Использование метода интеллектуального анализа данных для прогнозирования академически рискованных студентов в зависимости от их темперамента (на примере факультета ИМиКН в НИУ ВШЭ-Нижний Новгород)
The article discusses the influence of temperament on the academic performance of the first-year students at HSENizhny Novgorod on the example of the Faculty of Informatics, Mathematics and Computer Science. Analysis was held with the help of statistics methods and methods of data mining. The baseline data for the study is information about students, collected using the online support system for the educational process at HSE – LMS (Learning Management System). The material for the study was the information about temperament, degree of extroversion, stability, and other personality traits of students, obtained by conducting a survey. The study involved students of the first and second years of the faculty of the IM&CS 2017–2018 academic year. Further, the work identifies psychological factors affecting the average score and the probability of re-training for students with different temperaments. Some connection was found between temperament and academic success, which makes possible the prediction of "risky" students. For this, various machine learning methods are used: the kNN-method (k-nearest neighbors) and the decision tree. As a result of the calculations, the best result was shown by the decision tree method. 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.