Computer Based Training Authors' and Designers' training. 'Formación asistida por ordenador para autores y diseñadores'.

Resumen basado en el de la publicación

Learner centred control in computer based training

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Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

NURSES' PERCEPTION REGARDING THE IMPLEMENTATION OF COMPUTER-BASED CLINICAL NURSING DOCUMENTATION

This qualitative, exploratory, descriptive study was performed with the objective of understanding the perception of the nurses working in medical-surgical units of a university hospital, regarding the strategies developed to perform a pilot test of the PROCEnf-USP electronic system, with the purpose of computerizing clinical nursing documentation. Eleven nurses of a theoretical-practical training program were interviewed and the obtained data were analyzed using the Content Analysis Technique. The following categories were discussed based on the references of participative management and planned changes: favorable aspects for the implementation; unfavorable aspects for the implementation; and expectations regarding the implementation. According to the nurses' perceptions, the preliminary use of the electronic system allowed them to show their potential and to propose improvements, encouraging them to become partners of the group manager in the dissemination to other nurses of the institution.

The development and evaluation of a computer-based program to test and to teach the recognition of facial affect

Autism is a chronic pervasive neurodevelopmental disorder characterized by the early onset of social and communicative impairments as well as restricted, ritualized, stereotypic behavior. The endophenotype of autism includes neuropsychological deficits, for instance a lack of "Theory of Mind" and problems recognizing facial affect. In this study, we report the development and evaluation of a computer-based program to teach and test the ability to identify basic facially expressed emotions. 10 adolescent or adult subjects with high-functioning autism or Asperger-syndrome were included in the investigation. A priori the facial affect recognition test had shown good psychometric properties in a normative sample (internal consistency: rtt=.91-.95; retest reliability: rtt=.89-.92). In a prepost design, one half of the sample was randomly assigned to receive computer treatment while the other half of the sample served as control group. The training was conducted for five weeks, consisting of two hours training a week. The trained individuals improved significantly on the affect recognition task, but not on any other measure. Results support the usefulness of the program to teach the detection of facial affect. However, the improvement found is limited to a circumscribed area of social-communicative function and generalization is not ensured.

Peptide design by artificial neural networks and computer-based evolutionary search

A technique for systematic peptide variation by a combination of rational and evolutionary approaches is presented. The design scheme consists of five consecutive steps: (i) identification of a “seed peptide” with a desired activity, (ii) generation of variants selected from a physicochemical space around the seed peptide, (iii) synthesis and testing of this biased library, (iv) modeling of a quantitative sequence-activity relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function. This strategy was successfully applied to the identification of novel peptides that fully prevent the positive chronotropic effect of anti-β1-adrenoreceptor autoantibodies from the serum of patients with dilated cardiomyopathy. The seed peptide, comprising 10 residues, was derived by epitope mapping from an extracellular loop of human β1-adrenoreceptor. A set of 90 peptides was synthesized and tested to provide training data for neural network development. De novo design revealed peptides with desired activities that do not match the seed peptide sequence. These results demonstrate that computer-based evolutionary searches can generate novel peptides with substantial biological activity.

The Conception of Creation of Integrated Development Environment for Computer Training Systems

The various questions of creation of integrated development environment for computer training systems are considered in the given paper. The information technologies that can be used for creation of the integrated development environment are described. The different didactic aspects of realization of such systems are analyzed. The ways to improve the efficiency and quality of learning process with computer training systems for distance education are pointed.

Classroom Simulation is Computer Based

The authors review and evaluate the use of a business simulation, specifically he Hotel Operational Training Simulation (HOTS), in the fourth year of a hospitality undergraduate program. Four dimensions were explored: learning experience, alternative method of instruction, critical and analytical thinking ability and delivery time frame, in addition to the student overall satisfaction with the learning experience.