Students learning results prediction with usage of mixed diagnostic tests and 2-simplex prism

Anna Yankovskaya, Yury Dementyev, Artem Yamshanov, Danil Lyapunov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Students learning results prediction is one of the “hottest” problems in modern learning process. We describe mathematical framework of students learning results assessment on the basis of mixed diagnostic tests (MDT). This framework includes cognitive graphic tools 2-simplex and 2-simplex prism, being the powerful means of data visualization for learning outcomes evaluation and efficient goal-setting. A new approach to the prediction of students’ learning results based on MDT and cognitive graphic tools is proposed. Students test results for the e-learning course “Selected Chapters of Electronics” and examples of their learning outcomes cognitive visualization are given. The proposed approach to prediction and cognitive visualization is discussed. Cross-platform software implementation specificity of cognitive graphic tools invariant to problem areas is described.

Original languageEnglish
Title of host publicationIntelligent Data Processing - 11th International Conference, IDP 2016, Revised Selected Papers
EditorsVadim V. Strijov, Dmitry I. Ignatov, Konstantin V. Vorontsov
PublisherSpringer Paris
Pages106-121
Number of pages16
ISBN (Print)9783030353995
DOIs
Publication statusPublished - 2019
Event11th International Conference on Intelligent Data Processing, IDP 2016 - Barcelona, Spain
Duration: 10 Oct 201614 Oct 2016

Publication series

NameCommunications in Computer and Information Science
Volume794 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference11th International Conference on Intelligent Data Processing, IDP 2016
CountrySpain
CityBarcelona
Period10.10.1614.10.16

Keywords

  • 2-simplex prism
  • Cognitive tool
  • Data visualization
  • e-learning
  • Learning
  • Mixed diagnostic test
  • Prediction

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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