Electronic schedule at the university on the basis of 1C:Automated scheduling. University on the example of Omsk State University

IF 2.1 Q1 EDUCATION & EDUCATIONAL RESEARCH
T. A. Deyneko, O. L. Epanchintseva, A. Rodyukov
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引用次数: 0

Abstract

Automating scheduling is a classic task in learning management systems. The process of scheduling is, in a sense, the final one in the chain of support of educational activities, and its automation reveals all inconsistencies and shortcomings of the previous processes. In order for the scheduling to be automated as much as possible, a lot of various initial information should be processed in the information systems of the university — curricula, staff, workload, schedule of the educational process, contingent of students, classroom fund. It is especially difficult for universities with educational programs of various orientations — natural science, humanitarian, creative, etc., which have specific principles for organizing and conducting classes.The level of automation of educational activities at Dostoevsky Omsk State University, a classical university with a wide variety of types of educational programs, made it possible to tackle the task of scheduling. However, during the implementation of the automated scheduling system, the project team faced a number of problems.The article describes the results of the project for the transition to an electronic schedule in Dostoevsky Omsk State University using the circulation software product 1C:Automated scheduling. University based on the 1C:Enterprise 8.3 system. Initial data on the individual workload of teachers, the classroom fund, the list of student groups, and the list of disciplines were loaded into the configuration from the existing information system of design of Dostoevsky Omsk State University. Based on the results of the audit of the downloaded reference information, the initial data, including curricula, were normalized. The compiled schedule in two modes (manual and automatic) was published on the official website of the university and is used to operate a chatbot on the VKontakte network to inform students and teachers about upcoming classes.
在1C基础上的大学电子排课:自动排课。以鄂木斯克国立大学为例
自动化调度是学习管理系统中的一项经典任务。从某种意义上说,调度过程是教育活动支持链中的最后一个过程,它的自动化揭示了以前过程的所有不一致和缺点。为了使调度尽可能地自动化,在大学的信息系统中需要处理大量的初始信息——课程、人员、工作量、教育过程的日程安排、学生队伍、教室资金。这对于拥有自然科学、人文主义、创造等多种教育方向的大学来说尤其困难,这些大学有特定的组织和授课原则。陀思妥耶夫斯基鄂木斯克国立大学是一所拥有各种类型教育项目的古典大学,其教育活动的自动化水平使解决日程安排的任务成为可能。然而,在自动化调度系统的实施过程中,项目团队面临着许多问题。本文描述了陀思妥耶夫斯基鄂木斯克州立大学使用流通软件产品1C:自动调度向电子调度过渡的项目结果。大学基于1C:企业8.3系统。关于教师个人工作量、课堂资金、学生群体列表和学科列表的初始数据从陀思妥耶夫斯基鄂木斯克国立大学现有的设计信息系统中加载到配置中。根据对下载的参考资料的审计结果,对包括课程在内的初始数据进行了标准化。这份编制好的时间表有手动和自动两种模式,发布在学校的官方网站上,用于操作VKontakte网络上的聊天机器人,通知学生和老师即将到来的课程。
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来源期刊
Informatics in Education
Informatics in Education EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
6.10
自引率
3.70%
发文量
20
审稿时长
20 weeks
期刊介绍: INFORMATICS IN EDUCATION publishes original articles about theoretical, experimental and methodological studies in the fields of informatics (computer science) education and educational applications of information technology, ranging from primary to tertiary education. Multidisciplinary research studies that enhance our understanding of how theoretical and technological innovations translate into educational practice are most welcome. We are particularly interested in work at boundaries, both the boundaries of informatics and of education. The topics covered by INFORMATICS IN EDUCATION will range across diverse aspects of informatics (computer science) education research including: empirical studies, including composing different approaches to teach various subjects, studying availability of various concepts at a given age, measuring knowledge transfer and skills developed, addressing gender issues, etc. statistical research on big data related to informatics (computer science) activities including e.g. research on assessment, online teaching, competitions, etc. educational engineering focusing mainly on developing high quality original teaching sequences of different informatics (computer science) topics that offer new, successful ways for knowledge transfer and development of computational thinking machine learning of student''s behavior including the use of information technology to observe students in the learning process and discovering clusters of their working design and evaluation of educational tools that apply information technology in novel ways.
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