Spatial computing goes to education and beyond: Can semantic trajectory characterize students?

Joon Heo, Sanghyun Yoon, Won Seob Oh, Jong Won Ma, Sungha Ju, Sung Bum Yun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2016
EditorsRanga Raju Vatsavai, Varun Chandola
PublisherAssociation for Computing Machinery, Inc
Pages14-17
Number of pages4
ISBN (Electronic)9781450345811
DOIs
Publication statusPublished - 2016 Oct 31
Event5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2016 - San Francisco, United States
Duration: 2016 Oct 31 → …

Publication series

NameProceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2016

Other

Other5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2016
Country/TerritoryUnited States
CitySan Francisco
Period16/10/31 → …

Bibliographical note

Publisher Copyright:
© 2016 ACM.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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