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Evaluation of Machine Learning Methods for the Experimental Classification and Clustering of Higher Education Institutions
Abstract
Higher education institutions have a big impact on the future of skills supplied on the labour market. It means that depending on the changes in labour market, higher education institutions are making changes to fields of study or adding new ones to fulfil the demand on labour market. The significant changes on labour market caused by digital transformation, resulted in new jobs and new skills. Because of the necessity of computer skills, general universities started to offer various courses on IT, including computer science that was originally offered by technical universities. It is also possible to have selected medical studies not only at medical universities but also in private colleges, e.g., nursing studies. As a result, the current classification of higher education institutions used in official statistics can be revised. The paper shows the experimental work on the use of machine learning methods to classify and cluster higher education institutions in Poland. Different attributes were used to classify the type of institution, including fields of studies, programme orientation and others. The aim of the paper was also to evaluate various machine learning methods in the process of classifying or clustering and validating the associated types of higher education institutions.
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- Category:
- Other publications
- Type:
- Other publications
- Title of issue:
- Information Systems. EMCIS 2021. Lecture Notes in Business Information Processing
- Publication year:
- 2022
- Bibliographic description:
- Maślankowski, J., Brzezicki, Ł. (2022). Evaluation of Machine Learning Methods for the Experimental Classification and Clustering of Higher Education Institutions. In: Themistocleous, M., Papadaki, M. (eds) Information Systems. EMCIS 2021. Lecture Notes in Business Information Processing, vol 437. Springer, Cham. https://doi.org/10.1007/978-3-030-95947-0_3
- DOI:
- Digital Object Identifier (open in new tab) https://doi.org/10.1007/978-3-030-95947-0_3
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