Abstract:

Data Science is an interdisciplinary course synthesizing knowledge from several fields: mathematics, statistics, computer science, applied and theoretical economics, linguistics, and others. Students majoring in humanities and economics can choose to take this course to acquire supplementary knowledge and competencies within their programs in economics, management, or law. In any case, the central objective of the course is to develop skills in problem formulation and solution design. This forms the foundation for building applied expertise in developing systems of input and output indicators, information support, database design, solution algorithms, results interpretation, and presenting findings to users in the most accessible and useful format.

To establish a uniform baseline at the outset of the "Data Science" course, students are assigned an introductory essay. Students are asked to write on a topic related to a current issue in data science, discussing the relevance of its theoretical and applied solutions for the national or regional economy, or a specific industry. The texts are submitted to the instructor online and anonymously, using identification codes.

Through the clustering of these texts and the analysis of tokens using text mining methods, it is possible to extract essential professional and pedagogical information from the corpus. This includes: the depth of understanding of the current economic situation, the research topics of greatest interest to students, the division of learners into homogeneous subgroups based on knowledge level and depth of comprehension, the identification of intellectual and creative leaders within the cohort, and the extent of information borrowing from neural networks (AI) along with the students' critical approach to the answers obtained from them. A comparable procedure is implemented following the conclusion of the course. The transformation observed in the semantic spectrum of student understandings of key subject matter and the purposes of data science in the designated applied domain constitutes a beneficial instrument for evaluating pedagogical efficacy and for strategizing the prospective evolution of the course's content.

This report will present the results of developing a methodology, the corresponding software, and the accumulated experience in applying semantic analysis based on modern technologies to enhance engagement and effectiveness in learning Data Science as a supporting tool for the professional activities of economists, managers, and lawyers. Furthermore, the report will provide recommendations for utilizing these results in higher education pedagogy and for fostering effective, "competitive" collaboration between students, as future specialists, and the artificial intelligence technologies they employ during their studies.

Keywords:

Data Science Education, Text Mining, Semantic Analysis, Student Assessment, Pedagogical Methodology, Interdisciplinary Curriculum, AI-Augmented Learning.