title: Applied statistics to evaluate the quality of education
metadata.name: dc.contributor.author
metadata.value: Bure, Natalia A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Grebennikova, Nadezhda L.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Staroverova, Kseniya Yu.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2019-01-16T15:53:44Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2019-01-16T15:53:44Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2018-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Bure N. A., Grebennikova N. L., Staroverova K. Yu. Applied statistics to evaluate the quality of education. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2018, vol. 14, iss. 4, pp. 325–333.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11702/spbu10.2018.405
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/15245
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The application of statistical methods and machine learning to analyze the data describing
  the education process are considered. The solution of two problems typical of the educational
  process but different in the organization is shown. The first problem is to analyze the results
  of students’ tests who study Russian as a foreign language to enter the university in Russia.
  The purpose of the analysis is to evaluate the adequacy of the teaching methods, in particular,
  the consistency of results gained for the elementary and intermediate tests with the result
  obtained for the advanced test. Data is transformed firstly, then the analysis of variance is
  conducted, finally, the clustering is built. Found structure shows that students successfully
  coping with elementary and intermediate tests are likely to pass the advances level test.
  In the second problem, the results of studying mathematics by junior pupils are analyzed.
  Classification of pupils is made based on their marks gained for the answer in the lesson.
  The classifier determines the pupil mark for the final control work. The predictive model is
  built as the ensemble of random forests trained on four samples: the first is a sparse matrix
  of estimates, the others are the transformation of the first obtained by principal component
  analysis within a nuclear structure.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 14; Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: statistics
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: random forest
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: clustering
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: the methodics of studying Russian language and mathematics
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: the analysis of education progress
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Applied statistics to evaluate the quality of education
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 05-Bure.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/15245/1/05-Bure.pdf
resources.format: Adobe PDF
resources.size: 431,4 kB