Typologization of Moscow first-year students: Who goes to capital higher education institutions?

Abstract

Dmitry Popov, Ph.D. in Sociology, senior researcher at the Center for Monitoring Educational Quality, National Research University — Higher School of Economics, Moscow, Russian Federation.  Email: dmitry_popov@sociolog.net 

Yuliya Tyumeneva, Ph.D. in Psychology, senior researcher at the Center for Monitoring Educational Quality, National Research University — Higher School of Economics, Moscow, Russian Federation.  Email: jutu@yandex.ru 

Yuliya Kuzmina, analyst at the Center for Monitoring Educational Quality, National Research University — Higher School of Economics, Moscow, Russian Federation.  Email: papushka@mail.ru 

Using materials of an extensive survey of first-year students, the authors analyze specific aspects of students’ adaptation to learning in universities. A two-step cluster analysis has revealed seven qualitatively differentiated groups of students in Moscow universities. The study compares learning difficulties, attitudes to social life at universities, and distinctive features of goals in life and education in all the seven groups.

It has been established that first-year students are primarily differentiated by their family status and by the reasons for choosing their field of study. These factors are responsible for different attitudes towards education and belonging to the university in the first year of studies. Academic performance at school plays a less important role in differentiating between the student groups. The most significant differences between the groups are found in students’ confidence about their future careers and in their overall assessments of learning experience. The authors identify types of learning activities where different clusters encounter most difficulties. The paper contains a comparison of how first-year students from different clusters assess the choice of field of study they have made.

Each of the clusters is described in detail. Prospects for using the cluster analysis method are specified. Through the example of some student groups, the authors give recommendations on how to create programs oriented at satisfying students’ needs and compensating for specific deficiencies. Cluster analysis results may later be used to trace educational trajectories of students from different clusters.