RADIOELECTRONIC AND COMPUTER SYSTEMS

The subject of research in the article is the process of intelligent computer training in engineering skills. The aim is to model the process of teaching engineering skills in intelligent computer training programs through dynamic Bayesian networks. Objectives: To propose an approach to modeling the process of teaching engineering skills. To assess the student competence level by considering the algorithms development skills in engineering tasks and the algorithms implementation ability. To create a dynamic Bayesian network structure for the learning process. To select values for conditional probability tables. To solve the problems of filtering, forecasting, and retrospective analysis. To simulate the developed dynamic Bayesian network using a special Genie 2.0-environment. The methods used are probability theory and inference methods in Bayesian networks. The following results are obtained: the development of a dynamic Bayesian network for the educational process based on the solution of engineering problems is presented. Mathematical calculations for probabilistic inference problems such as filtering, forecasting, and smoothing are considered. The solution of the filtering problem makes it possible to assess the current level of the student's competence after obtaining the latest probabilities of the development of the algorithm and its numerical calculations of the task. The probability distribution of the learning process model is predicted. The number of additional iterations required to achieve the required competence level was estimated. The retrospective analysis allows getting a smoothed assessment of the competence level, which was obtained after the task's previous instance completion and after the computation of new additional probabilities characterizing the two checkpoints implementation. The solution of the described probabilistic inference problems makes it possible to provide correct information about the learning process for intelligent computer training systems. It helps to get proper feedback and to track the student's competence level. The developed technique of the kernel of probabilistic inference can be used as the decision-making model basis for an automated training process. The scientific novelty lies in the fact that dynamic Bayesian networks are applied to a new class of problems related to the simulation of engineering skills training in the process of performing algorithmic tasks.

dynamic Bayesian network; modeling; engineering skills; forecasting; filtering; smoothing

Murray, C. R., VanLehn, K., Mostow, J. Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach. International Journal of Artificial Intelligence in Education, 2004, vol. 14, no. 3,4, pp. 235-278. HAL Id: hal-00197308.

Hooshyar, D., Pedaste, M., Saks, K., Leijen, Ä., Bardone, E., Wang, M. Open learner models in supporting self-regulated learning in higher education: A systematic literature review. Computers & Education, 2020, vol. 154, pp. 103-108, Article Id: 103878. DOI: 10.1016/j.compedu.2020.103878.

Somyürek, S., Brusilovsky, P., Çebi, A., Akhüseyinoğlu, K., Güyer, T. How Do Students Perceive Their Own and Their Peers’ Progress in E-Learning? International Journal of Information and Learning Technology, 2021, vol. 38, no. 1, pp. 49-74. DOI: 10.1108/IJILT-05-2020-0073.

Corbeil J. P., Gagnon, M., Richard, P. R. Probabilistic Approaches to Detect Blocking States in Intelligent Tutoring System. Proceedings on 16th International Conference on Intelligent Tutoring Systems, Athens, Greece, June 8–12, 2020, pp. 79-88. DOI: /10.1007/978-3-030-49663-0_11.

Käser, T., Klingler, S., Schwing, A. G., Gross, M. Dynamic Bayesian Networks for Student Modeling. IEEE Transactions on Learning Technologies, 2017, vol. 10, no. 4, pp. 450-462. DOI: 10.1109/TLT.2017.2689017.

Green, D. T., Walsh, T. J., Cohen, P. R., & Chang, Y. H. Learning a skill-teaching curriculum with dynamic bayes nets. Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, 2011, San Francisco, California, USA, pp. 1648-1654.

McNaught, K., Zagorecki, A. Using Dynamic Bayesian Networks for Prognostic Modelling to Inform Maintenance Decision Making. IEEE International Conference on Industrial Engineering and Engineering Management, 2009, pp. 1155-1159.

Aleven, V., Koedinger, K. Limitations of student control: Do students know when they need help? Intelligent Tutoring Systems: 5th International Conference ITS, 2000, Montreal, Canada, pp. 292-303.

Mitrovic, A. An intelligent SQL tutor on the web. International Journal of Artificial Intelligence in Education, 2003, vol. 13, no. 3-4, pp. 173-197.

Kulik, A., Chukhray, A., Pedan S., Anzenberger, P. Development of the automated laboratory practical work at the course «Modeling of systems». Proceedings of the Interactive Computer Aided Learning Conference, September 24-26, 2008, Villach, Austria, pp. 1-11.

Marek, J. D. GeNIe: A Development Environment for Graphical Decision-analytic Models. Proceedings of the Annual Symposium of the American Medical Informatics Association, Washington, D.C., 1999, Corpus ID: 29592997, pp. 1206.

Dotsenko, S. I. Intelektual'ni systemy: pryntsypy evrystychnoyi samoorhanizatsiyi protsesiv smyslovoho myslennya ta smyslovoyi diyal'nosti [Intellectual systems: principles of the heuristic self-organization of the processes of sense thinking and sense activity]. Radioelektronni i komp'uterni sistemi – Radioelectronic and computer systems, 2020, no. 2(94), pp. 4-22. DOI: 10.32620/reks.2020.2.01.

Dotsenko, S. I. Intelektual'ni systemy: pryntsypy evrystychnoyi samoorhanizatsiyi [Intellectual systems: principles of the heuristic self-organization]. Radioelektronni i komp'uterni sistemi – Radioelectronic and computer systems, 2020, no. 1(93), pp. 4-16. DOI: 10.32620/reks.2020.1.01.

Gaydachuk, D., Havrylenko, O., Martínez Bastida, J. P., Chukhray, A. Structural diagnosis method for computer programs developed by trainees. Proceedings of 15th International Conference on ICT in Education, Research, and Industrial Applications, 2019, Kherson, Ukraine, pp. 485-490.

Martínez Bastida, J. P., Havrykenko, O., Chukhray, A. Developing a self-regulation environment in an open learning model with higher fidelity assessment. Communications in Computer and Information Science, 2018, vol. 826, pp. 112-131. DOI: 10.1007/978-3-319-76168-8_6.