Design of individual learning trajectory based on mixed diagnostic tests and cognitive graphic tools

Anna Yankovskaya, Yury Dementyev, Danil Lyapunov, Artem Yamshanov

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

2 Citations (Scopus)

Abstract

In this paper we discuss the relevance of students' computer-based testing, as well as the currently existing approaches for the control of learning and training within the scope of blended learning. Construction of mixed diagnostic tests, representing a compromise between unconditional and conditional components, in order to develop students' knowledge monitoring in electrical engineering is proposed. The authors suggest a technique for optimal mixed diagnostic tests construction based on the expert knowledge of the subjects. Decision-making is carried out by means of fuzzy logic, threshold function and cognitive graphic tools. One of the useful outcomes of mixed diagnostic tests is the courses learning curve design for each individual student. The developed approach is applied for the "Power Electronics" discipline to construct students' learning trajectory and define their further ways of personal development in the field of electrical engineering.

Original languageEnglish
Title of host publicationProceedings of the 35th IASTED International Conference on Modelling, Identification and Control, MIC 2016
PublisherACTA Press
Pages59-65
Number of pages7
Volume830
ISBN (Electronic)9780889869790
DOIs
Publication statusPublished - 1 Jan 2016
Event35th IASTED International Conference on Modelling, Identification and Control, MIC 2016 - Innsbruck, Austria
Duration: 15 Feb 201616 Feb 2016

Conference

Conference35th IASTED International Conference on Modelling, Identification and Control, MIC 2016
CountryAustria
CityInnsbruck
Period15.2.1616.2.16

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Keywords

  • Assessment
  • Blended learning
  • Cognitive graphic tool
  • Fuzzy logic
  • Learning and testing intelligent system
  • Learning trajectory
  • Mixed diagnostic tests
  • Pattern recognition
  • Quality control
  • Simplex

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Computer Science Applications

Cite this

Yankovskaya, A., Dementyev, Y., Lyapunov, D., & Yamshanov, A. (2016). Design of individual learning trajectory based on mixed diagnostic tests and cognitive graphic tools. In Proceedings of the 35th IASTED International Conference on Modelling, Identification and Control, MIC 2016 (Vol. 830, pp. 59-65). ACTA Press. https://doi.org/10.2316/P.2016.830-042