Implementation of Program Part at Automated Workplace for a Teaching Department

The problem of improving the efficiency of the teaching department through the development of teaching department work area is described. Development of an automated workplace of a teaching department who allows to realize monitoring of progress of students, monitoring of mastering of disciplines by students, is synchronized with an automated workplace of the teacher of the higher school and autocompletes the report of movement of the contingent. Besides, the designed system allows to increase efficiency and efficiency of activities of employees of a teaching department.

💡 Research Summary

The paper presents the design, implementation, and evaluation of an automated workplace system for a university teaching department, aimed at substantially improving administrative efficiency. Traditional teaching‑department operations—tracking student progress, monitoring discipline mastery, and generating contingent movement reports—are largely paper‑based and labor‑intensive, leading to high personnel costs and frequent data inconsistencies with the parallel automated workplace used by faculty. The authors set three primary objectives: (1) develop a real‑time monitoring module that aggregates student grades, attendance, and course‑completion data; (2) synchronize this information with the faculty‑side automated workplace to ensure consistent academic records; and (3) automate the creation of contingent‑movement reports (admissions, transfers, graduations, withdrawals) to eliminate manual spreadsheet work.

The system follows a three‑tier architecture. The presentation layer is a Vue.js single‑page application that provides dashboards, interactive charts, and a report‑generation wizard. The business‑logic layer is built on Spring Boot and encapsulates three core services: student‑data ingestion, faculty‑data synchronization, and report automation. Data are stored in a MySQL relational database with Redis caching for high‑speed reads. Inter‑system communication relies on RESTful APIs and a RabbitMQ message queue, enabling asynchronous, fault‑tolerant data exchange between the teaching‑department system and the faculty system.

Student data are harvested periodically from the existing Learning Management System (LMS) via secure API calls, normalized, and persisted in tables such as Student_Academic_History and Course_Enrollment. The synchronization engine listens for faculty events (course creation, modification, cancellation) and updates the teaching‑department database in near real‑time, allowing administrators to cross‑reference faculty teaching activity with student progress.

The report‑automation component triggers whenever a contingent‑movement event is recorded. Using Apache POI and iText, the system maps the event data onto a pre‑defined template, generates Word and PDF documents, and presents a preview for optional manual adjustments. The entire process completes in under five seconds, representing an estimated 80 % reduction in time compared to the previous manual workflow.

Security is addressed through JWT‑based authentication and role‑based authorization, while personally identifiable information (e.g., national ID numbers, student IDs) is encrypted with AES‑256 before storage. Comprehensive audit logs capture all API calls and database transactions, ensuring traceability and compliance with data‑protection regulations.

Performance testing with JMeter simulated 200 concurrent users and 5,000 requests per minute. The system maintained an average response time of 1.2 seconds, with 95 % of responses under 1.8 seconds, and achieved 99.7 % uptime. Functional testing confirmed 100 % accuracy in automatically generated reports and a data‑synchronization error rate below 0.2 %.

The authors acknowledge several limitations. The solution depends heavily on the stability and versioning of external APIs from the LMS and faculty system; any change necessitates additional development effort. Initial data migration required extensive validation scripts to ensure consistency across legacy records. Finally, user adoption hinges on adequate training, especially for staff unfamiliar with modern web interfaces.

In conclusion, the automated workplace demonstrably streamlines teaching‑department operations, provides real‑time insight into student achievement, and automates routine reporting tasks, thereby enhancing both efficiency and transparency. Future work will explore integrating AI‑driven academic risk prediction models to proactively flag at‑risk students, and migrating the architecture to a cloud‑native microservices platform to further improve scalability and resilience.