Analyzing the formation of metacompetence in students using EdTech in the learning process

In the study, we examine the impact of educational technology (EdTech) on metacompetence development in students, focusing on cost-effectiveness and economic implications. The research spans educational levels from K-12 to higher education in Kazakhstan and China between 2015-2023, addressing gaps in understanding how EdTech shapes students’ metacompetitive abilities and their economic outcomes. Methods: A mixed-methods approach was employed, combining quantitative data from surveys (n = 1000) and learning analytics with qualitative insights from interviews (n = 50) and case studies. The research utilized multiple statistical models including ANOVA, factor analysis, and regression analysis. Data collection covered various educational levels and disciplines, focusing on metacompetence indicators, learning outcomes, and economic variables. Results: The study revealed that 78.5% of students regularly use EdTech tools, with higher prevalence in STEM fields (85.2%). Three key metacompetence factors were identified: self-regulated learning (α = .85), adaptive problem-solving (α = .79), and collaborative knowledge construction (α = .82). Significant differences were found across demographic groups, with socioeconomic status influencing EdTech access and metacompetence development. Discussion: The findings demonstrate that well-designed EdTech solutions significantly enhance students’ metacompetence, leading to improved employability and higher expected incomes. The study revealed challenges in implementation, including the digital divide and the need for balanced technology integration. The research provides valuable insights for optimizing EdTech use while ensuring positive economic outcomes. Scientific Novelty: This study offers a comprehensive analysis of EdTech’s role in metacompetence formation from an economic perspective, introducing a novel conceptual framework that combines educational technology, metacompetence development, and economic outcomes. Practical Implications: The research provides evidence-based recommendations for educators, policymakers, and EdTech developers to maximize metacompetence formation while ensuring cost-effectiveness. The findings can guide strategic investments in EdTech and inform policies for addressing educational inequities

Mathematics education · Process management · Sociology · Computer Science · Educational Technology and Optimization · Engineering · Psychology · Artificial Intelligence

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