Learning Activities with Semantic. Hypermedia in Higher Education

The increasing use of information and communication technologies (ICT) in diverse professional and personal contexts calls for new knowledge, and a set of abilities, competences and attitudes, for an active and participative citizenship. In this context it is acknowledged that universities have an important role innovating in the educational use of digital media to promote an inclusive digital literacy. The educational potential of digital technologies and resources has been recognized by both researchers and practitioners. Multiple pedagogical models and research approaches have already contributed to put in evidence the importance of adapting instructional and learning practices and processes to concrete contexts and educational goals. Still, academic and scientific communities believe further investments in ICT research is needed in higher education. This study focuses on educational models that may contribute to support digital technology uses, where these can have cognitive and educational relevance when compared to analogical technologies. A teaching and learning model, centered in the active role of the students in the exploration, production, presentation and discussion of interactive multimedia materials, was developed and applied using the internet and exploring emergent semantic hypermedia formats. The research approach focused on the definition of design principles for developing class activities that were applied in three different iterations in undergraduate courses from two institutions, namely the University of Texas at Austin, USA and the University of Lisbon, Portugal. The analysis of this study made possible to evaluate the potential and efficacy of the model proposed and the authoring tool chosen in the support of metacognitive skills and attitudes related to information structuring and management, storytelling and communication, using computers and the internet.

The tutor's role in student learning

Higher education in Portugal, in the last forty years, has undergone profound changes with the enlargement of public higher education network, the appearance of new institutions, the quantity and the heterogeneity of students. The implementation of the Bologna Process in European community countries led to the redesign of higher education Portuguese courses as well as their corresponding curricula. In recent years, the use of Project-led education was one of the most significant changes in teaching and learning, particularly in engineering in higher education in Portugal. This teaching methodology encourages students and teachers to undertake new roles, new responsibilities and a new learning perspective. This study aims at understanding whether the role of the tutor is to be suitable to the needs and expectations of Project-led education students. These changes however are not only structural. At the University of Minho, new teaching and learning methodologies were adopted, which could guide the training of professionals on to the twenty-first century. The opportunity arising from the implementation of Project-led education in Engineering methodology was used in the University of Minho’s courses. This teaching method is intended to provide students with educational support programs that benefit the academic performance, allowing the opportunity to upgrade, train and develop the ability to study and learn more effectively. Through the Project-led education it is possible to provide students with techniques and procedures and develop the ability to communicate orally and in writing. Students and teachers have assumed new roles in the teaching-learning process allowing in one hand the students to explore, discover and question themselves about some knowledge and on the other hand the teachers to change to a tutor, a companion and to a student project guide. Therefore, surveys were analyzed, comprising questions about the most significant contribution of the tutor as well as if there are some initial expectations that have not been foreseen by the tutor.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Biographical narratives in teaching: a study of rofessors on postgraduate courses

Literature and research have shown that professional development constitutes an essential dimension in constructing both work and professional identity. An important aspect in such development is training. In the field of adult education, different authors (Pratt, 1993; Mezirow, 1985; Schön, 1996; Silva, 2007) emphasize the importance of placing trainees at the center of the learning and cognitive processes and within their corresponding social and historical contexts. Training is supported by a comprehensive adult learning theory. Therefore, the acquired knowledge is not only the result of an external and objective reality but also of a complex construction in which the appropriation of experience plays a relevant role. This paper reveals the findings obtained through biographical narratives in a five-year work program with teachers at different levels (from pre-school to higher education) on postgraduate courses. The core issue is the importance of biographical narratives, as an identification strategy for personal experience, knowledge construction and professional identity. This strategy provided the opportunity for recognition of practical experience, as a provider of learning, as well as his/her own authorship, which are important conditions in the understanding of professional identity.

Problemáticas de la interacción discursiva en la enseñanza y el aprendizaje de las Ciencias Naturales. Aportes didácticos. Interaction discoursive issues in teaching and learning Natural Sciences. Didactic contributions

Las didácticas específicas de las ciencias naturales revelan diferentes problemáticas en su enseñanza y aprendizaje en los diferentes niveles del sistema educativo. En particular, en las clases de ciencias la interacción discursiva docente alumnos adquiere relevancia, ya que el proceso de comunicación del conocimiento es uno de los pilares didácticos, junto a la trasposición del mismo. Especificamente, en este proyecto nos abocamos a aquellas intervenciones de docentes y alumnos que se relacionan con la construcción del conocimiento biológico y químico. El proyecto se enmarca en una actual linea de trabajo que indaga sobre las dificultades en los abordajes del conocimiento científico en las aulas, las características del discurso entre docentes y alumnos, las habilidades y dificultades en la comprensión de los enunciados de problemas y las características de los textos que se utilizan en las clases. Se focaliza este estudio en casos que intentan dar respuesta a tres temáticas, agrupadas en un conjunto de situaciones de investigación relacionadas con la interacción discursiva docente-alumno, retomando el rol del docente al hablar, guiar o diseñar las situaciones de referencia para el aprendizaje de los alumnos. Los casos son: 1- En cuanto a las concepciones sobre diversidad biológica en estudiantes de escuela secundaria y en textos académicos, atendemos a cómo la escuela presenta los contenidos ecológicos como un conjunto de dogmas y conceptos estáticos. Además suelen simplificarse conceptualmente y presentarse poco actualizados. Es por ello que se planea estudiar las concepciones y actitudes de los alumnos de secundaria sobre la biodiversidad, cómo estas dificultan su comprensión y los textos usados en relación a la promoción de la transposición didáctica. 2- En relación a cómo se elabora el patrón temático del tema célula en clases de Biología, se analizarán las diferentes estrategias de significados y de desarrollo temático, que se emplean en la comunicación aulica. Se intentará establecer si hay cambios en el desarrollo temático a medida que se avanza en la escolaridad. Esto es porque se puede apreciar que muchos de los problemas de aprendizaje del alumnado se deben a un desconocimiento tanto del patrón temático como del patrón estructural de la ciencia, siendo preciso evocar los patrones temáticos que se quieren utilizar, para construir un conocimiento compartido. 3-Finalmente, en los enunciados de problemas de Química, se analizarán las dificultades de comprensión lectora de alumnos de Ingeniería. Los docentes frecuentemente atribuyen los problemas a deficiencias en la instrucción recibida, sin considerarse los conocimientos previos del alumno, los obstáculos conceptuales originados en el tema, las deficiencias en la habilidad lectora, el tipo textual predominante en la consigna, el formato en el que se escribió la consigna y los factores personales, etc., siendo que la comprensión del enunciado de una consigna de trabajo condiciona fuertemente la posibilidad de su resolución. Los tres casos utilizarán metodologías cualitaritas que incluyan análisis de contenido en discursos orales y escritos. Los datos se registrarán desde observación no participante, registro etnográfico y con grabaciones de audio. Se espera contribuir al conocimiento, realizando aportes a la formación docente en tanto las estrategias discursivas que se emplean en el aula, en forma oral y en la escrita, conocer concepciones que dificultan o favoren la construcción del conocimiento científico, entre otras. Los productos de estos estudios estarán integrados por nuevos desarrollos para la formación docente, publicaciones científicas de impacto nacional e internacional, presentaciones a congresos, materiales didácticos y divulgativos, dictado de seminarios y/o cursos, redacción de informes a las escuelas intervinientes.. The specific Natural Sciences didactics show different problems in teaching and learning along the school system. In particular, the discourse used to communicate knowledge in Science lessons becomes important. With this project we will focus on the teachers and students actions regarding the construction of biological and chemical knowledge. This project attempts to answer these issues and brings together a range of research situations related to teacher-student interaction, through discourse, taking up the role of the teacher to speak, to plan and to guide student learning. We will study the ideas and attitudes of high school students about biodiversity that make difficult its understanding and the textbooks used in relation to promotion of the didactic transposition. In addition, regarding how the thematic pattern in biology classes is costructed, it will be analyzed the different meaning and thematic development strategies that are used in communication. We will attempt to establish whether there are any changes in the thematic development throughout high school education. Finally, we will analyze the reading comprehension problems in engineering students. Teachers frequently attribute these issues to deficiencies in prior education, without considering the students background, the conceptual obstacles arising in the field, the format in which the prompt is written, personal factors, etc., keeping in mind that the outcome of an activity is strictly dependant con the prompt understanding. We expect to make contributions to the teacher education in both the discourse strategies used in the classroom, orally and in writing, to learn about the conceptions that hinder or favor the knowledge construction, among others. The products of this study will be national and international impact scientific publications, conference presentations, popular science publications, seminars courses and reports to the schools involeved.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.

How unethical actions can be learned : the analysis of the sporting life courses of doping athletes

The aim of the present article was to present a specific activity approach called the 'life course of a practice' that could be used for the analysis of the long-term dynamic of learning within diverse social practices. The cases of unethical actions (doping) within a population of high-level athletes were examined, considering they could be fruitful exam- 10 ples for this type of analysis. After describing the theoretical construct 'life course of a practice', the places of these unethical actions in the sporting life courses and elements that characterize their emergence in the long-term dynamics are described. Last, targeted prevention measures of unethical actions over a lifetime are presented.

Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

EduFeedr - following and supporting learners in open blog-based courses

In recent years several educators have organized open courses where participants reflect on their personal blogs. With a large number of participants it becomes a challenge to follow all the course discussions. In this paper we present the EduFeedr system that is specifically designed for following and supporting student activities in blog-based courses.

A Method and Tool to Support the Analysis and Enhance the Understanding of Peer-to-Peer Learning Experiences

In this paper we look at how a web-based social software can be used to make qualitative data analysis of online peer-to-peer learning experiences. Specifically, we propose to use Cohere, a web-based social sense-making tool, to observe, track, annotate and visualize discussion group activities in online courses. We define a specific methodology for data observation and structuring, and present results of the analysis of peer interactions conducted in discussion forum in a real case study of a P2PU course. Finally we discuss how network visualization and analysis can be used to gather a better understanding of the peer-to-peer learning experience. To do so, we provide preliminary insights on the social, dialogical and conceptual connections that have been generated within one online discussion group.

Using problem-based learning for introducing producer theory and market structure in intermediate microeconomics

This paper shows how instructors can use the problem‐based learning method to introduce producer theory and market structure in intermediate microeconomics courses. The paper proposes a framework where different decision problems are presented to students, who are asked to imagine that they are the managers of a firm who need to solve a problem in a particular business setting. In this setting, the instructors’ role isto provide both guidance to facilitate student learning and content knowledge on a just‐in‐time basis

Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasinglypopular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigationaladaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.