Abstract:

The processes of globalization in all the spheres of human activity cause an urgent need for the integration of Russian education system into the world academic environment. One of the main obstacles to the integration is the lack of fluency in English of Russian academic staff. As the re-sponse to the urgent need to enhance the academic staff fluency in English the senior staff of the South Ural State University initiated the programme of additional language training «Lingva». 250 members of the academic staff have been trained and the programme is clearly proved to be effective. However, the issues of improving the programme are always the focus of those responsible for it. A critical aspect here is the feedback from the academic staff involved in training and the study of factors that influence the outcomes.

The goal of the study was to identify the level of maturity of the academic staff participating in the programme. The paper defines the concept of maturity, identifies its key characteristics for the academic staff (autonomy, intrinsic motivation, high level of emotional intelligence, tolerance, self-reflection, flexibility, accepting yourself and others). On the basis of the analysis made the questionnaire for the academic staff was devised focused on determining the level of the academic staff maturity.

The results of the survey were processed with a computer program «Statistical Package for the Social Sciences» (SPSS). We used the methods of descriptive statistics, reliability analysis, variance analysis, multidimensional scaling and cluster analysis.

This study shows a high level of maturity of the academic staff mastering their English language skills. It also demonstrates their strong motivation, ability to set goals, assess strengths and weak-nesses, and monitor the progress. The analysis of the questionnaire reliability revealed the possibility to decrease the number of survey statements, though, increase their level of consistency, which is important for improving the methodology for further surveys and research. Cluster analysis helped to define the optimal number of clusters to split the academic staff. The characteristics of the clusters formulated give the possibility to choose the appropriate methodology, to adjust the education process to a particular group of learners.

Keywords:

Level of maturity, motivational factors, English language proficiency, academic staff, variance analysis, multidimensional scaling, cluster analysis.