Preview

Open Education

Advanced search

Data mining methods application in reflexive adaptation realization in e-learning systems

https://doi.org/10.21686/1818-4243-2017-4-13-20

Abstract

In recent years, e-learning technologies are rapidly gaining momentum in their evolution. In this regard, issues related to improving the quality of software for virtual educational systems are becoming topical: increasing the period of exploitation of programs, increasing their reliability and flexibility. The above characteristics directly depend on the ability of the software system to adapt to changes in the domain, environment and user characteristics. In some cases, this ability is reduced to the timely optimization of the program’s own interfaces and data structure. At present, several approaches to creating mechanisms for self-optimization of software systems are known, but all of them have an insufficient degree of formalization and, as a consequence, weak universality. The purpose of this work is to develop the basics of the technology of self-optimization of software systems in the structure of e-learning. The proposed technology is based on the formulated and formalized principle of reflexive adaptation of software, applicable to a wide class of software systems and based on the discovery of new knowledge in the behavioral products of the system.
To solve this problem, methods of data mining were applied. Data mining allows finding regularities in the functioning of software systems, which may not be obvious at the stage of their development. Finding such regularities and their subsequent analysis will make it possible to reorganize the structure of the system in a more optimal way and without human intervention, which will prolong the life cycle of the software and reduce the costs of its maintenance. Achieving this effect is important for e-learning systems, since they are quite expensive.
The main results of the work include: the proposed classification of software adaptation mechanisms, taking into account the latest trends in the IT field in general and in the field of e-learning in particular; Formulation and formalization of the principle of reflexive adaptation in software systems applicable to a wide class of applied programs;
The development of a universal architectural template of the software system, oriented to restructuring in the process of operation; Algorithm for self-optimization of the user interface of the software system based on methods of data mining.
The development of the theoretical basis for the automatic reorganization of e-learning software will increase the flexibility of the virtual educational environment and increase the period of its exploitation. Unlike existing analogues, the methods proposed in the article are universal and applicable to a wide class of applied programs. This is relevant for e-learning systems, because their may have a different types and purposes (for example, virtual simulators and information library software may be components of one system).

About the Authors

A. S. Bozhday
Penza State University
Russian Federation
Dr. Sci. (Eng.), professor of the department «Computer Aided Design» Penza State University, Penza, Russia


Y. I. Evseeva
Penza State University
Russian Federation
Cand. Sci. (Eng.), assistant of the department «Computer Aided Design» Penza State University, Penza, Russia


A. A. Gudkov
Penza State University
Russian Federation
Cand. Sci. (Eng.), associate professor of the department «Computer Aided Design» Penza State University, Penza, Russia


References

1. Keromytis A.D. Characterizing Software Selfhealing Systems. Computer Network Security. Springer. 2007. P. 22–33.

2. Shin M.E., Cooke D. Connector-Based SelfHealing Mechanism for Components of a Reliable System. Workshop on the Design and Evolution of Autonomic Application Software (DEAS 2005). ACM, 2005. P. 1–7.

3. Smart Sparrow. URL: https://www.smartsparrow.com/ (accessed: 22.05.2017).

4. Rajan C.A., Baral R. Adoption of ERP system: An empirical study of factors influencing the usage of ERP and its impact on end user. IIMB Management Review. 2015. No. 2. P. 105–117.

5. The Next Evolution of ERP: Adaptive ERP. ERP the Right Way: Changing the game for ERP Cloud implementations URL: https://gbeaubouef.wordpress.com/2012/09/05/adaptive-erp/ (accessed: 14.05.2017).

6. WWU Munster. OpenUSS. URL: https://www.uni-muenster.de/studium/orga/openuss.html (accessed: 22.05.2017).

7. WebCT. URL: http://www.cuhk.edu.hk/eLearning/c_systems/webct6/ (accessed: 22.05.2017).

8. Zametki o Big Data. ES-Lizing URL: http://www.ec-leasing.ru/public/publikatsii/index.php?ELEMENT_ID=39 (accessed: 28.04.2017).

9. Big Data in eLearning: The Future of eLearning Industry. eLearning Industry. URL: https://elearningindustry.com/big-data-in-elearning-future-of-elearning-industry (accessed: 22.05.2017).

10. Ravindran K., Rabby M. Software cybernetics to infuse adaptation intelligence in networked systems. IEEE International Conference on the Network of the Future (NOF). Washington: IEEE Computer Society, 2013. P. 1–6.

11. Wang P., Cai K.Y. Representing extended finite state machines for SDL by a novel control model of discrete event systems. Sixth IEEE International Conference on Quality Software (QSIC 2006). Washington: Ieee Computer Society, 2006. P. 159–166.

12. Wang P., Cai K.Y. Supervisory control of a kind of extended finite state machines. 24th IEEE Chinese Control and Decision Conference (CCDC). Washington: Ieee Computer Society, 2012. P. 775–780.

13. Patikirikorala T., Colman A., Han J., Wang L. A systematic survey on the design of selfadaptive software systems using control engineering approaches. 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (IEEE Press). Washington: IEEE Computer Society, 2012. P. 33–42.

14. Lorenzoli, D., Mariani, L., Pezzè, M. Automatic generation of software behavioural models. 30th international ACM conference on Software engineering. New York: ACM, 2008. P. 501–510.

15. Yang, Q., Lü, J., Xing, J., Tao, X., Hu, H., Zou, Y. Fuzzy control-based software self-adaptation: A case study in mission critical systems. IEEE 35th Annual Computer Software and Applications Conference Workshops (COMPSACW). Washington: IEEE Computer Society, 2011. P. 13–18.

16. Liu L., Zhou Q., Liu J., Cao Z. Requirements cybernetics: elicitation based on user behavioural data. J. Syst. Software. Amsterdam: Elsevier. 2016.

17. Park J.S. Essence-based, goal-driven adaptive software engineering. EEE/ACM 4th SEMAT Workshop on General Theory of Software Engineering (GTSE). Washington: IEEE Computer Society, 2015. P. 33–38.

18. Liu C., Jiang C., Hu H., Cai K.Y., Huang D., Yau S.S. Control-based approach to balance services performance and security for adaptive service based systems (ASBS). 33rd Annual IEEE International Computer Software and Applications Conference (COMPSAC’09). Washington: IEEE Computer Society, 2009. P. 473–478.

19. Zhou Y., Taolue C. Software Adaptation in an Open Environment: A Software Architecture Perspective. Boca Raton: CRC Press, 2017.

20. Lemos R. Software Engineering for SelfAdaptive Systems. Berlin: Springer, 2009.


Review

For citations:


Bozhday A.S., Evseeva Y.I., Gudkov A.A. Data mining methods application in reflexive adaptation realization in e-learning systems. Open Education. 2017;(4):13-20. (In Russ.) https://doi.org/10.21686/1818-4243-2017-4-13-20

Views: 1273


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


ISSN 1818-4243 (Print)
ISSN 2079-5939 (Online)