CAJ | 학술논문

近年来，随着智慧学习环境的飞速发展，海量、丰富、多样、异构的学习数据急剧积累，如何有效地利用这些数据引起了学界的广泛关注。学习分析技术应运而生，并成为研究热点。学习分析工具在学习分析过程中具有举足轻重的作用，好的研究工具可以使研究过程事半功倍。本文从多个角度对学习分析工具进行了分类，并从使用环境、数据支持格式、是否可视化等维度详细比较，讨论了24种学习分析工具的特点，旨在为学习分析提供相应的技术支持。
근년래，수착지혜학습배경적비속발전，해량、봉부、다양、이구적학습수거급극적루，여하유효지이용저사수거인기료학계적엄범관주。학습분석기술응운이생，병성위연구열점。학습분석공구재학습분석과정중구유거족경중적작용，호적연구공구가이사연구과정사반공배。본문종다개각도대학습분석공구진행료분류，병종사용배경、수거지지격식、시부가시화등유도상세비교，토론료24충학습분석공구적특점，지재위학습분석제공상응적기술지지。
In recent years, with the rapid development of a smart learning environment, massive, rich, diverse, and heterogeneous data are increasing amazingly. In education field, students’ interests, preferences, activities, learning process information, such as the interaction with learning platform, as well as their implicit feedback to the e-learning platform, can all be recorded and traced. How to effectively make use of these data has drawn great concern. The da-ta of a single person seems to be chaotic, but with the data accumulating to a certain extent, it will be presented in an order. There are strong or weak relations among the data. For example, what are the characteristics of students in dif-ferent region or countries? What are the characteristics of learning behavior in different ages? What are the learning habits of different students? Which courses are needed urgently for a successful career? For a special course, which u-nits are needed for review? Which units are needed to be emphasized? Which students encounter difficulties and need help? Therefore there are amazing insights behind the data. If we extract the rules or determine the relationships among data, tremendous value will be created. Therefore, learning analysis techniques arise. According to the Horizon Report 2011 in the New Media Consortiums Horizon Project, learning analytics technol-ogy will become a hot topic in the next few years. It will contribute to improving the learning process and make the learning more intelligent. As we can imagine, the analytics tools play an important role in the process of learning an-alytics. Good tools can make the research process more effective. Many analytics tools have been developed. For example, Nvivo, Atlas. ti can be used to annotate the text and multimedia content. Gephi, JUNG, Guess can be used to analyze learning networks, and SPSS can analyze user data statistics. However, a key issue is how to choose the appropriate tool because different tools will have different func-tions. Previous studies conducted analysis on the analytic tools, which are fragmented and focused on the comparison of learning network analytics tools. Different from previous works, this paper categorizes the learning analytics tools from multiple perspectives, and compares the tools from the point of developer, application environment, users, the supported data format, visualization, advantages, disadvantages, and so on. Twenty-four tools are discussed in de-tail in order to provide proper technical support for learning analytics and help to make education more intelligent and effectively.