Abstract

The relationship between science and society has a fundamental role in the pursuit of improvement in our quality of life. In this study, we looked forward to finding relevant study topics for society, aligned with the Sustainable Development Goals approved by the UN in the 2030 Agenda. Using NLP (Natural Language Processing), we withdraw information from 115 undergraduate theses of systems engineering and computer science degrees, available in public repositories of Peruvian universities. We analyzed and elaborated a set of indicators (similarity with the keywords of the research’s lines and the statement of the SDGs) to know and quantify the degree of the scientific and social relevance of the undergraduate theses. Finally, we used bibliometric indicators (like originality, topic similarity, citations, etc.) to complement the relevance analysis contributing to knowledge about the gap of interests between academia and society.

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