The Future of Education

Edition 8

Accepted Abstracts

Data Mining Analysis of Learning Preferences among Gifted Students

Zdena Lustigova, Charles University in Prague (Czech Republic)

Veronika Novotna, Department of public health and preventive medicine, Faculty of medicine in Pilsen, Charles university in Prague, (Czech Republic)


This paper presents selected results of data mining analysis of educational and psychological/psychometric data, collected within 5 years on the group of 91 children, who participated in a specific online educational program (math, programming and science) for gifted children, organized by Charles University in Prague.  The information record for each student is represented by 151 variables, of both categorical and quantitative nature, describing 1) demographic characteristics, 2) personal characteristics, including motivation and intelligence 3) behavioral and action records and 4) particular educational results and learning paths. The study is based on comparison of students with very similar personal characteristics like motivation and intelligence and their study interests and results. Students, who chose the right courses, that matched their nature of talent and personal characteristics, succeed.  Those, who for some reason choose the course that does not match his /her nature of talent, are more likely to fail. Both, course selection and connected success or failure within selected course, is affected mostly by all components of intelligence, except of crystalline, and also by selected components of motivation, especially Dominance, Independence and Persistence. The nature of talent seems to be determined besides factors mentioned above by selected components of creativity.


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