research-article

Spatial-Data-Driven Student Characterization: Trajectory Sequence Alignment based on Student Smart Card Transactions

Authors:

Hyoungjoon Lim,

Joon HeoAuthors Info & Claims

PredictGIS 2018: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility

Pages 1 - 7

https://doi.org/10.1145/3283590.3283591

Published: 06 November 2018 Publication History

Abstract

Analyzing students' characteristic can provide much information for campus planning, education design and student management. This study built students' sequential trajectories based on student smart card transactions and calculate similarity scores for finding relationship between students' trajectories and academic performance. The data used in this study are student smart card transaction data and attendance information of Yonsei university Songdo campus students. Based on this, the trajectory of each student is created into daily context sequence and connected in semester unit. In order to calculate the similarity of one semester trajectory between two students, Needleman-Wunsch Algorithm, which is mainly used for comparison of the DNA nucleotide sequences of two different species, was applied. The similarity score of trajectory sequences for student pair were calculated for 685 students in spring semester. For finding relation with academic performance, authors divided students into two groups; one group with high similarity score for both students in the pair and the other with pair of students with low similarity score. 2-sample T-test was conducted afterward in to determine whether the GPA of these groups were different form the overall distribution of student GPA. As a result, the mean value of GPA of the students with low similarity scores were statistically significantly lower than the overall mean value of GPA. This means that the trajectory sequence of students with lower GPA is less similar than the other students. The results of this study indicate that trajectory information based on spatial data is related to characteristics such as student academic achievement, and it is possible to analyze characteristics of students through spatial trajectory sequence information.

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Cited By

Čavojský MDrozda MBalogh Z(2021)Analysis and experimental evaluation of the Needleman-Wunsch algorithm for trajectory comparisonExpert Systems with Applications10.1016/j.eswa.2020.114068165(114068)Online publication date: Mar-2021
https://doi.org/10.1016/j.eswa.2020.114068
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

Index Terms

Spatial-Data-Driven Student Characterization: Trajectory Sequence Alignment based on Student Smart Card Transactions
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Similarity measures

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Published In

cover image ACM Conferences

PredictGIS 2018: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility

November 2018

50 pages

ISBN:9781450360425

DOI:10.1145/3283590

Editors:
Akihito Sudo
Shizuoka University, Japan
,
Yoshihide Sekimoto
University of Tokyo, Japan
,
Lau Hoong Chin
Singapore Management University, Singapore
,
Xuan Song
University of Tokyo, Japan
,
Takahiro Yabe
Purdue University, IN, USA

Copyright © 2018 ACM.

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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2018

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The Ministry of Land, Infrastructure and Transport Korea

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SIGSPATIAL '18

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SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6, 2018

WA, Seattle, USA

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Citations

Cited By

Čavojský MDrozda MBalogh Z(2021)Analysis and experimental evaluation of the Needleman-Wunsch algorithm for trajectory comparisonExpert Systems with Applications10.1016/j.eswa.2020.114068165(114068)Online publication date: Mar-2021
https://doi.org/10.1016/j.eswa.2020.114068
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

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