Predicting Student Employment in Teacher Education Using Machine Learning Algorithms
https://doi.org/10.26907/esd.18.2.10
EDN: YWZVMY
Abstract
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.
About the Author
R. Nagovitsyn
Glazov State Pedagogical Institute; Kazan State Institute of Culture
Russian Federation
Russian Federation
Roman Nagovitsyn
Glazov
Kazan
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Review
For citations:
Nagovitsyn R. Predicting Student Employment in Teacher Education Using Machine Learning Algorithms. Education and Self-Development. 2023;18(2):133-148. (In Russ.) https://doi.org/10.26907/esd.18.2.10. EDN: YWZVMY
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