Explore Ways to Study Effectively in Groups from Data Science

Zhao Chenyang
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Abstract

Recently, there is a global outbreak of COVID-19. As a result, many students had to choose distance education. In this situation, learning in a group is more difficult to realize. What's more, teamwork is a vital part of learning. Because it is beneficial for students to learn how to work together in the future. For example, if the major of students is software engineering, working in group is their daily working way. When they are students, mastering how to work in group is crucial to their careers. Consequently, it is more important to find methods to study in groups effectively now. The objective of this article is to find the effective means of group learning which can help students to improve their grade, especially in software engineering. In addition, this article will analyze the method according to the following aspects, the count of issue, the count of the commit, the responses of students, the help hours, the meeting hours, the personal meeting hours, the count of the team and the sex ratio in the team. Data visualization and Machine learning will be used to deal with the relevant data. The effective means will be found by analyzing these data and other references.
从数据科学探索有效小组学习的方法
最近,全球爆发了新冠肺炎疫情。因此,许多学生不得不选择远程教育。在这种情况下,小组学习更难实现。更重要的是,团队合作是学习的重要组成部分。因为这对学生学习如何在未来一起工作是有益的。例如,如果学生的专业是软件工程,在小组中工作是他们的日常工作方式。当他们还是学生的时候,掌握如何在团队中工作对他们的职业生涯至关重要。因此,现在更重要的是找到有效的小组学习方法。本文的目的是寻找有效的小组学习方法,以帮助学生提高成绩,特别是在软件工程方面。此外,本文还将从问题数、提交数、学生反馈数、帮助时间、会议时间、个人会议时间、团队人数、团队性别比例等方面对该方法进行分析。数据可视化和机器学习将用于处理相关数据。通过对这些数据和其他参考文献的分析,找到有效的方法。
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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