空间计算应用于教育及其他领域:语义轨迹能表征学生吗?

J. Heo, Sanghyun Yoon, Won Seob Oh, J. Ma, Sungha Ju, S. Yun
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引用次数: 6

摘要

空间大数据(SBD)已经在许多领域得到了应用,我们提出了将SBD分析应用于延世大学松岛国际校区本科生语义轨迹数据的教育。高等教育面临着颠覆性创新的压力,为了在即将到来的激烈浪潮中生存下来,高校不仅要努力提供更好的教育,还要努力为每一位学生提供量身定制的服务。整个研究计划是为教育、安全、健康和校园管理提供一个具有SBD分析的智慧校园,本研究由四个具体项目组成:(1)制作项目现场的3D地图;(2)基于班级出勤记录、宿舍门禁记录等构建语义轨迹;(3)收集学生的教学参数和其他参数;(4)寻找轨迹模式与教学特征的关系。本研究的成功完成将为利用语义轨迹预测学生成绩和特征,进而走向积极主动的学生关怀系统和学生活动指导系统,树立一个里程碑。它最终可以为参与的学生提供更好的定制教育服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial computing goes to education and beyond: can semantic trajectory characterize students?
Spatial big data (SBD) has been utilized in many fields and we propose SBD analytics to apply to education with semantic trajectory data of undergraduate students in Songdo International Campus at Yonsei University. Higher education is under a pressure of disruptive innovation, so that colleges and universities strive to provide not only better education but also customized service to every single student, for a matter of survival in upcoming drastic wave. The entire research plan is to present a smart campus with SBD analytics for education, safety, health, and campus management, and this research is composed of four specific items: (1) to produce 3D mapping for project site; (2) to build semantic trajectory based on class attendance records, dorm gate entry records, etc.; (3) to collect pedagogical and other parameters of students; (4) to find relationship among trajectory patterns and pedagogical characteristics. Successful completion of the research would set a milestone to use semantic trajectory to predict student performance and characteristics, even further to go to proactive student care system and student activity guiding system. It can eventually present better customized education services to participating students.
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