Automated system for diagnosing the ability to solve computational problems based on structural and mental schemes

Cover Page


Problem and goal. The paper deals with an actual problem - learning to solve computational problems. The components of this problem are highlighted and it is shown that to solve it, it is necessary to automate the learning process, especially in terms of organizing independent work. The purpose of this work is to describe the scientific and technological basis for building an automated diagnostic system for solving computational problems (using the example of physical problems), as well as to present the results of using the developed system in the real pedagogical process. Methodology. The described system is based on a mental approach to learning. The paper introduces the concept of a computational primitive and, based on it, the concept of a structuralmental scheme (SMS) - a graph-like model of the ability to solve problems. An Elo-like rating system was used to provide adaptive, continuous monitoring. In order to increase the strength of mastering the ability to solve problems, we implemented the account of forgetting using a piecewise linear model of forgetting. To assess the formation of the ability to solve problems, a value is introduced - the level of assimilation, which is determined by the structural and mental scheme of the student and reflects its completeness and strength. Results. The results of the application of the described automated diagnostic system for the ability to solve computational problems in the real pedagogical process are presented. The described system proved to be effective, i.e. it positively affects the level of formation of the ability to solve problems. The correlation between the number of tasks solved by the student and the level of assimilation is calculated. The efficiency of the presented system in terms of forming the ability to solve computational problems on the example of physical problems is proved. Conclusion. Automation using a computer system allows to track and store information about the status of each individual SMS connection in memory. This cannot be done using any other non-automated tools and technologies due to the large amount of data and the complexity of calculations.

About the authors

Evgeniy V. Asaulenko

Divnogorsk Hydropower Technical School named after A.E. Bochkin

Author for correspondence.
41 Chkalova St, Divnogorsk, 663091, Russian Federation



  1. Asaulenko EV. Primenenie linejnoj funkcii dlya opisaniya zabyvaniya v usloviyah chastogo povtoreniya [Using a linear function to describe forgetting in conditions of frequent repetition]. Dostizheniya nauki v 2017 godu: materialy mezhdunarodnoj konferencii [Achievements of science in 2017: proceedings of the international conference] (p. 31–36). Kiev; 2017.
  2. Asaulenko EV, Pak NI. Model' iskusstvennogo uchitelya na osnove mental'nogo podhoda [Model of an artificial teacher based on a mental approach]. Rossiya – Koreya – SNG [Russia – Korea – CIS]: proceedings of international conference on science and technology (p. 199–203). Novosibirsk: NGTU Publ.; 2018.
  3. Bazhenova IV, Babich N, Pak NI. Ot proektivno-rekursivnoj tekhnologii obucheniya k mental'noj didaktike [From projective-recursive learning technology to mental didactics]: monograph. Krasnoyarsk: SFU Publ.; 2016.
  4. Gippenrejter YuB, Romanova VYa. Psihologiya pamyati [Psychology of memory]. Moscow: AST Publ., Astrel' Publ.; 2008.
  5. Zinchenko VP, Meshcheryakov BG. Bol'shoj psihologicheskij slovar' [Big psychological dictionary]. Moscow: AST Publ.; Saint Petersburg: Prajm-Evroznak Publ.; 2008.
  6. Karlova OA, Pak NI. Model' nepreryvnogo obrazovaniya shkoly budushchego (na primere inzhenernoj shkoly) [Model of continuous education of the school of the future (on the example of an engineering school)]. Otkrytoe obrazovanie [Open education]. 2013;4(99):98–104.
  7. Maslak AA. Teoriya i praktika izmereniya latentnyh peremennyh v obrazovanii [Theory and practice of measuring latent variables in education]: monograph. Moscow: Yurajt Publ.; 2016.
  8. Prikaz Ministerstva obrazovaniya i nauki Rossijskoj Federacii ot 17 maya 2012 g. No. 413 “Ob utverzhdenii federal'nogo gosudarstvennogo obrazovatel'nogo standarta srednego obshchego obrazovaniya” [Order of the Ministry of Education and Science of the Russian Federation of May 17, 2012 No. 413 “On approval of the Federal State Educational Standard of Secondary General Education]. Available from: 70188902/paragraph/2034:0 (accessed: 20.10.2019).
  9. Najsser U. Poznanie i real'nost'. Smysl i principy kognitivnoj psihologii [Cognition and reality. The meaning and principles of cognitive psychology]. Moscow: Progress Publ.; 1981.
  10. Orekhov VP, Usova AV, Turyshev IK et al. Metodika prepodavaniya fiziki v 8–10 klassah srednej shkoly [Methods of teaching physics in grades 8–10 of secondary school] (vol. 1). Moscow: Prosveshchenie Publ.; 1980.
  11. Pak NI. Gipermozg kak osnova stanovleniya mental'noj didaktiki [Hyperbrain as the basis for the formation of mental didactics]. Internet – svobodnyi, bezopasnyi, obrazovatel'nyi [Internet – free, safe, educational]: materials of the interregional scientific and practical conference (p. 42–47). Omsk: Poligraficheskij centr KAN Publ.; 2013.
  12. Pak NI. Na puti k mental'noj didaktike [On the way to mental didactics]. Perspektivy i vyzovy informacionnogo obshchestva [Prospects and challenges of the information society]: materials of the II All-Russian conference with international participation of Krasnoyarsk State Pedagogical University named after V.P. Astafyev (p. 99–102). Krasnoyarsk: Krasnoyarsk State Pedagogical University named after V.P. Astafyev; 2013.
  13. Usova AV, Tul'kibaeva NN. Praktikum po resheniyu fizicheskih zadach [Workshop on solving physical problems]. Moscow: Prosveshchenie Publ.; 2001.
  14. Pelanek R. Applications of the Elo Rating System in Adaptive Educational Systems, Computers & Education. 2016. Available from: 1376317 (data obrashcheniya: 20.10.2019).
  15. Rubin DC, Wenzel AE. One Hundred Years of Forgetting: A Quantitative Description of Retention. Psychological Review. 1996;103(4):734–760.



Abstract - 140

PDF (Russian) - 36




Copyright (c) 2020 Asaulenko E.V.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies