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Spatial-Data-Driven Student Characterization in Higher Education

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Sungha JuAuthors Info & Claims

PredictGIS'17: Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility

Article No.: 1, Pages 1 - 4

https://doi.org/10.1145/3152341.3152343

Published: 07 November 2017 Publication History

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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 student-related 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.

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Cited By

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Khor EK M(2023)A Systematic Review of the Role of Learning Analytics in Supporting Personalized LearningEducation Sciences10.3390/educsci1401005114:1(51)Online publication date: 31-Dec-2023
https://doi.org/10.3390/educsci14010051
Koteich JMitton N(2023)Machine Learning Approach for Mobility Context Classification Using Radio Beacons2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)10.1109/MASCOTS59514.2023.10387635(1-8)Online publication date: 16-Oct-2023
https://doi.org/10.1109/MASCOTS59514.2023.10387635
Carow SSchmitz PPretorius R(2023)Spatial perspectives on student profiling to inform open distance e-learning (ODeL) in various geographical contexts: a case study from the Global SouthDiscover Sustainability10.1007/s43621-023-00143-94:1Online publication date: 26-Jun-2023
https://doi.org/10.1007/s43621-023-00143-9
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Spatial-Data-Driven Student Characterization in Higher Education
1. Applied computing

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Published In

PredictGIS'17: Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility

November 2017

51 pages

ISBN:9781450355018

DOI:10.1145/3152341

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2017

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SIGSPATIAL'17

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SIGSPATIAL

SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 7 - 10, 2017

CA, Redondo Beach, USA

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Cited By

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Khor EK M(2023)A Systematic Review of the Role of Learning Analytics in Supporting Personalized LearningEducation Sciences10.3390/educsci1401005114:1(51)Online publication date: 31-Dec-2023
https://doi.org/10.3390/educsci14010051
Koteich JMitton N(2023)Machine Learning Approach for Mobility Context Classification Using Radio Beacons2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)10.1109/MASCOTS59514.2023.10387635(1-8)Online publication date: 16-Oct-2023
https://doi.org/10.1109/MASCOTS59514.2023.10387635
Carow SSchmitz PPretorius R(2023)Spatial perspectives on student profiling to inform open distance e-learning (ODeL) in various geographical contexts: a case study from the Global SouthDiscover Sustainability10.1007/s43621-023-00143-94:1Online publication date: 26-Jun-2023
https://doi.org/10.1007/s43621-023-00143-9
Khalil MPrinsloo PSlade SWise AMartinez-Maldonado RHilliger I(2022)A Comparison of Learning Analytics Frameworks: a Systematic ReviewLAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506878(152-163)Online publication date: 21-Mar-2022
https://dl.acm.org/doi/10.1145/3506860.3506878
Lim HKim SChung KLee KKim THeo J(2021)Is college students’ trajectory associated with academic performance?Computers & Education10.1016/j.compedu.2021.104397178:COnline publication date: 29-Dec-2021
https://dl.acm.org/doi/10.1016/j.compedu.2021.104397
Griffiths SWong MKwok CKam RLam SYang LYip THeo JChan BXiong GLu K(2019)Exploring Bluetooth Beacon Use Cases in Teaching and Learning: Increasing the Sustainability of Physical Learning SpacesSustainability10.3390/su1115400511:15(4005)Online publication date: 24-Jul-2019
https://doi.org/10.3390/su11154005
Heo JLim HYun SJu SPark SLee RHsiao SCunningham JMcCarthy KLynch GBrooks CFerguson RHoppe U(2019)Descriptive and Predictive Modeling of Student Achievement, Satisfaction, and Mental Health for Data-Driven Smart Connected Campus Life ServiceProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303792(531-538)Online publication date: 4-Mar-2019
https://dl.acm.org/doi/10.1145/3303772.3303792
Ju SPark SLim HYun SHeo J(2018)Spatial-Data-Driven Student CharacterizationProceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility10.1145/3283590.3283591(1-7)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3283590.3283591

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