Tuesday, April 16 at 12:30pm to 1:30pm
Hancock Foundation Building (AHF), AHF B57J
3616 Trousdale Parkway, Los Angeles, CA 90089
Spatial Computing for Sustainable Infrastructure (SCSI) Lab at Yonsei University has strived to contribute to the future from two perspectives: (1) 3D reconstruction of indoor/outdoor and (2) expansion of spatial data science. This presentation will focus on the second part – the efforts of widening the horizon of spatial data science in new fields of applications such as “Education”.
Higher Education is facing disruptive innovation that requires provision of a more effective and customized education service to individual student. 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 student-related datasets, to optimize learning performance and environments by means of personalization.
The datasets include traditional questionnaire surveys, student information, and, more recently in the wake of the big-data-analytics trend, unstructured datasets such as SNS activities, text data, and learning management system (LMS) log data of learner activities. 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.
As a new field of application, the speaker has proposed “spatial-data-driven student-characterization”, in which spatial data play a pivotal role of improving modeling quality. Based on two and half years’ data acquisition (Fall 2015 - Fall 2017) with respect to 4,000+ freshman students at residential college, SCSI lab conducted preliminary implementation of descriptive and predictive modeling for students’ achievements, satisfaction, and mental health dynamics.
The outcomes were promising enough to substantiate the value of spatial data for educational applications.