Abstract:
The study presents the results of developing a system to enhance and
optimize business processes in the interaction between higher education institu-
tions and students, as well as the engagement of prospective applicants in the
learning process. By utilizing Google Search Console an analysis of the website’s
search traffic was performed. The study identified the most popular queries and
the devices through which the highest number of transitions occurred to specific
pages. Data collection spanned both a single month (from December 1st to 31st)
and a full year (January to December).
The trend indicates that people are moving away from information over-
load and transitioning toward private chats. To explore this further, a survey was
conducted among students (48 respondents) at King Danylo University (KDU)
to determine which apps they use and how frequently. The results revealed
that the most widely used app among students is Telegram (72.9%), followed
by Instagram (only 54.2%).
Based on the obtained results, utilizing data from leading analytical compa-
nies and conducting our own survey, we have selected the platform and method
for implementing the system. The outcome is the conceptualization of three Tele-
gram bots: “KDU Addmission” designed to assist prospective students, “KDU
Student” tailored for active students and “KDU Schedule” focused on providing
schedules. As the final outcome, a system was developed that enables the educational institution to seamlessly interact with educational service recipients—from the moment of admission to graduation. The software product was designed with time requirements in mind, allowing for easy adaptation to new goals and demands.
Description:
Sergiy Vashchyshak, Maksym Karpash, Taras Styslo, Oksana Styslo, Vasyl Kitura . Optimization of Business Processes in Private Higher Education Institutions Management System During War Period in Ukraine // Engineering Education for Global Responsibility: Proceedings of the 27th International Conference on Interactive Collaborative Learning (ICL2024), Vol.1. Pp. 317-328: Fig. 11.