Student attrition is a significant problem and a huge challenge from an institutional point of view. Although completion rates in Poland are lower than in most Organisation for Economic Cooperation and Development countries, the problem has not been studied thoroughly. In order to shed light on that problem the aim of this paper is to present the results of a mixed methods study on dropout behaviour at the University of Warsaw, Poland's largest higher education institution, combining administrative data analysis, survey research and individual in-depth interviews. The main results are: 1) students drop out mainly during the first year of studies, 2) there are three main types of dropoutsplanned dropout, academic failure and those who are disappointed with their studies 3) one of the reasons for this lies in the process of choosing the study programme. Improving this decision process by providing more information and support to candidates should help reduce dropout rates.
The massification of higher education in Poland means that many students choose this educational pathway to improve their chances for a good job. Therefore, the labour market outcomes of graduates provide an important perspective for future students, higher education institutions, as well as decision makers at the national level. The Polish Graduate Tracking System (ELA), based on administrative data, is designed to monitor graduates' outcomes in the labour market by type of studies, higher education institution, as well as individual curricula. Results of the first two years of graduate tracking show that the outcomes vary by study area, but also change over time. While in the first months after graduation, aspects such as prior experience in the labour market and place of residence have a substantial effect on employment chances, in the longer run, they lose their importance relative to other factors.
This paper contributes to the growing body of research that demonstrates uneven impacts of the COVID‐19 pandemic on educational outcomes of students from different socioeconomic status (SES) backgrounds. We evaluate the early impacts of COVID‐19 on student attendance in secondary school and show how these impacts depend on students' SES. We employ a quasi‐experimental design, using difference‐in‐differences (DiD) estimation extended to incorporate third‐order differences over time between low‐SES and other students, and pre‐ versus during‐COVID‐19, leveraging robust administrative data extracted from the registers of the Tasmanian Department of Education. Using data from multiple cohorts of secondary school students in government schools in Tasmania (N = 14,135), we find that while the attendance rates were similar pre‐ and during‐COVID‐19 for high‐SES students, there was a significant drop in attendance rates during COVID‐19 among socioeconomically disadvantaged students, demonstrating the more pronounced impacts of COVID‐19 for these students. The findings demonstrate that even “relatively short” lockdowns, as those in Tasmania in 2020 (30–40 days of home learning), can significantly affect the learning experiences of students from socioeconomically disadvantaged backgrounds. We discuss the implications of this for future pandemic planning in educational policy and practice and how this needs to be addressed in Australia's COVID‐19 recovery.
Understanding the drivers of student dropout from higher education has been a policy concern for several decades. However, the contributing role of certain factors—including student mental health—remains poorly understood. Furthermore, existing studies linking student mental health and university dropout are limited in both methodology and scope—for example, they often rely on small and/or non-representative samples or subjective measures, and focus almost exclusively on main effects. This paper overcomes many of these shortcomings by leveraging unique linked administrative data on the full population of domestic students commencing undergraduate studies at Australian universities between 2012 and 2015 (n = 652,139). Using these data, we document that approximately 15% of students drop out of university within their first academic year. Critically, students receiving treatment for mental health problems are 4.3 (adjusted) to 8.3 (unadjusted) percentage points more likely to drop out of higher education. This association remains in the presence of an encompassing set of potential confounds, and is remarkably uniform across segments of the student population determined by individual, family, and programme characteristics. Altogether, our findings call for increased policy efforts to improve student mental health and to buffer against its deleterious effects on retention.
Artykuł stanowi próbę odpowiedzi na pytanie, na ile pomoc materialna oferowana przez uczelnie jest istotnym czynnikiem przy wyborze uczelni oraz kierunku studiów. Analiza danych dotyczących zakresu oraz skali wsparcia finansowego dla studentów w Polsce poprzedzona została przeglądem badań na temat uwarunkowań decyzji edukacyjnych. Sugerują one, że status społeczno‑ekonomiczny oraz wyobrażenia o przyszłych perspektywach zawodowych odgrywają szczególnie silną rolę w decyzjach kandydatów na studia. Potwierdzają to wyniki badania ankietowego przeprowadzonego wśród kandydatów na studia na Uniwersytecie Ekonomicznym w Poznaniu oraz Uniwersytecie Warszawskim. Wskazują one także na to, że nawet kandydaci o gorszej pozycji społecznej, dokonując wyboru kierunku i uczelni, w niewielkim stopniu biorą pod uwagę pomoc materialną oferowaną przez uczelnię.
Random and pseudo-random number and bit sequence generators with a uniform distribution law are the most widespread and in demand in the market of pseudo-random generators. Depending on the specific field of application, the requirements for their implementation and the quality of the generator’s output sequence change. In this article, we have optimized the structures of the classical additive Fibonacci generator and the modified additive Fibonacci generator when they work together. The ranges of initial settings of structural elements (seed) of these generators have been determined, which guarantee acceptable statistical characteristics of the output pseudo-random sequence, significantly expanding the scope of their possible application, including cybersecurity. When studying the statistical characteristics of the modified additive Fibonacci generator, it was found that they significantly depend on the signal from the output of the logic circuit entering the structure. It is proved that acceptable statistical characteristics of the modified additive Fibonacci generator, and the combined generator realized on its basis, are provided at odd values of the module of the recurrent equation describing the work of such generator. The output signal of the combined generator has acceptable characteristics for a wide range of values of the initial settings for the modified additive Fibonacci generator and the classic additive Fibonacci generator. Regarding the use of information security, it is worth noting the fact that for modern encryption and security programs, generators of random numbers and bit sequences and approaches to their construction are crucial and critical.
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