Multifunctional and flexible online platforms for creating educational materials

A. A. Nikandrov
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引用次数: 0

Abstract

The article actualizes the need to use multifunctional flexible online platforms to promote educational activities, in particular the discipline “Machine Learning”. The main characteristic features of the discipline “Machine Learning” are described, the teaching of which consists in a task-based approach through writing program codes in a programming language, which is the Python 3 interpreter with a bundle of libraries selected: NumPy, Pandas, Matplotlib and Seaborn for data processing and visualization. The Scikit-learn library is used directly for machine learning. In addition to the Python 3 interpreter, coding tools are involved, namely: the PyCharm Community cross-platform development environment and the Jupyter Notebook open source web application. The potential of educational multifunctional flexible online platforms including designers of open online courses to facilitate independent learning of students is evaluated. According to the versions of various domestic and foreign scientific publications, the most mentioned online platforms are identified, their functionality regarding the placement of material in the fields of programming and machine learning was analyzed. Based on the analysis of the functional, a group of potential basic requirements for educational platforms in teaching programming within the discipline “Machine Learning” was identified, analyzed and discussed.
创建教育材料的多功能和灵活的在线平台
本文实现了使用多功能灵活的在线平台来促进教育活动的需求,特别是“机器学习”学科。描述了“机器学习”学科的主要特征,其教学包括基于任务的方法,通过用编程语言编写程序代码,这是Python 3解释器,选择了一系列库:NumPy, Pandas, Matplotlib和Seaborn,用于数据处理和可视化。Scikit-learn库直接用于机器学习。除了Python 3解释器外,还涉及编码工具,即:PyCharm社区跨平台开发环境和Jupyter Notebook开源web应用程序。评估了包括开放式在线课程设计者在内的教育多功能灵活在线平台促进学生自主学习的潜力。根据国内外各种科学出版物的版本,确定了提到最多的在线平台,并分析了它们在编程和机器学习领域中关于材料放置的功能。在功能分析的基础上,对“机器学习”学科中编程教学平台的一组潜在基本需求进行了识别、分析和讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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