Examination of the Aggression Levels of Physical Education and Sport School Students

Abstract

The purpose of this research is to determine the level of aggression of students in different departments of physical education and sports school and to compare the aggression levels according to some demographic characteristics. The universe of your research is to constitute students who study in different departments of Bozok University Physical Education and Sports School in 2017-2018 academic years. The sample of the research consists of a total of 300 students who are identified by random sampling technique among the students studying in different departments at Bozok University Physical Education and Sports School in 2017-2018 academic year. Survey method was used in the research. Information on the demographic characteristics of the students was obtained through the "personal information form". Information on how to determine the level of aggression of the students was obtained with the "Buss Perry Aggression Scale". The obtained data were transferred to the SPSS 18 software program for statistical analysis. Frequency analysis, percentage analysis, arithmetic mean, t test, Anova analysis and post hoc tests were used in analyzing the data. As a result of the analyzes; statistically significant differences were found between aggression levels of students according to age, education department and monthly expenditure variables (p <0.05). There were no statistically significant differences according to gender, shelter status, maternal employment status, mother education level, father education level and sibling number (p> 0.05).

Building a Knowledge Base for QA System by Linking Korean Vocabulary and Wikipedia

“…Zesch et al, (2007) developed a general purpose, high performance Java-based Wikipedia API to use Wikipedia as a lexical semantic resource in large-scale NLP tasks. DBpedia project (Lehmannm et al, 2015;Rebele et al, 2016;Ponzetto and Strube, 2013;Wang and Kim, 2017;Tezcan Kardas and Sadik, 2018;Vafa et al, 2018;Wadmany and Melamed, 2018;Wyatt et al, 2018;Yang et al, 2017;Yildirim, 2018) extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things.…”

“…The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. Yago (Rebele et al, 2016;Ponzetto and Strube, 2013;Wang and Kim, 2017;Tezcan Kardas and Sadik, 2018;Vafa et al, 2018;Wadmany and Melamed, 2018;Wyatt et al, 2018;Yang et al, 2017;Yildirim, 2018;Yildirim and Çoban, 2018) is a large knowledge base that is built automatically from Wikipedia, WordNet and GeoNames. This project combines information from Wikipedias in 10 different languages, thus giving the knowledge a multilingual dimension.…”

“…This project combines information from Wikipedias in 10 different languages, thus giving the knowledge a multilingual dimension. Wikitaxonomy (Ponzetto and Strube, 2013;Wang and Kim, 2017;Tezcan Kardas and Sadik, 2018;Vafa et al, 2018;Wadmany and Melamed, 2018;Wyatt et al, 2018;Yang et al, 2017;Yildirim, 2018;Yildirim and Çoban, 2018;Lee et al, 2017) is a taxonomy automatically generated from the system of categories in Wikipedia. Categories in the resource are identified as either classes or instances and included in a large subsumption.…”

“…The workbench is used in various studies to build knowledge base (Vafa et al, 2018;Wadmany and Melamed, 2018;Wyatt et al, 2018). Rybina (Vafa et al, 2018;Wadmany and Melamed, 2018;Wyatt et al, 2018;Yang et al, 2017;Yildirim, 2018;Yildirim and Çoban, 2018;Lee et al, 2017;Rybina et al, 2017) suggested knowledge acquisition processes that use technologic knowledge base of intelligent planner of AT-TECHNOLOGY workbench and special program tools. This work is focused on models and methods of distributed knowledge acquisition from databases as additional knowledge sources and automation of the process via intelligent program environment.…”

“…This work is focused on models and methods of distributed knowledge acquisition from databases as additional knowledge sources and automation of the process via intelligent program environment. Choi (Wadmany and Melamed, 2018;Wyatt et al, 2018;Yang et al, 2017;Yildirim, 2018;Yildirim and Çoban, 2018;Lee et al, 2017;Rybina et al, 2017; suggested SINDI-WALKS, an integrated workbench that can extract and systematically manage technical knowledge inherent in scientific and technical literature such as academic papers and patents. SINDI-WALKS basically includes a technology knowledge extraction engine that identifies the PLOT, ie, names, names, institutions, and technical terms in text and extracts semantic relationships between them, and a testbed function for monitoring and error analysis of these engines.…”