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VOCATIONAL SCHOOLS PROVIDING SPECIAL TASKS COMPARATIVE ANALYSIS OF ITS DOCUMENTS IN REGIONS WITH DIFFERENT ECONOMIC STATUS
19-31Views:147Vocational schools providing special tasks comparative analysis of its documents in regions with different economic status. The study deals with the examination of dropout reduction strategies of vocational schools in the regions of Northern Hungary, Northern Great Plains and Western Hungary. We are looking for the answer to what pedagogical methods are used by vocational schools and the teachers who teach there to reduce dropout. Our research was a document analysis, which included an overview of the pedagogical/professional programs of vocational schools based on specific criteria. We are looking for an answer to the question, how does disadvantage compensation appear in the pedagogical and professional programs of vocational schools.
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THE SOCIO-DEMOGRAPHIC CHARACTERISTICS AND ACADEMIC PREPAREDNESS OF STEM STUDENTS IN HUNGARY
73-86Views:153Although the dropout behavior and labor market opportunities of STEM (Science, Technology, Engineering, and Mathematics) students and the masculinization of STEM fields are all well-researched topics in STEM research, there is a very limited body of literature focusing on the social background and academic preparedness of STEM applicants. Thus, in this research, we compared STEM and non-STEM students based on their type of settlement, type of secondary school program, the rate of students coming from a disadvantaged background, extra points given for academic accomplishments, and total application score. To identify variables that significantly predict getting admitted to a STEM field, we conducted binary logistic regression. During our research, we conducted the analysis using the 2017 Hungarian Admission Database. Our sample consisted of those who got admitted to a full-time BA/BSc or undivided course (N = 41324). According to our results, STEM students cannot be identified as a disadvantaged group either in terms of their social background or their lack of academic preparedness. According to the results of the binary logistic regression, the main predictors of getting admitted to a STEM field are gender (male), having a language certificate, and having a vocational training certificate. The main goal of our research was to explore whether the individual characteristics of STEM students can be the reason behind the high attrition rates specific to STEM fields. Since our results did not support this conclusion, we suppose to further investigate the role of institutional variables (such as climate, the selective approach of college teachers, and high academic expectations) in dropouts.
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The Analysis of Early School Leaving from the Aspect of Inter-Sectionality
19-33Views:70Hungary ranks in the bottom third of the European Union regarding early school leaving, falling further and further away from the EU average year on year. The lower educational attainment and higher drop-out rates of Roma/Gypsy youth have been confirmed by several studies. Still, the descriptions are often two-dimensional, as in international approaches. The Hungarian Youth 2020 database allowed for a wider range of explanatory variables in the analysis. In our study, we examine the educational attainment of Roma youth aged 20-29 and then compare subsamples of Roma and non-Roma dropouts. Finally, we run a binary regression model on the database with early school leaving as the dependent variable and explanatory variables as background variables that may shape the odds of early school leaving. The social and economic backgrounds of Roma and non-Roma ESL learners differed, while parental education and subjective financial situation showed a less favourable pattern for Roma. The effect of Roma identity was significant in the regression model, but the explanatory power did not reach the effect of lower parental education. In other words, ethnic background is a crucial factor in dropout, while some segments of the family background are more significant.