Architectural Synthesis of Composite Neural Network Systems for Predictive Data Processing in Digital Educational Environments

Abstract

Introduction. The ongoing digital transformation of global educational environments necessitates the deployment of intelligent frameworks capable of real-time optimization of individual learning pathways. Neural networks (NN) have emerged as primary technological drivers in this process, providing advanced predictive analytics of learning activities. However, the increasing parametric complexity of modern monolithic architectures, such as Transformers, creates significant technical barriers for their widespread use on entry-level student hardware (laptops and tablets). In the Ukrainian context, where infrastructure instability often limits access to reliable high-performance cloud resources, the synthesis of lightweight yet highly accurate composite models capable of local data processing is a critical strategic requirement.

The purpose of the paper is to substantiate the methodological and technical principles for synthesizing composite neural network architectures (integrating RNN, GNN, and Transformers) to intensify adaptive learning processes and ensure stable predictive data processing in resource-constrained environments.

Methods. The study employs a formal framework of non-linear dynamic systems to model student cognitive progress. The architectural synthesis involves a modular task decomposition strategy: temporal dynamics are managed by LSTM blocks, structural knowledge dependencies are captured by GCN layers, and semantic interactions are processed via self-attention mechanisms. Experimental validation was conducted on a laptop equipped with consumer-grade hardware (Intel i5-1135G7 CPU, NVIDIA MX350 GPU) with programmatically restricted video memory (1 GB) to simulate multitasking environments. Performance metrics included high-precision latency counters, hardware monitoring via NVML, and predictive accuracy assessment using AUC ROC.

Results. Theoretical FLOPs analysis indicated that the proposed composite architecture requires 18–20 times fewer floating-point operations than monolithic Transformer baselines. For a sequence length of N = 512, calculating the Self-Attention mechanism requires approximately 2  N2  d operations (covering both OKT and V). In contrast, the LSTM-based composite requires O(N  (8dh)) operations, providing massive efficiency gains. Empirical testing recorded a 14-fold reduction in GPU inference latency (from 86.4 ms down to 6.1 ms) and a 20-fold reduction in the memory footprint. The architecture successfully bypassed CUDA Out-of-Memory errors and prevented thermal throttling, maintaining a stable GPU operating temperature (54° C vs 78° C).

Conclusions. The optimization of educational trajectories is strictly dependent on the architectural synthesis of neural networks. Monolithic models are often impractical for local use due to their O(N2) complexity and high memory consumption. Composite ensembles provide a necessary balance between predictive precision and operational stability. The ability to process data locally on user devices ensures both educational continuity during infrastructure instability and data privacy by design. These findings establish that hybrid, resource-efficient architectures are the most viable path for creating sustainable, human-centric educational ecosystems in the digital era.