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    国外适应性学习支持系统中 情境感知模型对比研究

    A Comparative Study on Context Awareness Model in Adaptive Learning System

【作      者】:

杜 静, 高博俊, 周 伟, 蔡臻昱


【关 键 词 】:

适应性学习; 学习情境; 情境感知; 学习支持; 对比分析


【栏      目】:

学习环境与资源


【中文摘要】:

依据学习者的情境提供匹配的学习资源是自适应学习系统的研究难点之一,文章采用内容分析法和案例研究法,从人与环境两方面,对2015年至2019年的国外适应性学习支持系统中情境感知功能的相关要素、实现方法、评价方式进行剖析。经过研究发现,已有的适应性学习支持系统主要应用于词汇学习和场馆学习领域;涉及要素包括用户、社会情境、任务、时空、基础设施和物理环境六大维度,并且对物理环境、社会情境与任务三个维度的分析需要进一步细化要素;不同维度要素信息获取的实现方法多采用传感器、系统日志文件、系统预设的方式;对学习支持系统评价方式主要包括用户使用系统的一般态度、用户使用体验、系统对学习成绩的影响等方面。最后,基于以上分析提出未来情境感知模型研究可以从三个方面开展,包括融入实时情感分析以实现学习支持服务中的人文关怀,支持多主体互动以促进知识的探究式社会协商,关注复杂学习情境建模以驱动数据密集型支持服务。


【英文摘要】:

Providing matching learning resources according to learners' context is one of the difficulties in the study of adaptive learning system. This paper adopts content analysis and case study to analyze the relevant elements, implementation methods and evaluation methods of context awareness function in foreign adaptive learning support system from 2015 to 2019 from two aspects of human and environment. It has been found that the existing adaptive learning support system is mainly applied to vocabulary learning and museum learning. The factors involved include six dimensions: user, social context, task, space and time, infrastructure and physical environment. The analysis of physical environment, social context and task needs to be further refined. Sensors, system log files and system presets are often used to realize the information acquisition of different dimensions. The evaluation methods of learning support system mainly include the general attitudes of users, user experience and the impact of the system on academic performance. Finally, based on the above analysis, it is proposed that the future research on context awareness model can be carried out from three aspects, including integrating real-time emotional analysis to realize life care in learning support services, supporting multi-agent interaction to promote exploratory social consultation of knowledge, and focusing on complex learning context modeling to drive data-intensive support services.

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