Saltanat Tamenova

The effective functioning of a smart university management system significantly improves higher education institutions’ competitiveness. This study aims to assess the implementation of a smart university management system on the example of Kazakh Ablai khan International Relations and World Languages University. The paper considers elements of a smart university system (smart learning technology infrastructure, smart faculty staff, and smart students) from the university managers’ and students’ viewpoints using the stepwise algorithm to raise the university’s competitiveness. During the study, 2700 students were surveyed to assess the implementation and operation of smart systems at the university, and ten experts (the expert team consists of deans and head of departments) were interviewed to identify the weights of each element. The survey results showed that the technology infrastructure for smart learning at the university is at a lower level (50%) than the competence of the teaching staff (60%) and students (89%). Furthermore, based on standardized factor scores and the weighted average, the implementation level of a smart university management system is low – C (0.498), where the weighted assessment of smart learning technology infrastructure is 0.2, smart teaching faculty – 0.19, and smart students – 0.108. According to the results, the university management needs to develop the smart learning technology infrastructure (free Wi-Fi, smart education environment, and smart classrooms) and improve the qualifications of its teaching staff (knowledge and skills, content of the courses, and educational process).

Having a university degree provides only a slight advantage over those without higher education, making other factors crucial in determining a graduate’s employment prospects. This study aims to analyze the factors that affect the employment of university graduates and define opportunities for public administration and university management in Kazakhstan. A logistic regression model based on primary data was used to examine the impact of the availability of practical experience and jobs in their specialty, social connections, technology development, and entrepreneurship on the likelihood of employment. The survey was conducted in 2022 and involved 300 graduates of the 2020–2021 academic year from all regions of Kazakhstan. Findings show that personal connections (F3) and technology and entrepreneurship (F4) positively impact graduates’ job prospects. However, lack of experience (F1) and the limited number of job offers (F2) reduce employment likelihood. If a graduate responds with 3 points for F1, 7 for F2, 7 for F3, and 1 for F4, they are less likely to secure a job in their specialty within a year of graduation. The main practical value of this study is that university career centers can use this model to predict the likelihood of graduates being employed. Providing a sophisticated online platform and different analytics-driven career services, open access to administrative data on the labor market, and new programs for students’ job experience and entrepreneurship will prepare university graduates for a dynamic labor market and reduce the mismatch between education and employment needs.