Sistema otsenivaniya kompetentsii komandnoj raboty budushhikh spetsialistov IT-sfery [The system for assessing the teamwork competency of future IT specialists]

IF 2.1 Q1 EDUCATION & EDUCATIONAL RESEARCH
E. S. Vaseva, N. V. Buzhinskaya
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引用次数: 3

Abstract

The article highlights the need to prepare future IT specialists for teamwork. The system for assessing the teamwork competency is proposed. The system includes a set of indicators, which are divided into invariant and variable. Each indicator is characterized by a grading scale with three possible assessment options: “not mastered” — 0, “partially mastered” — 1, “mastered” — 2. In the context of “indicator — assessment”, the required standard of student behavior in the process of performing team activities was determined. Invariant indicators are used to assess the teamwork competency, regardless of the role played by the student. The invariant indicators are indicated: understanding the importance of the task for the team's activities, the distribution of roles in the team, team interaction, the assessment of the teamwork. To determine the variable indicators, an analysis of the existing methods for the distribution ofcommand roles was carried out, the types of activities of an IT specialist were studied. To solve problems related to the implementation of activities for the design, development and maintenance of software tools within the framework of teamwork, it is advisable to distinguish six roles: analyst, innovator, negotiator, designer, executor, coordinator. Based on these roles, variable indicators are defined. The division of indicators into invariant and variable implies different requirements for the level of development. Invariant indicators must be mastered by all, while variable indicators allow for partial mastering in some indicators. The use of the system for assessing the teamwork competency will reveal potential problems in the preparation of a future IT specialist, clearly define the directions of the student's development, and form recommendations for him regarding professional activities.
Sistema otsenivaniya konpetentsii komandnoj raboty budushhikh specialistov IT security[评估未来IT专家团队合作能力的系统]
这篇文章强调了让未来的IT专家为团队合作做好准备的必要性。提出了团队合作能力评价体系。该体系包括一套指标,分为不变指标和可变指标。每个指标都有一个评分量表,有三种可能的评估选项:“不掌握”- 0,“部分掌握”- 1,“掌握”- 2。在“指标-评价”的背景下,确定学生在进行团队活动过程中所需要的行为标准。无论学生所扮演的角色如何,都使用不变指标来评估团队合作能力。指出不变指标:理解任务对团队活动的重要性,团队中角色的分配,团队互动,团队合作的评估。为了确定可变指标,对现有的命令角色分配方法进行了分析,并研究了IT专家的活动类型。为了在团队合作的框架内解决与软件工具的设计、开发和维护活动的实施相关的问题,建议区分六个角色:分析师、创新者、谈判者、设计师、执行者、协调者。根据这些角色,定义可变指标。将指标分为不变指标和可变指标意味着对发展水平的不同要求。不变指标必须由所有人掌握,而可变指标允许部分掌握某些指标。运用这个系统来评估团队合作能力,可以发现在培养未来资讯科技专家时可能遇到的问题,清楚界定学生的发展方向,并就专业活动提出建议。
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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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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