kvmolurОбразование и саморазвитиеEducation and Self-Development1991-7740Казанский (Приволжский) федеральный университет10.26907/esd.18.2.10YWZVMYkvmolur-103Research ArticleСтатьиПрогнозированиe трудоустройства студентов педагогического вуза на основе использования алгоритмов машинного обученияPredicting Student Employment in Teacher Education Using Machine Learning Algorithmshttps://orcid.org/0000-0003-4471-0875НаговицынР. С.NagovitsynR.

Роман С. Наговицын

г. Глазов

г. Казань

 Roman Nagovitsyn

Glazov

 Kazan 

gto18@mail.ruГлазовский государственный педагогический институт; Казанский государственный институт культурыРоссияGlazov State Pedagogical Institute; Kazan State Institute of CultureRussian Federation202329092025182133148Copyright © Наговицын Р.С., 20252025Наговицын Р.С.Nagovitsyn R.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://kvmolur.elpub.ru/jour/article/view/103

Работа посвящена анализу проблемы, при которой на педагогические профили поступают, а после окончания обучения трудоустраиваются в систему образования не самые лучшие выпускники. В качестве возможного решения автор представляет прогнозирование профессиональной ориентации студентов. Для этого определена цель исследования – на основе внедрения различных алгоритмов машинного обучения разработать программу прогнозирования трудоустройства студентов педагогического вуза и экспериментально доказать эффективность ее использования. После случайного отбора студентов (2011-2016 годов набора) осуществлены сбор и обработка их анкет (n=205). Для создания программы были использованы различные алгоритмы машинного обучения: решающие деревья, логистическая регрессия и catboost. В процессе эксперимента в программу были загружены данные анкет для ее обучения по различным алгоритмам, чтобы в конечном итоге получить готовый интеллектуальный продукт с возможностью прогнозирования трудоустройства выпускников. В итоговом сравнении программа, разработанная на алгоритме «решающие деревья», допустила лишь 2 ошибки из 19 анкет и 7 ошибок из 61 анкеты, что составило самый наилучший результат – 89 % правильности прогноза. Реализация данного алгоритма позволяет наиболее точно, с наименьшим процентом ошибки выявить студентов, которые впоследствии не будут трудоустроены по профилю обучения или вообще не будут трудоустроены. Таким образом, в ходе исследования разработана интеллектуальная программа, которая позволяет моментально обрабатывать данные и получать точный прогноз трудоустройства с незначительной вероятностью ошибки.

One of the solutions to the problem, when not the best graduates enter the pedagogical profiles and after graduation are employed in the education system, is the prediction of professional orientation even at the stage of the student choosing their further professional trajectory. To solve this problem, the purpose of the study is to develop and experimentally prove the effectiveness of using a program for predicting the employment of students of a pedagogical university based on the introduction of various machine learning algorithms. Using a random selection of students, the collection and processing of their questionnaires (n=205) in 2011-2016 were carried out. Various machine learning algorithms were used to create the program: decision trees, logistic regression, and catboost. In the course of the experiment, the data of the questionnaires were loaded into the program for its training according to various algorithms, in order to ultimately obtain a finished intellectual product with the ability to predict the employment of graduates. In the final comparison, the program developed on the “decision trees” algorithm made only 2 out 19 questionnaires and 7 out 61, which was the best result - 89%. The implementation of this algorithm makes it possible to most accurately, with the least percentage of errors, identify students who will not be employed in the future according to their profile of study or not employed at all. Thus, the study developed an intelligent program that allows one to instantly process data and get an accurate forecast of employment with only a small probability of error.

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The authors declare that there are no conflicts of interest present.