Comparative analysis of pedagogical technologies of educational ecosystems of a technical university in the teaching of economics, innovation and mathematical analysis

Abstract

The modern educational paradigm in higher technical education is undergoing significant transformations under the influence of global technological shifts and new labor market demands on graduates' competencies. An engineer today must possess not only technical knowledge, but also the skills of an economist, innovator, and researcher capable of working in interdisciplinary teams. In this context, the concept of the university's educational ecosystem is being formed, combining formal and informal practices, digital platforms, research centers and industrial partners. The effectiveness of such an ecosystem depends on the adequacy of pedagogical technologies, especially when teaching disciplines with different epistemological natures, such as economics, innovation, and mathematical analysis. Traditional reproductive approaches are insufficient for the formation of comprehensive competencies, leading to a decrease in students' motivation and a gap between theory and practice. There is a need for a comparative analysis of pedagogical technologies – from lecture and seminar to project and digital technologies – to identify their effectiveness in a technical university. The purpose of the study is to evaluate the effectiveness of various technologies in teaching these disciplines and to develop recommendations for optimizing the educational process. The study is based on a comprehensive pedagogical experiment conducted at the engineering faculties of the Technical University over four academic semesters with the participation of more than 500 students divided into experimental and control groups. For each discipline (Economics, Innovation, and Mathematical Analysis), three models were used: traditional (lecture-seminar), project-based (based on cases and projects), and mixed (with elements of online learning and digital resources in the LMS). The data was collected through an analysis of academic performance, a questionnaire to assess engagement, and an expert assessment of practical skills. The processing was carried out using mathematical statistics methods (variance and correlation analysis) using SPSS. The toolkit included standardized tests, an adapted NSSE questionnaire, and criteria for assessing skills: analyzing business cases for economics, developing projects for innovation, and applying theory to mathematical analysis tasks. The analysis revealed the heterogeneity of technology efficiency. For innovation, the project model provided the highest academic performance (88.7 points), engagement (9.1 points) and practical skills (4.7 points). The economy was dominated by mixed technology, with scores of 85,3 in academic performance, 8,5 in engagement, and 4,1 in skills. For mathematical analysis, the traditional model yielded better academic performance (82.4 points), but low engagement (7.3 points) and skills (3.1 points), while the project model increased motivation (8.2 points) and practice (3.9 points). Correlation analysis showed a strong relationship between engagement and skills (r > 0.85) for active models, but a weak one for fundamental disciplines (r ≈ 0.4). The results emphasize the impossibility of a universal approach and confirm the hypothesis of technology differentiation across disciplines. The project model is optimal for innovation, stimulating creativity and practice. Mixed – for economics, balancing theory and simulation. Mathematical analysis requires a hybrid that integrates traditional rigor with applied elements to increase motivation without compromising theory. The study recommends a transition to an adaptive ecosystem that combines technologies to form integrated engineering competencies, which will increase the competitiveness of graduates. Further work should focus on the development of hybrid models for fundamental disciplines.