Comparative analysis of two new concepts of adaptive training

473

Abstract

Two new concepts of adaptive learning are presented. The first of them is based on a self-learning probabilistic model, the second one uses multivariate statistical analysis of wavelet representations for task execution trajectories as well as a matrix of recommended transitions. A comparative analysis of various aspects of their practical application has been carried out.

General Information

Keywords: adaptive training, IRT, method of patterns, Markov random processes, wavelet analysis, self-learning systems.

Journal rubric: Mathematical Psychology

Article type: scientific article

DOI: https://doi.org/10.17759/exppsy.2019120213

Funding. This work has been supported by the Russian Foundation for Basic Research (Project No 17-29-07034).

For citation: Kuravsky L.S., Yuryev G.A., Dumin P.N., Pominov D.A. Comparative analysis of two new concepts of adaptive training. Eksperimental'naâ psihologiâ = Experimental Psychology (Russia), 2019. Vol. 12, no. 2, pp. 177–192. DOI: 10.17759/exppsy.2019120213. (In Russ., аbstr. in Engl.)

References

  1. Kibzun A.I., Panarin S.I. Formirovanie integral’nogo rejtinga s pomoshch’yu statisticheskoj obrabotki rezul’tatov testov // Avtomatika i telemekhanika. 2012. № 6. 119—139.
  2. Kibzun A.I., Vishnyakov B.V., Panarin S.I. Obolochka sistemy distancionnogo obucheniya po matematicheskim kursam // Vestnik komp’yuternyh i informacionnyh tekhnologij. 2008. № 10. S. 43—48.
  3. Kuravskij L.S., Marmalyuk P.A., YUr’ev G.A., Dumin P.N. CHislennye metody identifikacii markovskih processov s diskretnymi sostoyaniyami i nepreryvnym vremenem // Matematicheskoe modelirovanie. 2017. T. 29. № 5. S. 133—146.
  4. Kuravskij L.S., YUr’ev G.A., Ushakov D.V., YUr’eva N.E., Valueva E.A., Lapteva E.M. Diagnostika po testovym traektoriyam: metod patternov // Eksperimental’naya psihologiya. 2018. T. 11. № 2. S. 77—94. doi:10.17759/exppsy.2018110206
  5. Osipov G.S., Bryancev O.A. Modificirovannyj metod svodnyh pokazatelej kak metod ocenki sistem distancionnogo obucheniya dlya morskogo flota // Ekspluataciya morskogo transporta. 2007. № 3 (49). S. 48—52.
  6. Sologub G. B.Postroenie frejmovyh semanticheskih modelej v intellektual’noj sisteme testirovaniya // Informacionnye i telekommunikacionnye tekhnologii. 2012. № 14. S. 87—93.
  7. Aircraft trajectory clustering techniques using circular statistics. Yellowstone Conference Center, Big Sky, Montana, 2016. IEEE.
  8. Bastani V., Marcenaro L., Regazzoni C. Unsupervised trajectory pattern classification using hierarchical Dirichlet Process Mixture hidden Markov model // 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) / IEEE. 2014. P. 1—6.
  9. Borg I., Groenen P.J.F. Modern Multidimensional Scaling Theory and Applications // Springer. 2005. P. 140.
  10. Cramer H. Mathematical Methods of Statistics. Princeton: Princeton University Press. 1999. 575 p.
  11. Eerland W.J., Box S. Trajectory Clustering, Modelling and Selection with the focus on Airspace Protection // AIAA Infotech@ Aerospace. AIAA. 2016. P. 1—14.
  12. Enriquez M. Identifying temporally persistent flows in the terminal airspace via spectral clustering // Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013) / Federal Aviation Administration (FAA) and EUROCONTROL. Chicago, IL, USA: 2013. June 10—13.
  13. Enriquez M., Kurcz C. A Simple and Robust Flow Detection Algorithm Based on Spectral Clustering // International Conference on Research in Air Transportation (ICRAT) / Federal Aviation Administration (FAA) and EUROCONTROL. Berkeley, CA, USA, 2012. May 22—25.
  14. Gaffney S., Smyth P. Joint probabilistic curve clustering and alignment // Advances in Neural Information Processing Systems. Vol. 17. Cambridge, MA: MIT Press, 2005. P. 473—480.
  15. Gaffney S., Smyth P. Trajectory clustering with mixtures of regression models // Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. 1999. P. 63—72.
  16. Srivastava A., Feron E. Trajectory clustering and an application to airspace monitoring // IEEE Transactions on Intelligent Transportation Systems. 2011. Vol. 12. № 4. P. 1511—1524.
  17. Grevtsov N. Synthesis of control algorithms for aircraft trajectories in time optimal climb and descent // Journal of Computer and Systems Sciences International. 2008. Vol. 47. № 1. P. 129—138.
  18. Hung C., Peng W., Lee W. Clustering and aggregating clues of trajectories for mining trajectory patterns and routes // The VLDB Journal — The International Journal on Very Large Data Bases. 2015. Vol. 24. № 2. P. 169—192.
  19. Krasilshchikov M.N., Evdokimenkov V.N., Bazlev D.A. Individually adapted airborne systems for monitoring the aircraft technical condition and supporting the pilot control actions. M.: MAI Publishing House, 2011. 440 p (in Russian).
  20. Kuravsky L.S., Artemenkov S.L., Yuriev G.A., Grigorenko E.L. New approach to computer-based adaptive testing // Experimental Psychology. 2017. Vol. 10. № 3. P. 33—45. doi:10.17759/exppsy.2017100303
  21. Kuravsky L.S., Margolis A.A., Marmalyuk P.A., Panfilova A.S. , Yuriev G.A. Mathematical aspects of the adaptive simulator concept // Psychological Science and Education. 2016. Vol. 21. № 2. P. 84—95. doi: 10.17759/pse.2016210210  (in Russian).
  22. Kuravsky L.S., Margolis A.A., Marmalyuk P.A., Panfilova A.S., Yuryev G.A., Dumin P.N. A Probabilistic Model of Adaptive Training // Applied Mathematical Sciences. 2016. Vol. 10. № 48. 2369. URL: http:// dx.doi.org/10.12988/ams.2016.65168 (Accessed 13.04.2019)
  23. Kuravsky L.S., Marmalyuk P.A., Yurev G.A. Diagnostics of professional skills based on probability distributions of oculomotor activity// RFBR Journal. 2016. №. 3 (91). P. 72—82 (Supplement to “Information Bulletin of RFBR” № 24, in Russian).
  24. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A. and Dumin P.N. A Numerical Technique for the Identification of Discrete-State Continuous-Time Markov Models // Applied Mathematical Sciences. , 2015. Vol. 9. № 8. P. 379—391. URL: https://doi.org/10.12988/ams. 2015.410882. (Accessed 13.02.2019)
  25. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A., Belyaeva O.B., Prokopieva O.Yu. Mathematical foundations   of flight crew diagnostics based on videooculography data // Applied Mathematical Sciences. 2016. Vol. 10. № 30. P. 1449—1466. URL: https://doi.org/10.12988/ams.2016.6122 (Accessed 3.02.2019).
  26. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A., Dumin P.N., Panfilova A.S. Probabilistic modeling of CM operator activity on the base of the Rasch model // Proc. 12th International Conference on Condition Monitoring & Machinery Failure Prevention Technologies. Oxford, UK, June 2015.
  27. Kuravsky L.S., Yuriev G.A. Probabilistic method of filtering artefacts in adaptive testing // Experimental Psychology. 2012. Vol. 5. № 1. P. 119—131 (in Russian).
  28. Kuravsky L.S., Yuryev G.A. Certificate of state registration of the computer program № 2018660358 Intelligent System for Flight Analysis v1.0 (ISFA#1.0). — Application № 2018617617; declared 18 July 2018; registered 22 August 2018. (ROSPATENT).
  29. Kuravsky L.S., Yuryev G.A. Detecting abnormal activities of operators of complex technical systems and their causes basing on wavelet representations // International Journal of Civil Engineering and Technology (IJCIET). Vol. 10 (2). P. 724—742. URL: http://www.iaeme.com/IJCIET/ issues.asp?JType=IJCIET&V Type=10&IType=2.  (Accessed 19.03.2019)
  30. Kuravsky L.S., Yuriev G.A., Dumin P.N. Estimating the Influence of Human Factor on the Activity of Operators of Complex Technical Systems in Civil Engineering with the Aid of Adaptive Diagnostics // International Journal of Civil Engineering and Technology. 2019. Vol. 10(2). P. 1930—1941, http://www. iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10  &IType=02  (Accessed 11.01.2019)
  31. Kuravsky L.S., Yuryev G.A. On the approaches to assessing the skills of operators of complex technical systems // Proc. 15th International Conference on Condition Monitoring & Machinery Failure Prevention Technologies. Nottingham, UK, September 2018. 25 p.
  32. Laxhammar R., Falkman G. Online learning and sequential anomaly detection in trajectories // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2014. Vol. 36. № 6. P. 1158—1173.
  33. Li Z. et al. Incremental clustering for trajectories // Database Systems for Advanced Applications. Lecture Notes in Computer Science. 2010. Vol. 5982. P. 32—46.
  34. Markov models in the diagnostics and prediction problems: Textbook / Edited by L.S. Kuravsky. 2nd Edition, Enlarged. Moscow: MSUPE Edition, 2017. 203 p. (in Russian).
  35. Neal P.G. Multiresolution Analysis for Adaptive Refinement of Multiphase Flow Computations. University of Iowa, 2010. 116 p.
  36. Rasch G. Probabilistic models for some intelligence and attainment tests. // Copenhagen, Danish Institute for Educational Research, expanded edition (1980) with foreword and afterword by B.D. Wright. Chicago: The University of Chicago Press, 1960/1980.
  37. René Vidal, Yi Ma, Shankar Sastry. Generalized Principal Component Analysis / New York: Springer- Verlag, 2016. URL: http://www.springer.com/ us/book/9780387878102 (Accessed 13.04.2019)
  38. Rintoul M., Wilson A. Trajectory analysis via a geometric feature space approach // Statistical Analysis and Data Mining: The ASA Data Science Journal. 2015.
  39. Trevor F. Cox, M.A.A. Cox. Multidimensional Scaling, Second Edition. Chapman & Hall/CRC, 2001. 299 p.
  40. Wilson A., Rintoul M., Valicka C. Exploratory trajectory clustering with distance geometry // International Conference on Augmented Cognition / Springer. 2016. P. 263—274.
  41. Xiangyu Kong, Changhua Hu, Zhansheng Duan. Principal Component Analysis networks and algorithms. Springer, 2017. URL: http://www.springer.com/us/book/9789811029134 (Accessed 13.04.2019)

  42.  

Information About the Authors

Lev S. Kuravsky, Doctor of Engineering, professor, Dean of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-3375-8446, e-mail: l.s.kuravsky@gmail.com

Grigory A. Yuryev, PhD in Physics and Matematics, Associate Professor, Head of Department of the Computer Science Faculty, Leading Researcher, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-2960-6562, e-mail: g.a.yuryev@gmail.com

Pavel N. Dumin, PhD in Physics and Matematics, head of laboratory of quantitative psychology, faculty of information technologies, Moscow State University of Psychology and Education (MSUPE), Moscow, Russia, ORCID: https://orcid.org/0000-0001-9122-252X, e-mail: duminpn@gmail.com

Denis A. Pominov, Research Scholar, Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-1321-3713, e-mail: pominovda@mgppu.ru

Metrics

Views

Total: 1300
Previous month: 12
Current month: 17

Downloads

Total: 473
Previous month: 2
Current month: 7