University teacher competences in a virtual teaching/learning environment : analysis of a teacher training experience

This paper attempts to shed light on the competencies a teacher must have inorder to teach in online university environments. We will relate a teacher trainingexperience, which was designed taking into account the methodological criteriaestablished in line with previous theoretical principles. The main objective of ouranalysis is to identify the achievements and difficulties of a specific formativeexperience, with the ultimate goal of assessing the suitability of this conceptualmethodologicalframework for the design of formative proposals aiming to contribute tothe development of teacher competencies for virtual environments.

Research and training in medicinal chemistry in south and central american countries and sub-saharan Africa

With the proposal to search for universal cooperation in the field of Medicinal Chemistry, the IUPAC group has elaborated a line of work divided into two phases: a- An Awareness of the true situation of Medicinal Chemistry in the different geographic areas of the world; b- A proposal of actions as to achieve more effective cooperation. This first report presents and discusses the actual situation in South and Central America as well as in sub-Saharan Africa.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

The Role of Firm Size in Training Provision Decisions: evidence from Spain

The level of training provided by small firms to their employees is below that provided by their larger counterparts. The provision of firm-related training is believed to be associated to certain characteristics of the firm. In this paper we argue that small firms provide fewer training opportunities as they are less likely to be associated with these characteristics than large firms. The suitability of estimating training decisions as a double-decision process is examined here: first, a firm has to decide whether to provide training or not and, second, having decided to do so, the amount of training to provide. The differences in training provision between small and large firms are decomposed in order to analyse the individual contribution of these characteristics to explaining the gap. The results show that small firms face greater obstacles in accessing training and that the main reasons for that are related to their technological activity and the geographical scope of the market in which they operate.

Supervised regionalization methods: A survey

This paper reviews almost four decades of contributions on the subject of supervised regionalization methods. These methods aggregate a set of areas into a predefined number of spatially contiguous regions while optimizing certain aggregation criteria. The authors present a taxonomic scheme that classifies a wide range of regionalization methods into eight groups, based on the strategy applied for satisfying the spatial contiguity constraint. The paper concludes by providing a qualitative comparison of these groups in terms of a set of certain characteristics, and by suggesting future lines of research for extending and improving these methods.

Supervised regionalization methods: A survey

This paper reviews almost four decades of contributions on the subject of supervised regionalization methods. These methods aggregate a set of areas into a predefined number of spatially contiguous regions while optimizing certain aggregation criteria. The authors present a taxonomic scheme that classifies a wide range of regionalization methods into eight groups, based on the strategy applied for satisfying the spatial contiguity constraint. The paper concludes by providing a qualitative comparison of these groups in terms of a set of certain characteristics, and by suggesting future lines of research for extending and improving these methods.

Music-supported training is more efficient than functional motor training for recovery of fine motor skills in stroke patients

MOTOR IMPAIRMENTS ARE COMMON AFTER STROKE but efficacious therapies for these dysfunctions are scarce. Extending an earlier study on the effects of music-supported training (MST), behavioral indices of motor function were obtained before and after a series of training sessions to assess whether this new treatment leads to improved motor functions. Furthermore, music-supported training was contrasted to functional motor training according to the principles of constraint-induced therapy (CIT). In addition to conventional physiotherapy, 32 stroke patients with moderately impaired motor function and no previous musical experience received 15 sessions of MST over a period of three weeks, using a manualized, step-bystep approach. A control group consisting of 15 patients received 15 sessions of CIT in addition to conventional physiotherapy. A third group of 30 patients received exclusively conventional physiotherapy and served as a control group for the other three groups. Fine as well as gross motor skills were trained by using either a MIDI-piano or electronic drum pads programmed to emit piano tones. Motor functions were assessed by an extensive test battery. MST yielded significant improvement in fine as well as gross motor skills with respect to speed, precision, and smoothness of movements. These improvements were greater than after CIT or conventional physiotherapy. In conclusion, with equal treatment intensity, MST leads to more pronounced improvements of motor functions after stroke than CIT.

The process of basic training, applied training, maintaining the performance of an observer

In the field of observational methodology the observer is obviously a central figure, and close attention should be paid to the process through which he or she acquires, applies, and maintains the skills required. Basic training in how to apply the operational definitions of categories and the rules for coding, coupled with the opportunity to use the observation instrument in real-life situations, can have a positive effect in terms of the degree of agreement achieved when one evaluates intra- and inter-observer reliability. Several authors, including Arias, Argudo, & Alonso (2009) and Medina and Delgado (1999), have put forward proposals for the process of basic and applied training in this context. Reid y De Master (1982) focuses on the observer's performance and how to maintain the acquired skills, it being argued that periodic checks are needed after initial training because an observer may, over time, become less reliable due to the inherent complexity of category systems. The purpose of this subsequent training is to maintain acceptable levels of observer reliability. Various strategies can be used to this end, including providing feedback about those categories associated with a good reliability index, or offering re-training in how to apply those that yield lower indices. The aim of this study is to develop a performance-based index that is capable of assessing an observer's ability to produce reliable observations in conjunction with other observers.

Sairaanhoitajaopiskelijoiden ohjattujen käytännön harjoittelujen aikana ilmenneet eettiset ongelmat

Ethical problems occurring during the practical training period of Finnish nursing students The present study focused on nursing students adopting the professional code of conduct during their supervised practical training. The study was carried out in two phases. During the first phase, the objective was to survey ethical problems occurring in practical training as well as how these problems are detected and resolved by nursing students and their supervisors at different stages of their studies. In the second phase, the capability of the nursing students about to graduate to detect and resolve ethical problems was described and analyzed. The students’ capacity for self-instruction, independent search for information as well as factors related to teaching of ethics were determined within this phase. Further, an extensive literature review was carried out to complement the study. Thus, the main objective of the thesis was to make suggestions for the development of the teaching of ethics and supervision in nursing studies and in practice. In the first part of the empirical phase (2002–2005), the views of the nursing students (n =18) were clarified with themed open essay questions. Furthermore, the views of the supervising nurses (n = 115) were established by utilizing a series of themed questions and group interviews. During the second phase (2006–2007), the data for the analyses were collected from nursing students in their graduating stage (n = 319) by a national Internet-based questionnaire. The results of the first phase were examined with contentanalysis and those of the second phase both statistically and by using content analysis. Ethical problems occurring during supervised practical training were typically connected to a patient or a client, a member of the nursing staff or to a student, while solutions were connected to preparation and the action to solve the problem in question. Ethical dilemmas were classified as legal, ethical comportment and uncertainty problems as well as personal and institutional ones. The solutions for these problems were further grouped as based on facts, instructor/staff/member/specialist or patient/client/relative. The results showed that although the nursing students about to graduate had detected many ethical problems both independently as well as together with the nursing staff during every practical training period, they were able to resolve only few of them. Ethical problems were most frequently encountered during training in psychiatric nursing. On the grounds of their own impressions, the nursing students stated that their ability to detect and solve ethical problems improved during their training period. The primary factors related to this enhancement of their skills were teaching and the students’ readiness for selfinstruction. Gender, orientation of the studies and age were observed to be the most important among the underlying factors influencing the capability to detect and solve ethical problems as well as to engage in self-instruction. Based on the results obtained, suggestions for development as well as topics for further studies are presented through teaching of professional ethics and supervision during practical training.

Un análisis optimista de la educación matemática en la formación de maestros de educación infantil = An optimistic analysis of mathematics education in training pre-school teachers

This article offers a panorama of mathematics training for future teachers at pre-school level in Spain. With this goal in mind, this article is structured infour sections: where we come from, where we are, where we’re going and where we want to go. It offers, in short, a brief analysis that shows the efforts made to ensure there is sufficient academic and scientific rigour in teachers’ studies at pre-school in general and students’ mathematics education in particular. Together with a description of the progress made in recent years, it also raises some questions for all those involved in training future teachers for this educational stage

Evaluating End-User Training Effectiveness in ERP System Implementation

Today’s business world demands more and more internal and external integration and transparency among companies at all fields. Integrated ERP (enterprise resource planning) systems offer a possibility to improve business practices and procedures by providing a unified view on the business including all functions and departments. Due to the obvious benefits, the popularity of integrated ERP systems keeps growing. The implementation of ERP systems has however proven risky. The implementation projects tend to be long, extensive, and costly – and often they end up in a failure. Due to the significant task and role changes ERP implementation brings to almost everybody in the company, training has been identified as one of the most critical success factors of an ERP implementation. To ensure that the training is conducted in the most effective and successful manner, the training outcomes should be evaluated. So far, training evaluation has however gained only limited attention at most companies investing in different training programs. Uponor corporation has initiated a large ERP implementation and process harmonization program in 2004. Thousands of end-users have been trained during this project so far, and the work still continues until the project is completed in 2010. In this thesis, the evaluation of end-user training in Uponor’s ERP program is brought further from the current state of performing the basic participant satisfaction survey in the end of each class. The results show that in order to reach reliable training effectiveness evaluation results, not only the reaction towards training but also transfer of skills and attitudes and the final results of the training program should be evaluated.

Diretrizes para a formação de professores da educação básica em interface com a licenciatura em química: em contexto as possibilidades formativas

We developed and applied an educational instrument (ID) to discuss, in the discipline of Supervised Pre Service Teachers Practice 1, guidelines for teacher education to redefine teacher knowledge with a specific focus on chemistry teachers. The study used methodological fundamentals of Discursive Textual Analysis that involves identifying and isolating a set of materials subjected to analysis, categorizing these statements and producing texts, integrating these descriptions and interpretations, basing its construction on the category system built. Data were collected through participant observation and text readings used by the students in performing the activity. It was noted that the chemistry teachers trained were able to express opinions that indicated the Construction of Meanings Interface that was the category of analysis established a priori.

Development of Calculation Procedures for Wastewater Treatment Technologies for a Training System

Efficient designs and operations of water and wastewater treatment systems are largely based on mathematical calculations. This even applies to training in the treatment systems. Therefore, it is necessary that calculation procedures are developed and computerised a priori for such applications to ensure effectiveness. This work was aimed at developing calculation procedures for gas stripping, depth filtration, ion exchange, chemical precipitation, and ozonation wastewater treatment technologies to include them in ED-WAVE, a portable computer based tool used in design, operations and training in wastewater treatment. The work involved a comprehensive online and offline study of research work and literature, and application of practical case studies to generate ED-WAVE compatible representations of the treatment technologies which were then uploaded into the tool.

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.