Predicting the Percentage of Student Placement: A Comparative Study of Machine Learning Algorithms

Autor/inn/en	Çakit, Erman; Dagdeviren, Metin
Titel	Predicting the Percentage of Student Placement: A Comparative Study of Machine Learning Algorithms
Quelle	In: Education and Information Technologies, 27 (2022) 1, S.997-1022 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Çakit, Erman)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-021-10655-4
Schlagwörter	Student Placement; Foreign Countries; Artificial Intelligence; Mathematics; Computation; Higher Education; Prediction; Accuracy; Universities; Reputation; Input Output Analysis; College Students; Resource Allocation; Turkey + Suchen Sie Ihr Suchwort? Schülerpraktikum; Ausland; Künstliche Intelligenz; Mathematik; Hochschulbildung; Hochschulsystem; Hochschulwesen; Vorhersage; University; Universität; Collegestudent; Ressourcenallokation; Türkei
Abstract	In recent years, there has been an increase in the demand for higher education in Turkey, where the demand, as in most other countries, exceeds what is available. The main purpose of this research is to develop machine learning algorithms for predicting the percentage of student placement based on the data related to the university's academic reputation, opportunities of the city where the university is located, facilities and cultural opportunities of the university. When the model accuracy was evaluated on the basis of performance metrics, the Extreme Gradient Boosting (XGBoost) algorithm showed greater predictive accuracy than other machine learning approaches. A sensitivity analysis was performed using the extreme gradient boosting machines algorithm to identify the degree to which the input variables contribute to the determination of the output variable. Five input variables, namely the percentage of student placement at year t-1, the university scientific document score, university Phd programme score, university faculty member/student score, and the percentage of student placement at year t-2 were found to be the most effective parameters. Prediction and sensitivity analysis results obtained in this study can be used in many different ways, such as determining the quotas for universities, allocating resources, and making new regulations. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01