The Use of Data Mining in the Management of the Career Guidance Work of the University

This study explores the application of data mining techniques to analyse factors influencing university choice and predict enrolment trends in Kazakhstan. For this purpose, methods of analysis (multiple correlation and regression analysis, factor analysis), Brown’s prediction model (Brown’s method), synthesis, concretization, comparison, generalization and survey were used. A survey of 192 first-year students was conducted to identify information sources used by applicants and key factors influencing university choice. Four main factors were revealed through principal component analysis: organization of educational activities, recommendations, availability of state grants, and unwillingness to serve in the army. Enrolment forecasting was conducted using demographic and economic variables in a time series regression model. Results indicated birth rate and migration as significant drivers of enrolment demand. The findings provide insights into applicant decision-making that can inform career guidance and enrolment management strategies. This exploratory study demonstrates the potential of data mining to improve the career counselling and strategic planning capacity of universities.