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  • Author: Dorota Jankowska x
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Educating Educators and Teachers in Poland under Conditions of Neo-Liberal Culture of Consumption

Abstract

The presented text discusses the problems of academic and teacher education under conditions of consumption culture and neoliberal ideology development at the beginning of the 21st century in Poland. The article puts the thesis that neo-liberalism, manifested by economic phenomena, permeates all spheres of social life, enhancing the characteristics of consumption culture. It enters education, including academic education. In this context, there has been shown a part of our research conducted in 2006-2016 (27 interviews in depth with students completing Master’s degree in Pedagogy). The research has taken the form of semantic reconstruction of statements made on studies and pedagogical training, expressed by students during in-depth interviews (IDI). During the research study the leading categories have been defined, which set the main perspectives for thinking about study. Within these perspectives, the student’s goals of study have been identified and more detailed profiles of perceiving pedagogical studies have been defined. The comparative analysis of the IDI narratives of 2006 and 2016 allows us to see phenomena that can be interpreted as indicating that pedagogy students are increasingly taking over the market thinking, acting more explicitly as customers of educational services, presenting a utilitarian attitude to study.

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Prediction of Infertility Treatment Outcomes Using Classification Trees

Abstract

Infertility is currently a common problem with causes that are often unexplained, which complicates treatment. In many cases, the use of ART methods provides the only possibility of getting pregnant. Analysis of this type of data is very complex. More and more often, data mining methods or artificial intelligence techniques are appropriate for solving such problems. In this study, classification trees were used for analysis. This resulted in obtaining a group of patients characterized most likely to get pregnant while using in vitro fertilization.

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Analyzing Outcomes of Intrauterine Insemination Treatment by Application of Cluster Analysis or Kohonen Neural Networks

Abstract

Intrauterine insemination (IUI) is one of many treatments provided to infertility patients. Many factors such as, but not limited to, quality of semen, the age of a woman, and reproductive hormone levels contribute to infertility. Therefore, the aim of our study is to establish a statistical probability concerning the prediction of which groups of patients have a very good or poor prognosis for pregnancy after IUI insemination. For that purpose, we compare the results of two analyses: Cluster Analysis and Kohonen Neural Networks. The k-means algorithm from the clustering methods was the best to use for selecting patients with a good prognosis but the Kohonen Neural Networks was better for selecting groups of patients with the lowest chances for pregnancy.

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Significance of Discriminant Analysis in Prediction of Pregnancy in IVF Treatment

Abstract

Many factors play an important role in prediction of infertility treatment outcome (for example, female age and quality of oocytes or embryos are the most important prognostic factors concerning positive IVF outcome). The purpose of this study was to identify a set of variables that could fulfill criteria for prediction of pregnancy in IVF patients through the application of data mining – using the discriminant analysis method. The principle of this method is to establish a set of rules that allows one to place multi-dimensional objects into one of two analyzed groups (pregnant or not pregnant). Six hundred and ten IVF cycles were included in the analysis and the following variables were taken into consideration: female age, number and quality of retrieved oocytes, number and quality of embryos, number of transferred embryos, and outcome of treatment. Discriminant analysis allowed for the creation of a model with a 51.22% correctness of prediction to achieve pregnancy during IVF treatment and with 74.07% correctly predicted failure of pregnancy. Therefore, the created model is more suitable for the prediction of a negative outcome (lack of pregnancy) during IVF treatment and offers an option for adjustments to be made during infertility treatment.

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The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome

Abstract

Principal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the mainte- nance of as much information as possible, uncovering the structure of the data, its visualization as well as classification of the objects within the space defined by the newly created components. PCA is very often used as a preliminary step in data preparation through the creation of independent components for further analysis. We used the PCA method as a first step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to create models that predict pregnancy. Therefore, 805 different types of IVF cy- cles were analyzed and pregnancy was correctly classified in 61-80% of cases for different analyzed groups in obtained models.

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The Use of Log-linear Analysis for Pregnancy Prediction

Abstract

Log-linear analysis is a practical tool for examining relationships, successfully applied in many fields of science. This paper discusses the topic of estimation of the chance of getting pregnant in couples that underwent ART insemination. The authors focus on finding significant interactions between variables, on the basis of which statistical models are built. With the use of results of log-linear analysis, a model predicting the chances of achieving a clinical pregnancy that contained interactions was successfully built. Moreover, it was more complete than the model obtained with the use of logistic regression alone.

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Application of Artificial Neural Networks and Principal Component Analysis to Predict Results of Infertility Treatment Using the IVF Method

Abstract

There are high hopes for using the artificial neural networks (ANN) technique to predict results of infertility treatment using the in vitro fertilization (IVF) method. Some reports show superiority of the ANN approach over conventional methods. However, fully satisfactory results have not yet been achieved. Hence, there is a need to continue searching for new data describing the treatment process, as well as for new methods of extracting information from these data. There are also some reports that the use of principal component analysis (PCA) before the process of training the neural network can further improve the efficiency of generated models. The aim of the study herein presented was to verify the thesis that the use of PCA increases the effectiveness of the prediction by ANN for the analysis of results of IVF treatment. Results for the PCA-ANN approach proved to be slightly better than the ANN approach, however the obtained differences were not statistically significant.

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New Technologies as a Tool for Changing Academic Communities in the Global Context

Abstract

It has been defined that knowledge society emerges at the end of the twentieth century as the socio-economic structure characteristic for developed societies in which unlike in industrial societies, the dominant sector of economy is services and the largest social group is the “men of knowledge”. It has been indicated that the development of the knowledge society is considered in connection with scientific and technological development and the expansion of new technologies as they determined is that we live in a globalized world, connected by a dense network of connections and intensive contacts both between the individuals and the authorities at local, regional, national and transnational or institutions level or between the institutions of various types, among which, of course, there are also educational institutions, including universities. It has been concluded that the accentuated thought should be read as a contemporary call for the implementation of the classic academic ideas in the conditions of the use of new technologies. Paraphrasing a metaphor of J. Morbitzer, modern university institutions should become intellectual arcs that will save humanity from the information deluge, the flood of endless, murky waves to safely bring it to the port of the society in which knowledge will be a resource for creative and innovative activities. A very important task is in the situation of current unprecedented acceleration of technical and technological progress not to lose the deep humanistic character of the educational process in which the modern technology should well serve a man and not contribute to its enslavement.

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What can psycholinguistic research on word class ambiguities tell us about categories?

Abstract

This paper is a contribution to a long-standing debate between constructionist, lexicalist, and emergentist schools of thought related to the question of what determines the category of lexically ambiguous words whose meanings belong to different syntactic categories (e.g., duck, walk). In the lexicalist view part-of-speech information is stored in the mental lexicon. According to the syntax-first (or constructionist) view, the ambiguous word is assigned to the syntactic category NOUN or VERB solely on the basis of the morphosyntactic frame in which it occurs irrespective of its meaning. In contrast, the emergentist view assumes an interaction of many constraints (semantic and syntactic) whereby semantic constraints are weaker than syntactic constraints in the resolution of word class ambiguities because while semantic context only favors one of the meanings of ambiguous words but does not exclude the competitors, syntactic context supports one meaning of an ambiguous word by ruling out its alternative interpretation. We intend to provide an overview of recent psycholinguistic studies focusing on the processing of word-class ambiguities in order to show that the syntax-first approach is too restrictive while the emergentist view is too permissive. What seems to be at issue is that when grammatical category-ambiguous words are processed, it is not that all constraints are available at the same time and they compete but rather different sources of information can be predicted to affect the process of lexical disambiguation at different stages during processing.

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Potential Mechanisms Underlying the Impact of Imaginative Play on Socio-Emotional Development in Childhood

Abstract

The aim of this paper is to weigh the empirical and hypothetical evidence to assess the claim that imaginative play supports the acquisition and development of social and emotional competence. We analyse children’s play and social skills using a development-based perspective. On this basis, we describe the developmental trajectories of imaginative play and the components of socio-emotional competence during childhood, especially in the pre-school period. In addition, we review the research literature on the possible link between imaginative play and creativity in children, and on how this type of play is predictive of later life creativity. Finally, we discuss hypothetical mechanisms that may account for the relationship between imaginative play and social competence in the preschool years and beyond.

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