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July 24, 2026
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Educational Data Mining for Prediction of Academically Risky Students Depending on their Temperament

P. 277–290.
Korenkova M., Shadrina E. V., Oshmarina O. E.

The article discusses the influence of temperament on the academic performance of the first-year students at HSE-Nizhny Novgorod on the example of the Faculty of Informatics, Mathematics and Computer Science (IM&CS). The analyses were done with the help of statistics and educational data mining. The baseline data for the study is information about students, obtained by a survey: the information about temperament, degree of extraversion, stability, and other personality traits of students. The study involved students of the first and second years of the faculty of the IM&CS 2017-2018 academic year. Further, psychological factors affecting the average score and the probability of re-training for students with different temperaments were identified. A certain connection between temperament and academic success, which makes possible the prediction of "risky" students, was found. Various machine learning methods are used: the kNN-method and decision trees. The best results were shown by decision trees. As a result, first-year students are classified into three groups (Good, Medium, Bad) according to the degree of risk of getting academic debt. The practical result of the research was the recommendations to the educational office of the Faculty of IM&CS to pay attention to risky students and assist them in the educational process. After the end of the summer session, the classification results were checked. The article also presents an algorithm for finding risky students, taking temperament into account.

Language: English
Full text
DOI
Keywords: studentsdata miningdecision treeacademic achievementson-line questionnairehuman temperamentpsychology of higher education

In book

Recent Trends in Analysis of Images, Social Networks and Texts. 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020 Revised Supplementary Proceedings
Vol. 12602. , Springer, 2021.
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