Towards a better understanding of self-selection to teacher training programmes: A case study of a renowned public university in Poland

Mikołaj Herbst, Tomasz Zając
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Abstract

Abstract It is difficult to achieve high-quality education without good teachers. Therefore, it is crucial to understand who decides to become a teacher. This study leverages a large-scale administrative dataset comprising detailed records of the educational trajectories of 10 cohorts of students at the University of Warsaw, the largest higher education institution in Poland, in order to investigate self-selection to the teaching profession and to learn whether it depends on the mode of teacher training and the student's primary field of studies. We find that the recruitment of students to the concurrent teacher training programme is characterised by adverse self-selection with respect to prior academic achievements. When it comes to consecutive programmes, pursued as an extension or specialisation within the major programme, the willingness of students to enroll in teacher training is related to their secondary school achievements, but also – and in a distinct way – to their early experience at the university. In the case of STEM and foreign language programmes, we observe adverse selection to teacher training with respect to either the student's pre-university academic outcomes or their achievements during the first year of university studies.
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更好地理解教师培训方案的自我选择:以波兰一所著名公立大学为例
没有优秀的教师,就很难实现高质量的教育。因此,了解谁决定成为一名教师是至关重要的。本研究利用了一个大规模的管理数据集,其中包括波兰最大的高等教育机构华沙大学10组学生的教育轨迹的详细记录,以调查教师职业的自我选择,并了解它是否取决于教师培训模式和学生的主要研究领域。我们发现,学生的招聘并行教师培训计划的特点是不利的自我选择相对于先前的学术成就。当涉及到连续课程时,作为专业课程的延伸或专业,学生参加教师培训的意愿与他们的中学成绩有关,但也以一种独特的方式与他们在大学的早期经历有关。在STEM和外语课程的情况下,我们观察到教师培训在学生大学前的学术成果或他们在大学第一年的学习成绩方面的逆向选择。
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来源期刊
CiteScore
0.60
自引率
0.00%
发文量
9
期刊介绍: The Central European Journal of Economic Modelling and Econometrics (CEJEME) is a quarterly international journal. It aims to publish articles focusing on mathematical or statistical models in economic sciences. Papers covering the application of existing econometric techniques to a wide variety of problems in economics, in particular in macroeconomics and finance are welcome. Advanced empirical studies devoted to modelling and forecasting of Central and Eastern European economies are of particular interest. Any rigorous methods of statistical inference can be used and articles representing Bayesian econometrics are decidedly within the range of the Journal''s interests.
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