Furthermore, perception and memory traces rely on parallel processing. This has been recently demonstrated for human audition. Activation studies in normal subjects and psychophysical investigations in patients with focal hemispheric lesions have shown that auditory information relevant to sound recognition and that relevant to sound localisation are processed in parallel, anatomically distinct cortical networks, often referred to as the "What" and "Where" processing streams. Parallel processing may appear counterintuitive from the point of view of a unified perception of the auditory world, but there are advantages, such as rapidity of processing within a single stream, its adaptability in perceptual learning or facility of multisensory interactions. More generally, implicit learning mechanisms are responsible for the non-conscious acquisition of a great part of our knowledge about the world, using our sensitivity to the rules and regularities structuring our environment. Implicit learning is involved in cognitive development, in the generation of emotional processing and in the acquisition of natural language. Preserved implicit learning abilities have been shown in amnesic patients with paradigms like serial reaction time and artificial grammar learning tasks, confirming that implicit learning mechanisms are not sustained by the cognitive processes and the brain structures that are damaged in amnesia. In a clinical perspective, the assessment of implicit learning abilities in amnesic patients could be critical for building adapted neuropsychological rehabilitation programs.]

The european higher education area at work: Lights and shadows defining continuous assessment

The aim of this paper is to analyse how learning assessment, particularly the Continuous Assessment system, has been defined in the Public Administration and Management Diploma Course of the University of Barcelona (Spain). This course was a pioneering experiment at this university in implementing the guidelines of the European Higher Education Area (EHEA), and thus represents a good case study for verifying whether one of the cornerstones of the EHEA has been accomplished with success. Using data obtained from the Teaching Plans elaborated by the lecturers of each subject, we are able to establish that the CA system has been progressively accepted to such an extent that it is now the assessment formula used by practically all of the lecturers, conforming in this way to the protocols laid down by the Faculty of Law in which this diploma course is taught. Nevertheless, we find that high dispersion exists in how Continuous Assessment is actually defined. Indeed, it seems that there is no unified view of how Continuous Assessment should be performed. This dispersion, however, seems to diminish over time and raises some questions about the advisability of agreement on criteria, considering the potential which CA has as a pedagogical tool. Moreover, we find that the Unique Assessment system, which students may also apply for, is an option chosen only by a minority, with lecturers usually defining it as merely a theoretical and/or practical test, of little innovation in relation to traditional tests.

The european higher education area at work: Lights and shadows defining continuous assessment

The aim of this paper is to analyse how learning assessment, particularly the Continuous Assessment system, has been defined in the Public Administration and Management Diploma Course of the University of Barcelona (Spain). This course was a pioneering experiment at this university in implementing the guidelines of the European Higher Education Area (EHEA), and thus represents a good case study for verifying whether one of the cornerstones of the EHEA has been accomplished with success. Using data obtained from the Teaching Plans elaborated by the lecturers of each subject, we are able to establish that the CA system has been progressively accepted to such an extent that it is now the assessment formula used by practically all of the lecturers, conforming in this way to the protocols laid down by the Faculty of Law in which this diploma course is taught. Nevertheless, we find that high dispersion exists in how Continuous Assessment is actually defined. Indeed, it seems that there is no unified view of how Continuous Assessment should be performed. This dispersion, however, seems to diminish over time and raises some questions about the advisability of agreement on criteria, considering the potential which CA has as a pedagogical tool. Moreover, we find that the Unique Assessment system, which students may also apply for, is an option chosen only by a minority, with lecturers usually defining it as merely a theoretical and/or practical test, of little innovation in relation to traditional tests.

The Arts (Music, Arts&Crafts and Drama) as a procedure for learning pre-school content: Grupo Edebé Project

There is nothing as amazing and fascinating as children learning process. Between 0 and 6 years old, a child brain develops in a waythat will never be repeated. At this age, children are eager to discover and they have great potential of active and affective life.Because of this, their learning capacity in this period is incalculable. (Jordan-Decarbo y Nelson, 2002; Wild, 1999).Pre-school Education is a unique and special stage, with self identity, which aims are:attending children as a whole,motivate them to learn,give them an affective and stable environment in which they can grow up and get to be balanced and confident people and inwhich they can relate to others, learn, enjoy and be happy.Arts, Music, Visual Arts and Drama (Gardner, 1994) can provide a framework of special, even unique, personal expression.With the aim of introducing qualitative improvements in the education of children and to ensure their emotional wellbeing, and havingnoticed that teachers had important needs and concerns as regards to diversity in their student groups, we developed a programbased on the detection of needs and concerns explained by professionals in education.This program of Grupo edebé, object of our research, is a multicultural, interdisciplinary and globalizing project the aims of which are:developing children's talent and personality,keeping their imagination and creativity and using these as a learning resource,promoting reasoning, favouring expression and communication,providing children with the tools to manage their emotions,and especially, introducing Arts as a procedure to increase learning.We wanted to start the research by studying the impact (Brice, 2003) that this last point had on the learning of five-year-old childrenschooled in multicultural environments.Therefore, the main goal of the research was the assessment of the implementation of a child education programme attending todiversity in a population of five-year-old children, specifically in the practice of procedures based on the use of Arts (music, arts andcrafts and theatre) as a vehicle or procedure for learning contents in Pre-school stage.Because children emotional welfare was a subject of our concern, and bearing in mind that the affective aspects are of vitalimportance for learning and child development (Parke and Gauvain, 2009), Grupo Edebé has also evaluated the starting, evolving andfinal impact in five-year-old children given that they finish Pre-school education at that age.

Ambulatory assessment of 3D ground reaction force using plantar pressure distribution.

This study aimed to use the plantar pressure insole for estimating the three-dimensional ground reaction force (GRF) as well as the frictional torque (T(F)) during walking. Eleven subjects, six healthy and five patients with ankle disease participated in the study while wearing pressure insoles during several walking trials on a force-plate. The plantar pressure distribution was analyzed and 10 principal components of 24 regional pressure values with the stance time percentage (STP) were considered for GRF and T(F) estimation. Both linear and non-linear approximators were used for estimating the GRF and T(F) based on two learning strategies using intra-subject and inter-subjects data. The RMS error and the correlation coefficient between the approximators and the actual patterns obtained from force-plate were calculated. Our results showed better performance for non-linear approximation especially when the STP was considered as input. The least errors were observed for vertical force (4%) and anterior-posterior force (7.3%), while the medial-lateral force (11.3%) and frictional torque (14.7%) had higher errors. The result obtained for the patients showed higher error; nevertheless, when the data of the same patient were used for learning, the results were improved and in general slight differences with healthy subjects were observed. In conclusion, this study showed that ambulatory pressure insole with data normalization, an optimal choice of inputs and a well-trained nonlinear mapping function can estimate efficiently the three-dimensional ground reaction force and frictional torque in consecutive gait cycle without requiring a force-plate.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.