Consideraciones para la implementación de un modelo de educación virtual: Revisión de áreas estratégicas

Recibido 07 febrero de 2011 • Aceptado 09 de marzo de 2011 • Corregido 23 de agosto de 2011   En este ensayo, a partir de una revisión bibliográfica con respecto a las áreas medulares de un modelo de educación virtual, se proponen algunas orientaciones dirigidas a aquellas personas interesadas en implementar una oferta académica virtual o bimodal, en la Universidad Nacional de Costa Rica. Se razona en función de siete áreas estratégicas sobre las cuales se debe sustentar un modelo de educación virtual: la filosófica; la académica; la profesoral; la estudiantil; la técnica; la legal/jurídica y, la financiera, geográfica y de dirección. Para cada área se presenta: a) un resumen de las tendencias de las mejores prácticas de educación virtual, b) recomendaciones de los expertos para realizar una gestión efectiva de carreras universitarias virtuales o bimodales, c) una breve reseña de las experiencias de la UNA en ese campo. Finalmente, se brindan, para cada área estratégica, algunas sugerencias que la UNA debiera considerar para continuar la ruta que asegure el éxito y la continuidad de sus carreras en modalidades virtuales y bimodales.

Reconsidering the role of recorded audio as a rich, flexible and engaging learning space

Audio needs to be recognised as an integral medium capable of extending education’s formal and informal, virtual and physical learning spaces. This paper reconsiders the value of educational podcasting through a review of literature and a module case study. It argues that a pedagogical understanding is needed and challenges technology-centred or teacher-centred understandings of podcasting. It considers the diverse methods being used that enhance and redefine podcasting as a medium for student-centred active learning. The case study shows how audio created a rich learning space by meaningfully connecting tutors, students and those beyond the existing formal study space. The approaches used can be categorised as new types of learning activity, extended connected activity, relocated activity, and recorded ‘captured’ activity which promote learner replay and re-engagement. The paper concludes that the educational use of the recorded voice needs to be reconsidered and reconceptualised so that audio is valued as a manageable, immediate, flexible, potent and engaging medium.

Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses

With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community. The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner. Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services.

Depth Estimation in Stereo Biomedical Images via Proxy-Supervised Deep Learning

In order to estimate depth through supervised deep learning-based stereo methods, it is necessary to have access to precise ground truth depth data. While the gathering of precise labels is commonly tackled by deploying depth sensors, this is not always a viable solution. For instance, in many applications in the biomedical domain, the choice of sensors capable of sensing depth at small distances with high precision on difficult surfaces (that present non-Lambertian properties) is very limited. It is therefore necessary to find alternative techniques to gather ground truth data without having to rely on external sensors. In this thesis, two different approaches have been tested to produce supervision data for biomedical images. The first aims to obtain input stereo image pairs and disparities through simulation in a virtual environment, while the second relies on a non-learned disparity estimation algorithm in order to produce noisy disparities, which are then filtered by means of hand-crafted confidence measures to create noisy labels for a subset of pixels. Among the two, the second approach, which is referred in literature as proxy-labeling, has shown the best results and has even outperformed the non-learned disparity estimation algorithm used for supervision.

ICE Emissions Estimation through Machine-learning techniques to support real-world R&D experiments

The emissions estimation, both during homologation and standard driving, is one of the new challenges that automotive industries have to face. The new European and American regulation will allow a lower and lower quantity of Carbon Monoxide emission and will require that all the vehicles have to be able to monitor their own pollutants production. Since numerical models are too computationally expensive and approximated, new solutions based on Machine Learning are replacing standard techniques. In this project we considered a real V12 Internal Combustion Engine to propose a novel approach pushing Random Forests to generate meaningful prediction also in extreme cases (extrapolation, very high frequency peaks, noisy instrumentation etc.). The present work proposes also a data preprocessing pipeline for strongly unbalanced datasets and a reinterpretation of the regression problem as a classification problem in a logarithmic quantized domain. Results have been evaluated for two different models representing a pure interpolation scenario (more standard) and an extrapolation scenario, to test the out of bounds robustness of the model. The employed metrics take into account different aspects which can affect the homologation procedure, so the final analysis will focus on combining all the specific performances together to obtain the overall conclusions.

HBIM to Virtual Reality Workflow for Industrial Archaeology Immovable Assets’ Management: A Case Study

As a witness on the industrialization in Bologna, since its first generation was born in the late 1760, the Battiferro lock has been coping with the innovation that the city experienced throughout the centuries, until it has lost its functionality due to the technological development for which Bologna’s canals were gradually covered starting from the 1950s under Giuseppe Dozza ’s administration, as part of the reconstruction, reclamation and urban requalification that was carried out in the aftermath the World War II and which involved the whole city. The interest of the research carried out on this case study was primarily to reintroduce the landmark that is still intact, to what is considered to be the fourth generation of the industrial revolution, namely in the construction field, which is recognized as Construction 4.0, by means of the Historic (or Heritage) Information Modeling HBIM and Virtual Reality (VR) application. A scan-to-BIM approach was followed to create 3D as-built BIM model, as a first step towards the storytelling of the abandoned industrial built asset in VR environment, or as a seed for future applications such as Digital Twins (DT), heritage digital learning, sustainable impact studies, and/or interface with other interfaces such as GIS. Based on the HBIM product, examples of the primary BIM deliverables such as 2D layouts is given, then a workflow to VR is proposed and investigated the reliability of data and the type of users that may benefit of the VR experience, then the potential future development of the model is investigated, with comparison of a relatively similar experience in the UK.

Avoiding Pitfalls Of Intraoperative Peripheral Endovascular Surgery With The Aid Of Osirix: Expanding The Use Of Virtual Fluoroscopy.

We have shown how the analysis of the angiotomography reconstruction through OsiriX program has assisted in endovascular perioperative programming. We presented its application in situations when an unexpected existence of metallic overlapping artifact (orthopedic osteosynthesis) compromised the adequate visualization of the arterial lesion during the procedure. Through manipulation upon OsiriX software, with assistance of preview under virtual fluoroscopy, it was possible to obtain the angles that would avoid this juxtaposition. These angles were reproduced in the C-arm, allowing visualization of the occluded segment, reducing the need for repeated image acquisitions and contrast overload, allowing the continuation of the procedure.

Ecological Literacy And Beyond: Problem-based Learning For Future Professionals.

Ecological science contributes to solving a broad range of environmental problems. However, lack of ecological literacy in practice often limits application of this knowledge. In this paper, we highlight a critical but often overlooked demand on ecological literacy: to enable professionals of various careers to apply scientific knowledge when faced with environmental problems. Current university courses on ecology often fail to persuade students that ecological science provides important tools for environmental problem solving. We propose problem-based learning to improve the understanding of ecological science and its usefulness for real-world environmental issues that professionals in careers as diverse as engineering, public health, architecture, social sciences, or management will address. Courses should set clear learning objectives for cognitive skills they expect students to acquire. Thus, professionals in different fields will be enabled to improve environmental decision-making processes and to participate effectively in multidisciplinary work groups charged with tackling environmental issues.

Delivery Of A Urology Online Course Using Moodle Versus Didactic Lectures Methods.

To subjectively and objectively compare an accessible interactive electronic library using Moodle with lectures for urology teaching of medical students. Forty consecutive fourth-year medical students and one urology teacher were exposed to two teaching methods (4 weeks each) in the form of problem-based learning: - lectures and - student-centered group discussion based on Moodle (modular object-oriented dynamic learning environment) full time online delivered (24/7) with video surgeries, electronic urology cases and additional basic principles of the disease process. All 40 students completed the study. While 30% were moderately dissatisfied with their current knowledge base, online learning course delivery using Moodle was considered superior to the lectures by 86% of the students. The study found the following observations: (1) the increment in learning grades ranged from 7.0 to 9.7 for students in the online Moodle course compared to 4.0-9.6 to didactic lectures; (2) the self-reported student involvement in the online course was characterized as large by over 60%; (3) the teacher-student interaction was described as very frequent (50%) and moderately frequent (50%); and (4) more inquiries and requisitions by students as well as peer assisting were observed from the students using the Moodle platform. The Moodle platform is feasible and effective, enthusing medical students to learn, improving immersion in the urology clinical rotation and encouraging the spontaneous peer assisted learning. Future studies should expand objective evaluations of knowledge acquisition and retention.

Flicker perimetry in healthy subjects: influence of age and gender, learning effect and short-term fluctuation

PURPOSE: To determine the mean critical fusion frequency and the short-term fluctuation, to analyze the influence of age, gender, and the learning effect in healthy subjects undergoing flicker perimetry. METHODS: Study 1 - 95 healthy subjects underwent flicker perimetry once in one eye. Mean critical fusion frequency values were compared between genders, and the influence of age was evaluated using linear regression analysis. Study 2 - 20 healthy subjects underwent flicker perimetry 5 times in one eye. The first 3 sessions were separated by an interval of 1 to 30 days, whereas the last 3 sessions were performed within the same day. The first 3 sessions were used to investigate the presence of a learning effect, whereas the last 3 tests were used to calculate short-term fluctuation. RESULTS: Study 1 - Linear regression analysis demonstrated that mean global, foveal, central, and critical fusion frequency per quadrant significantly decreased with age (p<0.05).There were no statistically significant differences in mean critical fusion frequency values between males and females (p>0.05), with the exception of the central area and inferonasal quadrant (p=0.049 and p=0.011, respectively), where the values were lower in females. Study 2 - Mean global (p=0.014), central (p=0.008), and peripheral (p=0.03) critical fusion frequency were significantly lower in the first session compared to the second and third sessions. The mean global short-term fluctuation was 5.06±1.13 Hz, the mean interindividual and intraindividual variabilities were 11.2±2.8% and 6.4±1.5%, respectively. CONCLUSION: This study suggests that, in healthy subjects, critical fusion frequency decreases with age, that flicker perimetry is associated with a learning effect, and that a moderately high short-term fluctuation is expected.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Pedagogia do esporte : pressupostos para uma teoria da avaliação da aprendizagem = Sport pedagogy: presuppositions for a learning assessment theory

Universidade Estadual de Campinas . Faculdade de Educação Física

Centro esportivo virtual : um recurso de informação em educação fisica e esportes na internet

Universidade Estadual de Campinas . Faculdade de Educação Física

Learning processes and the neural analysis of conditioning

Classical and operant conditioning principles, such as the behavioral discrepancy-derived assumption that reinforcement always selects antecedent stimulus and response relations, have been studied at the neural level, mainly by observing the strengthening of neuronal responses or synaptic connections. A review of the literature on the neural basis of behavior provided extensive scientific data that indicate a synthesis between the two conditioning processes based mainly on stimulus control in learning tasks. The resulting analysis revealed the following aspects. Dopamine acts as a behavioral discrepancy signal in the midbrain pathway of positive reinforcement, leading toward the nucleus accumbens. Dopamine modulates both types of conditioning in the Aplysia mollusk and in mammals. In vivo and in vitro mollusk preparations show convergence of both types of conditioning in the same motor neuron. Frontal cortical neurons are involved in behavioral discrimination in reversal and extinction procedures, and these neurons preferentially deliver glutamate through conditioned stimulus or discriminative stimulus pathways. Discriminative neural responses can reliably precede operant movements and can also be common to stimuli that share complex symbolic relations. The present article discusses convergent and divergent points between conditioning paradigms at the neural level of analysis to advance our knowledge on reinforcement.