The Use of Fuzzy Sets to Detect Strengths Among Students
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| Status: | |
| Autorzy: | Kiersztyn Adam, Kiersztyn Krystyna, Bis Jakub, Jędrzejewska-Rzezak Patrycja |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 243 - 252 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | The work was co-financed by the Lublin University of Technology Scientific Fund: FD-20/IT-3/002. |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 24rd International Conference on Artificial Intelligence and Soft Computing |
| Skrócona nazwa konferencji: | 24rd ICAISC 2025 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 22 czerwca 2025 do 26 czerwca 2025 |
| Miasto konferencji: | Zakopane |
| Państwo konferencji: | POLSKA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| CliftonStrengths (formerly StrengthsFinder) is a popular psychometric tool created by Donald Clifton at the Gallup Institute, used to assess the intensity of 34 traits called “talents”, of which 5 are considered dominant. Talent is understood as a natural pattern of thinking, feeling and acting that can be effectively used in various areas of life. According to the assumptions of the theory, people who develop their key talents are more likely to achieve above-average results. The use of CliftonStrengths in higher education has wide implications. It is commonly perceived that science and technology studies are chosen by people with a greater degree of introversion, disciplined and analytical thinking, while humanities and arts courses attract people who are extroverted, creative and focused on Relationship Building. It is therefore assumed that having certain talents can predispose to fulfilling specific roles in the professional community. The aim of this study was to determine the most frequently appearing talents depending on the chosen field of study. Statistical analysis of the obtained results showed the existence of certain relationships between the talent profile and educational preferences, which are described in detail in the article. The applied machine learning tools and fuzzy techniques allowed to indicate relationships that had not been considered by specialists so far and shed new light on the issues considered. |
