The Use of Neural Network Technologies Using the Example of the Domestic Software Platform DeepTalk in the Educational Process of PGUPS at the Department “Information and Computing Systems”

Sergey Ermakov, Natal'ya Shed'ko
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Abstract

Purpose: To develop a methodology for using artificial intelligence to solve the problem of intensifying the process of developing students’ professional competencies. To show the need to improve teaching technologies due to a reduction in classroom time allocated to general education disciplines when reconfiguring the curricula for training specialists. To consider the issue of rethinking the effectiveness of the existing distance learning system (DLS) as a means of rationalizing educational procedures in connection with the continuing risk of students acquiring only a minimum level of knowledge due to the use of template electronic demonstration materials in the DLS. Methods: To achieve the stated goals, data from the analysis of the results of the implementation of the “Digital Teacher” (DT) product, developed on the basis of the domestic DeepTalk platform, into the educational process of the ICS Department are used. An algorithm for training a neural network as a mathematical core of the DT has been developed. When used at the first stages of loading the DT with text and presentation materials, the principle of randomization of logically related queries, answers and comments from teachers on lectures and practical assignments is implemented. Results: A technique for iterative adjustment of the DT has been developed and tested when increasing the array of situational data with the following elimination of the contradiction between the requirement for representativeness and the initially small volume of the generated training sample. Practical significance: The creation of a developed neural network product is the first step in the deployment of the University’s digital services system, the hierarchical structure of which according to areas of training, qualifications and specializations should include “Digital Teacher”, “Digital Applicant Curator” and other similar products. The introduction of DT into providing teaching in senior years will contribute to the formation of the student’s individual educational trajectory and the development of his cognitive abilities. Further, in the course of obtaining additional professional education, the use of DeepTalk will ensure the acquisition of skills in interacting with AI, used as a means of supporting decision-making under conditions of uncertainty. DeepTalk allows you to assess the predisposition of applicants to the main types of professional activities at JSCo “Russian Railways”.
以 "信息与计算系统 "系 PGUPS 的教学过程中使用的国内软件平台 DeepTalk 为例,介绍神经网络技术的使用情况
目的:开发一种使用人工智能的方法,以解决强化学生专业能力培养过程的问题。说明在重新配置培养专业人才的课程时,由于分配给普通教育学科的课堂时间减少,需要改进教学技术。反思现有远程学习系统(DLS)的有效性,将其作为合理化教学程序的一种手段,因为在远程学习系统中使用模板电子演示材料,学生可能只能获得最低水平的知识。方法:为了实现既定目标,我们使用了对在国内 DeepTalk 平台基础上开发的 "数字教师"(DT)产品在综合学科系教学过程中的实施结果进行分析的数据。作为 DT 的数学核心,开发了一种训练神经网络的算法。在将文本和演示材料加载到 DT 的第一阶段使用时,执行了教师对讲座和实践作业提出的逻辑相关的询问、回答和评论的随机化原则。结果:在增加情景数据阵列时,开发并测试了迭代调整 DT 的技术,从而消除了对代表性的要求与最初生成的少量训练样本之间的矛盾。实际意义:创建开发的神经网络产品是部署大学数字服务系统的第一步,根据培训领域、资格和专业,该系统的层次结构应包括 "数字教师"、"数字申请人馆长 "和其他类似产品。在高年级教学中引入 DT 将有助于学生个人教育轨迹的形成和认知能力的发展。此外,在接受更多专业教育的过程中,DeepTalk 的使用将确保学生获得与人工智能互动的技能,作为在不确定条件下支持决策的一种手段。通过DeepTalk,您可以评估申请人对 "俄罗斯铁路 "股份公司主要职业活动类型的倾向性。
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