Dynamic group formation in mobile computer supported collaborative learning environment

Forming suitable learning groups is one of the factors that determine the efficiency of collaborative learning activities. However, only a few studies were carried out to address this problem in the mobile learning environments. In this paper, we propose a new approach for an automatic, customized, and dynamic group formation in Mobile Computer Supported Collaborative Learning (MCSCL) contexts. The proposed solution is based on the combination of three types of grouping criteria: learner’s personal characteristics, learner’s behaviours, and context information. The instructors can freely select the type, the number, and the weight of grouping criteria, together with other settings such as the number, the size, and the type of learning groups (homogeneous or heterogeneous). Apart from a grouping mechanism, the proposed approach represents a flexible tool to control each learner, and to manage the learning processes from the beginning to the end of collaborative learning activities. In order to evaluate the quality of the implemented group formation algorithm, we compare its Average Intra-cluster Distance (AID) with the one of a random group formation method. The results show a higher effectiveness of the proposed algorithm in forming homogenous and heterogeneous groups compared to the random method.(undefined

Using students’ learning style to create effective learning groups in MCSCL environments

Students have different ways for learning and processing information. Some students prefer learning through seeing while others prefer learning through listening; some students prefer doing activities while other prefer reflecting.Some students reason logically, while others reason intuitively, etc. Identifying the learning style of each student, and providing learning content based on these styles represents a good method to enhance the learning quality. However, there are no efforts onhow to detect the students’ learning styles in mobile computer supported collaborative learning (MCSCL) environments. We present in this paper new ways for automatically detecting the learning styles of students in MCSCL environments based on the learning style model of Felder-Silverman. The identified learning styles of students could be then stored and used at anytime toassign each one of them to his/her appropriate learning group

PathologyBERT -- Pre-trained Vs. A New Transformer Language Model for Pathology Domain

Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions. However, successful text mining of a large pathology database can play a critical role to advance 'big data' cancer research like similarity-based treatment selection, case identification, prognostication, surveillance, clinical trial screening, risk stratification, and many others. While there is a growing interest in developing language models for more specific clinical domains, no pathology-specific language space exist to support the rapid data-mining development in pathology space. In literature, a few approaches fine-tuned general transformer models on specialized corpora while maintaining the original tokenizer, but in fields requiring specialized terminology, these models often fail to perform adequately. We propose PathologyBERT - a pre-trained masked language model which was trained on 347,173 histopathology specimen reports and publicly released in the Huggingface repository. Our comprehensive experiments demonstrate that pre-training of transformer model on pathology corpora yields performance improvements on Natural Language Understanding (NLU) and Breast Cancer Diagnose Classification when compared to nonspecific language models.Comment: submitted to "American Medical Informatics Association (AMIA)" 2022 Annual Symposiu

Análise do financiamento do BNDES no setor agropecuário brasileiro para o período de 2012 a 2015

O presente trabalho busca analisar como foi o comportamento da produtividade total dos fatores (PTF) de algumas empresas do setor agropecuário brasileiro que receberam crédito do BNDES, entre 2012 e 2015. Pretende-se, então, apurar se as empresas que receberam mais crédito são aquelas com o melhor desempenho produtivo para o setor agropecuário. Para isso utilizou-se a técnica Análise Envoltória de Dados (DEA), com o cálculo do Índice de Malmquist, a partir de uma amostra de 14 empresas do setor. Constatou-se que, na média, apenas quatro empresas apresentaram variações positivas de produtividade. O valor financiado pelo BNDES, pode ter tido impacto positivo sobre o desempenho das empresas do ponto de vista tecnológico, mas não foi suficiente para melhorar sua eficiência técnica. Além disso, foi possível verificar que as empresas que receberam mais crédito não foram, necessariamente, as que tiveram o melhor desempenho produtivo

EDUCAÇÃO AMBIENTAL: SCRATCH COMO FERRAMENTA PEDAGÓGICA NO ENSINO DE SANEAMENTO BÁSICO

A falta de cuidados com o saneamento básico pode acarretar diversos problemasambientais e de saúde. Entende-se, portanto, que é de fundamental importância que a conscientização sobre esses cuidados seja discutida desde a infância. Nesse contexto, o presente artigo analisa a contribuição de objetos de aprendizagem (OA), desenvolvidos no ambiente de programação Scratch, ao serem utilizados como ferramentas pedagógicas para o ensino de Saneamento Básico no 5ọ ano do Ensino Fundamental. Para tanto, foi realizado um estudo de caso em uma escola pública do município de Campos dos Goytacazes/RJ, utilizando os três OA produzidos. Como fundamentação teórica adotou-se a teoria vygotskyana. Para a avaliação da estratégia pedagógica adotada, foram utilizadas listas de exercícios de pré e pós-teste, observação e questionário. Os dados foram analisados e mostraram boa aceitação dos alunos aos OA e à proposta da aula

Influence of the volume of restorative material on the concentration of stresses in the restorative interface

To evaluate the microtensile strength in the adhesive interface depending on the volume of the composite resin used to restore class I cavities. Forty-eight human third molars received a standardized class I cavity preparation and they were separated i

High-resolution imaging of the cosmic mass distribution from gravitational lensing of pregalactic HI

Low-frequency radio observations of neutral hydrogen during and before the epoch of cosmic reionization will provide ~ 1000 quasi-independent source planes, each of precisely known redshift, if a resolution of ~1 arcminutes or better can be attained. These planes can be used to reconstruct the projected mass distribution of foreground material. Structure in these source planes is linear and gaussian at high redshift (30<z<300) but is nonlinear and nongaussian during re ionization. We demonstrate that this structure can, in principle, be used to make mass images with a formal signal-to-noise per pixel exceeding 10, even for pixels as small as an arc-second. With an ideal telescope, both resolution and signal-to-noise can exceed those of even the most optimistic idealized mass maps from galaxy lensing by more than an order of magnitude. Individual dark halos similar in mass to that of the Milky Way could be imaged with high signal-to-noise out to z ~ 10. Even with a much less ambitious telescope, a wide-area survey of 21 cm lensing would provide very sensitive constraints on cosmological parameters, in particular on dark energy. These are up to 20 times tighter than the constraints obtainable from comparably sized, very deep surveys of galaxy lensing, although the best constraints come from combining data of the two types. Any radio telescope capable of mapping the 21cm brightness temperature with good frequency resolution (~ 0.05 MHz) over a band of width >~ 10 MHz should be able to make mass maps of high quality. The planned Square Kilometer Array (SKA) may be able to map the mass with moderate signal-to-noise down to arcminute scales, depending on the reionization history of the universe and the ability to subtract foreground sources.Comment: revised and extended, more detailed predictions for observations, 25 pages, 11 figures, submitted to MNRA

Probing the accelerating Universe with radio weak lensing in the JVLA Sky Survey

We outline the prospects for performing pioneering radio weak gravitational lensing analyses using observations from a potential forthcoming JVLA Sky Survey program. A large-scale survey with the JVLA can offer interesting and unique opportunities for performing weak lensing studies in the radio band, a field which has until now been the preserve of optical telescopes. In particular, the JVLA has the capacity for large, deep radio surveys with relatively high angular resolution, which are the key characteristics required for a successful weak lensing study. We highlight the potential advantages and unique aspects of performing weak lensing in the radio band. In particular, the inclusion of continuum polarisation information can greatly reduce noise in weak lensing reconstructions and can also remove the effects of intrinsic galaxy alignments, the key astrophysical systematic effect that limits weak lensing at all wavelengths. We identify a VLASS "deep fields" program (total area ~10-20 square degs), to be conducted at L-band and with high-resolution (A-array configuration), as the optimal survey strategy from the point of view of weak lensing science. Such a survey will build on the unique strengths of the JVLA and will remain unsurpassed in terms of its combination of resolution and sensitivity until the advent of the Square Kilometre Array. We identify the best fields on the JVLA-accessible sky from the point of view of overlapping with existing deep optical and near infra-red data which will provide crucial redshift information and facilitate a host of additional compelling multi-wavelength science.Comment: Submitted in response to NRAO's recent call for community white papers on the VLA Sky Survey (VLASS

First-Principles Study of the Electronic and Magnetic Properties of Defects in Carbon Nanostructures

Understanding the magnetic properties of graphenic nanostructures is instrumental in future spintronics applications. These magnetic properties are known to depend crucially on the presence of defects. Here we review our recent theoretical studies using density functional calculations on two types of defects in carbon nanostructures: Substitutional doping with transition metals, and sp$^3$-type defects created by covalent functionalization with organic and inorganic molecules. We focus on such defects because they can be used to create and control magnetism in graphene-based materials. Our main results are summarized as follows: i)Substitutional metal impurities are fully understood using a model based on the hybridization between the $d$ states of the metal atom and the defect levels associated with an unreconstructed D$_{3h}$ carbon vacancy. We identify three different regimes, associated with the occupation of distinct hybridization levels, which determine the magnetic properties obtained with this type of doping; ii) A spin moment of 1.0 $\mu_B$ is always induced by chemical functionalization when a molecule chemisorbs on a graphene layer via a single C-C (or other weakly polar) covalent bond. The magnetic coupling between adsorbates shows a key dependence on the sublattice adsorption site. This effect is similar to that of H adsorption, however, with universal character; iii) The spin moment of substitutional metal impurities can be controlled using strain. In particular, we show that although Ni substitutionals are non-magnetic in flat and unstrained graphene, the magnetism of these defects can be activated by applying either uniaxial strain or curvature to the graphene layer. All these results provide key information about formation and control of defect-induced magnetism in graphene and related materials.Comment: 40 pages, 17 Figures, 62 References; Chapter 2 in Topological Modelling of Nanostructures and Extended Systems (2013) - Springer, edited by A. R. Ashrafi, F. Cataldo, A. Iranmanesh, and O. Or