Developing big data competencies in learners within the context of agro-industrial complex informatization: a model and experimental work

The objective of the study was to develop a pedagogical model for developing big data technology competencies in students majoring in agriculture and to experimentally test its effectiveness in the context of the digital transformation of the agro-industrial complex. The study involved a two-year longitudinal pedagogical experiment (2023–2025) involving 156 students from Murmansk Arctic University (control and experimental groups of 78 students each). The model included four components: cognitive, operational-activity, motivational value, and reflective-evaluative. The experimental group completed a modular course entitled «Big Data in the Digital Agro-Industrial Complex» (72 hours), a practical course on industry datasets with up to 18% omissions (36 hours), and projects based on agricultural holdings (48 hours). Statistical analysis included Pearson’s χ² test, Mann–Whitney test, and Spearman correlation. The proportion of students with a high integrated competency level in the experimental group increased from 7.7% to 47.4% (in the control group, from 7.7% to 17.9%). The differences are statistically significant: χ² = 22.14 with χ²crit = 9.21 (p ≤ 0.01, df = 2). The greatest increase was recorded for the operational-activity component (+25.5 points), and the smallest for the motivational-value component (+13.5 points). The rank correlation coefficients between the components were ρ = 0.62–0.74, confirming the systemic integrity of the competency. The proposed four-component model statistically significantly outperforms the traditional approach to developing digital competencies in agricultural specialists. The model is recommended for multi-center testing in other federal districts.

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