Organization of online learning using the intelligent metasystem of open semantic technology for intelligent systems
DOI:
https://doi.org/10.15587/1729-4061.2023.272952Keywords:
online learning, online course, data visualization method, machine learning methodAbstract
Distance learning today allows you to create a system of mass continuous self-learning, universal exchange of information, regardless of the presence of time and space zones. For more effective online learning, it is necessary to introduce artificial intelligence technologies and methods for implementing learning systems.
In this paper, the objects of research were:
1) the capabilities of the OSTIS open semantic technology, the capabilities of the IMS.OSTIS metasystem for organizing online learning;
2) machine learning method – classification.
Results of the study:
1) an online course was organized using the IMS.OSTIS metasystem of the OSTIS open semantic technology;
2) for the analysis and visualization of training data, a machine learning method is implemented – classification.
The results of the implementation of the online course were obtained using the semantic technology for designing an intelligent learning system: the IMS.OSTIS metasystem using the graphical semantic code SCg. The OSTIS kernel requires a machine with the Ubuntu operating system installed, which is a GNU/Linux distribution based on Debian GNU/Linux, an operating system based on the Linux kernel.
The paper also shows an example of using the machine learning method – classification. This method allows you to classify data. Intelligent processing and visualization of data were carried out based on the results of testing students in order to classify them into letter categories A, B, C, D according to a set of features: scores and points of the average score. The high-level Python library Pandas was used, this is a library for data analysis. To visualize the results of data processing, the Matplotlib library in Python was used
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Copyright (c) 2023 Aliya Kintonova, Amanbek Sabitov, Igor Povkhan, Dinara Khaimulina, Galymzhan Gabdreshov
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