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Predicting Student Employment in Teacher Education Using Machine Learning Algorithms

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  Digital Object Identifier (DOI): 10.26907/esd.18.2.10
  Volume Number: 19 | Issue Number: 2 | Pages: 133 - 148
  Published: June 2023
  Article Keyword(s): algorithms, decision trees, employment, forecasting, machine learning, pedagogical university
  Article Author(s) - listed alphabetically: Roman Nagovitsyn
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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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Journal Information

Publisher

Education and Self Development (E&SD) is published by Kazan Federal University (KFU)
See http://kpfu.ru/eng

Contact

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ISSN

ISSN 1991-7740

Frequency of Publication

E&SD publishes four print issues each year. It was established in June 2006

Impact Factor and Ranking

The Journal has been accepted for inclusion in Scopus and is applying for inclusion in Web of Science. At present it has not established an impact factor or ranking but these will be forthcoming.

Open Access

E&SD is an online, open access journal fully funded by Kazan Federal University. The Journal is a signatory to the Budapest Open Access Initiative and is committed to ensuring that all of the articles we publish are freely available. Articles are available to all without charge, and there are no article processing charges (APCs) for authors.

Scope Statement

Available here…

Article Keywords

assessment bibliometric analysis blended learning communication competence Covid-19 creativity critical thinking distance learning education educational environment educational process educational standard evaluation foreign language future teachers higher education identity inclusive education lifelong learning model motivation multicultural education non-formal education pedagogy personality professional competence professional development professional orientation psychological safety quality quality of education reading comprehension reflection self-assessment self-development students teacher teacher education teacher professional development teachers teacher training training upbringing values

Article Authors

Albina R. Drozdikova-Zaripova Alena Hašková Andreja Istenic Starcic Andreja Istenič Anna I. Akhmetzyanova Anna Kobtseva Aydar Kalimullin Aydar M. Kalimullin Aydar Minimansurovich Kalimullin Balwant Singh Branka Radulović Daria Medvedeva Dinara Bisimbaeva Elena Ibragimova Evgeniya Shishova Evsyukova E.A. Fatemeh Khonamri Ian Menter Idiyatov I.E. Ilshat R. Gafurov Kadriya Shakirova Liliana Shakirova Lira V. Artishcheva Lyubov A. Kochemasova Martina Pavlikova Mourat Tchoshanov Musa Nushi Natalya N. Kalatskaya Nick Rushby Oksana Kozhevnikova Olga K. Evdokimova Rezeda M. Khusainova Roza A. Valeeva Roza Valeeva Rushby N.J Tatiana Baklashova Valerian Faritovich Gabdulchakov Venera Zakirova Vera K. Vlasova Vera Khotinets Vera Vlasova Violeta Rosanda Vsevolod V. Andreev Yulia Novgorodova Zdenka Gadušová

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