Cooperative Approaches and Academic Motivation towards Enhancing Pre-Service Teachers’ Achievement

This paper describes a quantitative study that explores both the degree of preservice teachers’ (PSTs) motivation and achievement, and the dimensions of need-supportive teaching, when PSTs were involved in designing and implementing contextualized physical cooperative challenges (CPCCs) in primary schools. The analysis was based on the PSTs’ perceptions of the dimensions of need-supportive teaching (namely autonomy support, structure, and involvement), and the dimensions of motivation. Need-supportive teaching was evaluated through a version of the Teacher as a Social Context Questionnaire (TASC-Q), and motivation through a SMOTIV motivation questionnaire. Results showed that the dimensions of the teaching process were all positively correlated, thus, implying the importance of a teacher’s role in supporting autonomy, providing structure, and being involved. While motivation during the in-class definition of the CPCCs was correlated with the teachers’ involvement, the out-of-class implementation of the CPCCs was found to correlate with the three dimensions of teaching, thus, implying that the PST students’ perceptions of developing instructional approaches in schools is mediated by the role their university teachers play in defining and structuring all aspects of the teaching process. Students’ achievement, on the other hand, was a process that relied on the synergies between the teachers’ involvement and the PST student motivation during the stage when the CPCCs in the classroom environment were defined. This interlink responds to the constructivist position adopted to implement a cooperative approach in the university that, in turn, responds to assessing a student-centered cooperative-based learning approach.

ASSEMBLING AN IMAGE AND POINT CLOUD DATASET FOR HERITAGE BUILDING SEMANTIC SEGMENTATION

Creating three-dimensional as-built models from point clouds is still a challenging task in the Cultural Heritage environment. Nowadays, performing such task typically requires the quite time-consuming manual intervention of an expert operator, in particular to deal with the complexities and peculiarities of heritage buildings. Motivated by these considerations, the development of automatic or semi-automatic tools to ease the completion of such task has recently became a very hot topic in the research community. Among the tools that can be considered to such aim, the use of deep learning methods for the semantic segmentation and classification of 2D and 3D data seems to be one of the most promising approaches. Indeed, these kinds of methods have already been successfully applied in several applications enabling scene understanding and comprehension, and, in particular, to ease the process of geometrical and informative model creation. Nevertheless, their use in the specific case of heritage buildings is still quite limited, and the already published results not completely satisfactory. The quite limited availability of dedicated benchmarks for the considered task in the heritage context can also be one of the factors for the not so satisfying results in the literature.Hence, this paper aims at partially reducing the issues related to the limited availability of benchmarks in the heritage context by presenting a new dataset for semantic segmentation of heritage buildings. The dataset is composed by both images and point clouds of the considered buildings, in order to enable the implementation, validation and comparison of both point-based and multiview-based semantic segmentation approaches. Ground truth segmentation is provided, for both the images and point clouds related to each building, according to the class definition used in the ARCHdataset, hence potentially enabling also the integration and comparison of the results obtained on such dataset.

Evaluación de parámetros de segmentación en OBIA para la clasificación de coberturas del suelo a partir de imágenes VANT

<p>Unmanned Aerial Vehicles (UAVs) have given a new boost to remote sensing and image classification techniques due to the high level of detail among other factors. Object-based image analysis (OBIA) could improve classification accuracy unlike to pixel-based, especially in high-resolution images. OBIA application for image classification consists of three stages i.e., segmentation, class definition and training polygons, and classification. However, defining the parameters: spatial radius (SR), range radius (RR) and minimum region size (MR) is necessary during the segmentation stage. Despite their relevance, they are usually visually adjusted, which leads to a subjective interpretation. Therefore, it is of utmost importance to generate knowledge focused on evaluating combinations of these parameters. This study describes the use of the mean-shift segmentation algorithm followed by <em>Random Forest </em>classifier using Orfeo Toolbox software. It was considered a multispectral orthomosaic derived from UAV to generate a suburban map land cover in town of El Pueblito, Durango, Mexico. The main aim was to evaluate efficiency and segmentation quality of nine parameter combinations previously reported in scientific studies.This in terms of number generated polygons, processing time, discrepancy measures for segmentation and classification accuracy metrics. Results evidenced the importance of calibrating the input parameters in the segmentation algorithms. Best combination was RE=5, RR=7 and TMR=250, with a Kappa index of 0.90 and shortest processing time. On the other hand, RR showed a strong and inversely proportional degree of association regarding the classification accuracy metrics.</p>

A New Dimension of the Social Structure of the Cognitive Society «Knowledge-class» and its Class Definiteness

The article outlines the principles of class definition of the cognitive society’s social structure element called «Knowledge-class» and reveals the features of the indicated phenomenon. Showing the specificity of the new dimension of the cognitive society’s social structure, the author proceeds from the fact that this specificity is organically linked to the formation of new social resources, such as knowledge and information, the ability to manage knowledge and information, and the ability to control. The logic of the «Knowledge-class» concept formation (R. Dahrendorf, R. Florida, P. Drucker, F. Machlup) is revealed and the properties and characteristics of a new element of the cognitive society’s social structure are indicated

A DEFINITION OF VISUAL INTERPRETATION CRITERIAS TO MAPPING LAND-USE AND LAND-COVER IN THE BRAZILIAN BIOMES

The present work aims to establish of Visual Interpretation Criterias of the land-use and land-cover (LULC) classes of the Brazilian biomes. The process relies on the efforts of experts from each biome, Ph.D. and Master's students, and undergraduate students in research. Due to the particularities, the criterias were elaborated individually for each biome. The classes correspond to MapBiomas collection 04 legend. In each LULC class, the user has the following information: class definition, patterns (e.g., color, texture, roughness), and historical Landsat images (RGB 564) from the dry and rainy periods, as well as high-resolution images and field photos of the class. These visual interpretation criterias was used to generate data of samples for MapBiomas mapping validation. With the help of Visual Interpretation Criterias, experienced and inexperienced interpreters were able to produce high-quality sample data without visual inspection. This initiative, a pioneer in Brazil, is a tool to support future interpretations of Brazilian biomes. The results can be found on Lapig website.

Drivers of farm performance in Czech crop farms

When analysing drivers affecting the farm performance, the presence of different technologies should be taken into account. We assume that the technology used by crop farms is not the same for all producers and therefore we use latent class model to identify technological classes at first. Class definition is based on multidimensional classification and determination of indices given by the values of individual components. The principal components analysis is applied to estimate significant and robust weights for the index components. FADN (Farm Accountancy Data Network) database, Czech crop farms data from 2005 to 2017 were used and three groups of technology classes of farms were identified with a determinant influence of the structure index and localisation. The other indices characterise sustainability, innovation, technology, diversification, and individual characteristics. Three distinct classes of crop farms were found, one major class and two minor classes. Family driven farms are usually smaller farms in terms of acreage. Highly sustainable crop farms are most likely located in lower altitudes and not in less-favoured areas. Innovative farms are also likely to be more productive. The results indicate that agricultural production farms with a more sustainable way of farming are most likely to be more productive.

Research on the internal influence factors of the text multi-classification problem

This paper mainly deals with the classification of text type data. The statistics show that more than 8000 articles have been reached in all kinds of documents retrieved by the optical network. However, there are few papers on the factors that affect the classification of text. The text classification method used is important, but the internal factors sometimes play a great role, and even affect the success or failure of the whole text classification. In order to make up for this deficiency, this paper selects the Rocchio algorithm as the classification method, mainly from the category clustering density, class complexity, category definition, stop words and document’s length five internal factors, we tested their influences on text classification by the experiment. Experiment shows that the clustering density is higher and the complexity of the lower class, class definition is higher, the higher the accuracy of text classification, text classification effect is better, and better effect to text stop words, the length of the text does not directly affect the effect of text classification, but according to the text classification algorithm is more suitable to choose the length of the document.

ANALISIS PELAKSANAAN PEMBELAJARAN KIMIA DI KELAS HOMOGEN (Studi Kasus Pembelajaran Kimia di SMA Negeri 11 Banda Aceh)

Conceptual understanding of chemistry requires the ability to represent and interpret the problems of chemicals in a form that is easy to understand. One way that allows students to learn chemistry is to implement effective learning activities. This research was conducted in SMA Negeri 11 Banda Aceh. One of the schools with a homogeneous class management systems based on the same gender. The purpose of this study was to determine the learning process for the chemical held in a homogeneous class. Definition of homogeneous classes of this research is the class that is populated by students collectively have the same gender. Observations obtained is the percentage of teachers' activities with a percentage of 71.42% of activities, which means that activities of teachers in the classroom in both categories (51% - 80%). Results of student feedback through a questionnaire, obtained only 32% of students from a total of 88 students who find it easy to understand the chemistry lesson taught over the years, while the remaining amount to 68% of students still find it difficult to understand the chemistry lesson. Chemistry learning in homogeneous classes requires readiness of teachers, both in the preparation of learning tools and also the ability of teachers to manage the classroom, so that learning can be effective.

On Errors in Terminology on Theory of Surfaces And Geometric Modelling

At present, a great amount of scientific papers, monographs, and reference books dealing with analytical and differential geometry of surfaces have been published. They contain materials for following geometric investigations, for implementation of received earlier geometrical results into architecture, building, and machinery manufacturing. In the paper it has been shown on the specific examples that sometimes the results of geometric investigations for shells’ middle surfaces taken in published references for the following application without check could lead to serious errors because of ones in the surfaces equations or inexactitudes in a surfaces class definition. At present, 38 classes of surfaces, uniting more than 600 ones that have their own names and are described in scientific publications, are known. The author has worked up a great number of researches and found errors, inaccuracies, and alternative versions in monographs and scientific papers, related to questions on geometry of developable surfaces (conic and torse surfaces), surfaces of rev olution (paraboloid and ellipsoid of revolution, nodoid), minimal surfaces (catenoids), conoids, and cyclic surfaces including the canal ones. In actual practice there are much more geometric errors, but in this paper are discussed only well-known geometricians and architects’ works, as well as in this paper there is no information on surfaces that are presented at specialized sites in Internet. Here are encountered misreckoned coefficients for surfaces’ fundamental quadratic forms, there are errors in the formulae for the quadratic forms’ coefficients determination, as well as in the formulae for the calculation a surface element’s area, surface’s principle curvatures, and so on. All of encountered errors have been divided into four groups. The fourth group’s errors named as “typographical errors and authors’ slips of the pen” have been considered fragmentarily because they are encountered the most frequently, and can be corrected by the authors themselves in the following papers.