Development and Validation of Classroom Problem Behavior Scale - Elementary School Version(CPBS-E) (초등학생 문제행동선별척도: 교사용(CPBS-E)의 개발과 타당화)
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- Korean Journal of School Psychology
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- v.16 no.3
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- pp.433-451
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- 2019
This study aimed to develop and validate the Classroom Problem Behavior Scale - Elementary School Version (CPBS-E) measure which is unique to classroom problem behavior exhibited by Korean elementary school students. The focus was on developing a universal screening instrument designed to identify and provide intervention to students who are at-risk for severe social-emotional and behavioral problems. Items were initially drawn from the literature, interviews with elementary school teachers, common office discipline referral measures used in U.S. elementary schools, penalty point systems used in Korean schools, 'Green Mileage', and the Inventory of Emotional and Behavioral Traits. The content validity of the initially developed items was assessed by six classroom and subject teachers, which resulted in the development of a preliminary scale consisting of 63 two-dimensional items (i.e., Within Classroom Problem Behavior and Outside of Classroom Problem Behavior), each of which consisted of 3 to 4 factors. The Within Classroom Problem Behavior dimension consisted of 4 subscales (not being prepared for class, class disruption, aggression, and withdrawn) and the Outside of Classroom Problem Behavior dimension consisted of 3 subscales (rule-violation, aggression, and withdrawn). The CPBS-E was pilot tested on a sample of 154 elementary school students, which resulted in reducing the scale to 23 items. Following the scale revision, the CPBS-E was validated on a sample population of 209 elementary school students. The validation results indicated that the two-dimensional CPBS-E scale of classroom problem behavior was a reliable and valid measure. The test-retest reliability was stable at above .80 in most of the subscales. The CPBS-E measure demonstrated high internal consistency of .76-.94. In examining the criterion validity, the scale's correlation with the Teacher Observation of Classroom Adaptation-Checklist (TOCA-C) was high and the aggression and withdrawn subscales of the CPBS-E demonstrated high correlations with externalization and internalization, respectively, of the Child Behavior Checklist - Teacher Report Form CBCL-TRF). In addition, the factor structure of the CPBS-E scale was examined using the structural equation model and found to be acceptable. The results are discussed in relation to implications, contributions to the field, and limitations.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (