Advertisement

Smart Algebraic Approach to Analysis of Learning Outcomes

  • Natalia A. SerdyukovaEmail author
  • Vladimir I. Serdyukov
  • Sergey S. Neustroev
  • Elena A. Vlasova
  • Svetlana I. Shishkina
Conference paper
  • 49 Downloads
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 188)

Abstract

One of the problematic issues of education is a comparative analysis of the results achieved by students in the learning process. The results may vary, for example, in breadth, in depth of knowledge, different areas of mathematics can be mastered by the same student at completely different levels, etc. The proposed smart algebraic approach that uses a probabilistic approach, semantic networks, and marked graphs for the analysis of learning outcomes allows you to measure learning outcomes, taking into account the structure of the knowledge system, rank of students according to their level of knowledge, evaluate their strengths and weaknesses, identify gaps among students in knowledge, and create recommendations on the teaching methodology. Smart algebraic model of learning outcomes analysis uses algebraic formalization of systems and probabilistic estimation methods.

Keywords

Smart algebraic model For the analysis of learning outcomes Probabilistic estimation methods 

References

  1. 1.
    Serdyukov, V., Serdyukova, N.: Quasi-fractal model of the semantic knowledge network as the basis for the formation of a pedagogical test. In: Proceedings of the International Conference on the Development of Education in Eurasia (ICDEE 2019).  https://doi.org/10.2991/icdee-19.2019.9
  2. 2.
    Serdyukova, N., Serdyukov, V., Neustroev, S., Shishkina, S.: Assessing the reliability of automated knowledge control results. In: 2019 IEEE Global Engineering Education Conference (EDUCON), pp. 1453–1456. American University in Dubai, Dubai, UAE (2019)Google Scholar
  3. 3.
    Palagin, A., Krivoy, S., Petrenko, N.: Conceptual graphs and semantic networks in systems of processing of naturally-language information. Math. Mach. Syst. (3), 67–79 (2009)Google Scholar
  4. 4.
    Kuznetsov, O., Adelson-Velsky, G.: Discrete Mathematics for the Engineer. Energia, Moscow (1980) (in Russian)Google Scholar
  5. 5.
    Lobach, V.: Bayes Time Series Forecasting Based on Models in the State Space. Belarusian State University, Minsk, Belarus. http://www.elib.bsu.by
  6. 6.
    Lbov, G., Nedelko, V.: Bayesian approach to forecasting problem solution based on experts’ information and data table. RAS Rep. 357(1), 29–32 (1997)Google Scholar
  7. 7.
    Bishop, C.: Pattern recognition and machine learning. In: Jordan, M., Kleinberg, J., Schoellkopf, B. (eds.) Information Science and Statistics, 2006. Springer Science + Business Media, LLC (2006)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Natalia A. Serdyukova
    • 1
    Email author
  • Vladimir I. Serdyukov
    • 2
    • 3
  • Sergey S. Neustroev
    • 3
  • Elena A. Vlasova
    • 2
  • Svetlana I. Shishkina
    • 2
  1. 1.Plekhanov Russian University of EconomicsMoscowRussia
  2. 2.Bauman Moscow State Technical UniversityMoscowRussia
  3. 3.Institute of Education Management, Russian Academy of EducationMoscowRussia

Personalised recommendations