Spatial-data-driven student characterization in higher education

Joon Heo, Chung Kyong-Mee, Sanghyun Yoon, Sung Bum Yun, Jong Won Ma, Sungha Ju

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

3 Citations (Scopus)

Abstract

Higher Education is facing disruptive innovation that requires, among other things, provision of a more effective and customized education service to individual students. In the field of Learning Analytics (LA), there has been much effort, in the form of the collection and thorough analysis of a variety of studentrelated datasets, to optimize learning performance and environments by means of personalization. The datasets include traditional questionnaire surveys, learning management system (LMS) log data of learner activities, and, more recently in the wake of the big-data-analytics trend, unstructured datasets such as SNS activities, text data, and other transactional data. Spatial data, however, is rarely considered as a key dataset, despite its high potential for characterization of students and prediction of their performances and conditions. In this context, the authors propose a new, spatial-data-driven student-characterization research framework. This vision paper describes spatial computing in its three, descriptive, predictive, and prescriptive modeling stages as well as its three challenges: (1) technical spatial data acquisition issues; (2) legal and administrative issues; (3) expansion of the application domains in which spatial data can contribute to improved modeling quality. With respect to each challenge, the on-going efforts are briefly introduced in order to substantiate the feasibility of the proposed research framework.

Original languageEnglish
Title of host publicationProceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility, PredictGIS 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450355018
DOIs
Publication statusPublished - 2017 Nov 7
Event1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility, PredictGIS 2017 - Redondo Beach, United States
Duration: 2017 Nov 72017 Nov 10

Publication series

NameProceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility, PredictGIS 2017
Volume2017-January

Other

Other1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility, PredictGIS 2017
CountryUnited States
CityRedondo Beach
Period17/11/717/11/10

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Transportation
  • Control and Systems Engineering

Cite this

Heo, J., Kyong-Mee, C., Yoon, S., Yun, S. B., Ma, J. W., & Ju, S. (2017). Spatial-data-driven student characterization in higher education. In Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility, PredictGIS 2017 [1] (Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility, PredictGIS 2017; Vol. 2017-January). Association for Computing Machinery, Inc. https://doi.org/10.1145/3152341.3152343