Development of distributed data processing and machine learning competencies for agroindustrial information networks in students of agricultural study programmes: methodological system and experimental validation

A methodological system for developing distributed data processing and machine learning competencies for agro-industrial information networks among students of agricultural study programmes has been designed and experimentally validated. The system comprises five components: a target component (four competency blocks), a content component (a 108-hour course of four modules), a procedural and technological component (a laboratory workbench and 34 practical assignments based on a single greenhouse telemetry dataset), a diagnostic component (assessment criteria, a common three-level scale, a test, and scoring rubrics for practical work and project defence), and a resource component. The intended outcome is formulated operationally: a student independently deploys the chain “sensor — message broker — server — time-series database — model — edge node” and interprets the result in agronomic terms. The study was conducted at Murmansk Arctic University during the 2023/24 and 2024/25 academic years and involved 132 bachelor students assigned to an experimental group (n = 68) and a control group (n = 64), comparable in their initial educational characteristics. Competency formation was assessed across cognitive, instrumental, analytical, and integrative blocks with common level boundaries: low — 0–39 points, basic — 40–69, advanced — 70–100. The integral competency index in the experimental group increased from 38.4 to 72.8 points (Δ = +89.6%), whereas in the control group it rose from 37.9 to 52.1 points (Δ = +37.5%); the differences were statistically significant (p < 0.001), with Cohen’s d = 1.24. The share of students reaching the advanced level amounted to 42.6% in the experimental group compared with 14.1% in the control group. The results confirm the pedagogical effectiveness of the modular methodological system aimed at moving from theoretical understanding of distributed architectures to the deployment of applied pipelines for processing agricultural telemetry and machine learning models.

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