Early identification of students at risk of academic failure is a central problem in learning analytics, yet the multi-table, behaviorally heterogeneous, and class-imbalanced nature of educational data complicates reliable prediction. This study develops and comparatively evaluates four supervised machine learning mode...
A. Omarbekova, A. Nazyrova, G. Bekmanova et al.· International Journal of Int...· 0 citations
A wearable marker-assisted navigation system integrating QR code localization, SSD MobileNet V3 obstacle detection, TFmini-S LiDAR ranging, A*-based dynamic route planning, and audio feedback on a Raspberry Pi 5.
Aibol Tileukhan, G. Bekmanova, Valentina Franzoni et al.· Computers· 0 citations
Generative AI tools are now widely used in undergraduate programming, yet most evidence about how students use them comes from self-report rather than from observed behaviour. This study examined the sequential structure of students’ ChatGPT (GPT- 4o, OpenAI)-supported programming work and the cognitive complexity of t...
A. Omarbekova, M. Miłosz, G. Bekmanova et al.· Education sciences· 0 citations
This paper systematically examines monitoring tools for containerized applications, microservices, and DevOps environments, and provides an experimental evaluation across two deployment scenarios. The study was conducted across two infrastructures: on-premises (Docker Swarm, Kubernetes) and cloud-based (Google Kubernet...
A. Omarbekova, Lazzat Kussepova, A. Zulkhazhav et al.· Frontiers of Computer Scienc...· 0 citations
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