Buldaev A.A., Naykhanova L.V., Evdokimova I.S..
Model of decision support system in educational process of a university on the basis of learning analytics
// Software systems and computational methods. – 2020. – є 4.
– P. 42-52.
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Abstract: In recent decades, the potential of analytics and data mining – the methodologies that extract valuable information from big data, transformed multiple fields of scientific research. Analytics has become a trend. With regards to education, these methodologies are called the learning analytics (LA) and educational data mining (EDM). Latterly, the use of learning analytics has proliferated due to four main factors: a significant increase in data quantity, improved data formats, achievements in the area of computer science, and higher complexity of available analytical tools. This article is dedicated to the description of building the model of decision support system (DSS) of a university based on educational data acquired from digital information and educational environment. The subject of this research is the development of DSS with application of learning analytics methods. The article provides a conceptual model of decision-making system in the educational process, as well as a conceptual model of the components of DSS component – forecasting subsystem. The peculiarity of forecasting subsystem model implies usage of learning analytics methods with regards to data sets of a higher educational institution, which contain the results of work of the digital information and educational environment, and include the characteristics of student activity. The main results of the conducted research is the examined and selected methods of clusterization and classification (KNN), the testing of which demonstrated palatable results. The author examined various methods of clusterization, among which k-prototypes method showed best results. The conclusion is made on favorable potential of application of the methods of learning analytics in Russian universities.
Keywords: risk group, forecasting methods, progress forecasting, electronic information and educational environment, educational analytics, classification, clustering, decision support, student progress, artificial intelligence
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