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.
Original language | English |
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Title of host publication | Proceedings - 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022 |
Place of Publication | New York |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 229-234 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-6654-9117-4 |
DOIs | |
State | Published - 2022 |
Event | 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering - Hong Kong, China Duration: 4 Dec 2022 → 7 Dec 2022 |
Conference
Conference | 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering |
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Abbreviated title | IEEE TALE 2022 |
Country/Territory | China |
City | Hong Kong |
Period | 4/12/22 → 7/12/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Government
- Education
- Focusing
- Natural language processing
- Internet
- Indexes
- Global communication
- Text extraction
- Scientific relevance
- Bibliometrics
- SDG
- Word2Vec
- Social relevance