Formation of adaptive dynamic scenarios in computer educational systems

Authors

DOI:

https://doi.org/10.15587/2312-8372.2016.86342

Keywords:

adaptive educational materials, e-learning, educational process, knowledge testing

Abstract

Considered dynamic scenarios in adaptive educational systems have the following disadvantages:

  • Insufficient analysis of learning estimation classifiers.
  • Little attention is given to estimation of focus attention on the material.

To address these issues in the study materials we use the works of other scientists that is devoted to adaptive methods of presentation and testing in educational systems.

Process formalization of building a knowledge base is done using semantic networks and an estimation of the minimum and maximum limit of the number of rules is obtained. The minimum number of test questions on the topic to form linear plots is within 3-5 (depending on the topic complexity), the maximum number of questions – 11.

Estimation algorithms within adaptive educational courses for learning control are implemented. Requirements to tests are formed as a part of the estimation algorithms, which include:

  • Independent of the tasks.
  • Simplicity and compactness of question formulation.
  • Exclusion of ambiguous question understanding.

The complex model of the educational process is developed using information technologies. This model includes presentation of technological, targeted, meaningful and effective system components and their relationships (substantiation of learning objectives, content modules of curriculum, conditions for achieving goals, learning activities of students and teachers, learning results). This model allows to use approaches for building of dynamic scenarios, which include the principles of non-linearity of the plot, a variety of solutions, loose problem solutions, variety of ways to pass educational courses.

Author Biographies

Євген Борисович Артамонов, National Aviation University, pr. Kosmonavta Komarova, 1, Kyiv, Ukraine, 03058

Candidate of Technical Sciences, Associate Professor

Department of Computerized Control Systems

Олександр Володимирович Панфьоров, National Aviation University, pr. Kosmonavta Komarova, 1, Kyiv, Ukraine, 03058

PhD student

Department of Computerized Control Systems

References

  1. In: Moran, D. J., Malott, R. W. (2004). Evidence-Based Educational Methods. Elsevier Science & Technology Books, 408. doi:10.1016/b978-0-12-506041-7.x5000-1
  2. Cristea, A., Aroyo, L. (2002). Adaptive Authoring of Adaptive Educational Hypermedia. Lecture Notes in Computer Science, 2347, 122–132. doi:10.1007/3-540-47952-x_14
  3. In: Spector, J. M. (2015). The SAGE Encyclopedia of Educational Technology. SAGE Publications, Inc., 604. doi:10.4135/9781483346397
  4. Chatzopoulou, D. I., Economides, A. A. (2010). Adaptive assessment of student’s knowledge in programming courses. Journal of Computer Assisted Learning, 26 (4), 258–269. doi:10.1111/j.1365-2729.2010.00363.x
  5. Eggen, T. J. H. M., Straetmans, G. J. J. M. (2000). Computerized Adaptive Testing for Classifying Examinees into three Categories. Educational and Psychological Measurement, 60 (5), 713–734. doi:10.1177/00131640021970862
  6. Stocking, M. L. (1996). An Alternative Method for Scoring Adaptive Tests. Journal of Educational and Behavioral Statistics, 21 (4), 365–389. doi:10.2307/1165340
  7. Schnipke, D. L., Green, B. F. (1995). A Comparison of Item Selection Routines in Linear and Adaptive Tests. Journal of Educational Measurement, 32 (3), 227–242. doi:10.1111/j.1745-3984.1995.tb00464.x
  8. Van der Linden, W. J. (2008). Using Response Times for Item Selection in Adaptive Testing. Journal of Educational and Behavioral Statistics, 33 (1), 5–20. doi:10.3102/1076998607302626
  9. Liu, C. M., Sun, Y. J., Li, H. Y. (2013). Adaptive Learning System Designed and Learning Program Optimization Algorithm. Applied Mechanics and Materials, 347-350, 3114–3118. doi:10.4028/www.scientific.net/amm.347-350.3114
  10. Xiao, J. Q. (2013). Research on Student Model of Adaptive Learning System Based on Semantic Web. Advanced Materials Research, 739, 562–565. doi:10.4028/www.scientific.net/amr.739.562
  11. Artamonov, E. B., Zholdakov, O. O. (2010). Concept of creating a software environment for automated text manipulation. Proceedings of National Aviation University, 44 (3), 111–115. doi:10.18372/2306-1472.44.1916
  12. Artamonov, Ye. B., Maslovskyi, B. H. (2007). Vyrishennia problemy vykorystannia yakisnoi klasyfikatsii parametriv v intelektualnykh systemakh. Elektronika i zviazok. Tematychnyi vypusk «Problemy elektroniky», Part 3, 77–79.
  13. Sato, S., Sasaki, Y. (2003). Automatic collection of related terms from the web. Proceedings of the 41st Annual Meeting on Association for Computational Linguistics – ACL’03, 2, 121–124. doi:10.3115/1075178.1075196
  14. Karpova, T. (2002). Bazy dannyh: modeli, razrabotka, realizatsiia. St. Petersburg: Piter, 304.
  15. Hahalin, G. K., Voskresenskii, A. L. (2006). Kontekstnoe fragmentirovanie v lingvisticheskom analize. Trudy X Natsional'noi konferentsii po Iskusstvennomu Intellektu s mezhdunarodnym uchastiem – KII-2006, 25-28 sentiabria 2006 g., Obninsk, Vol. 2. Moscow: Fizmatlit, 479–488.
  16. Ueno, H., Koiama, T., Okamoto, T., Matsubi, B., Isidzuka, M.; In: Ueno, H., Isidzuka, M.; Translated from Japanese: Ivanova, I. A. by ed. Volkov, N. G. (1989). Predstavlenie i ispol'zovanie znanii. Moscow: Mir, 220.

Published

2016-11-24

How to Cite

Артамонов, Є. Б., & Панфьоров, О. В. (2016). Formation of adaptive dynamic scenarios in computer educational systems. Technology Audit and Production Reserves, 6(1(32), 66–71. https://doi.org/10.15587/2312-8372.2016.86342