Spatial-data-driven student characterization: Trajectory sequence alignment based on student smart card transactions

Sungha Ju; Sangyoon Park; Hyoungjoon Lim; Sung Bum Yun; Joon Heo

doi:10.1145/3283590.3283591

Spatial-data-driven student characterization: Trajectory sequence alignment based on student smart card transactions

Sungha Ju, Sangyoon Park, Hyoungjoon Lim, Sung Bum Yun, Joon Heo

Department of Civil and Environmental Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018
Editors	Akihito Sudo, Lau Hoong Chin, Takahiro Yabe, Xuan Song, Yoshihide Sekimoto
Publisher	Association for Computing Machinery, Inc
Pages	1-7
Number of pages	7
ISBN (Electronic)	9781450360425
DOIs	https://doi.org/10.1145/3283590.3283591
Publication status	Published - 2018 Nov 6
Event	2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018 - Seattle, United States Duration: 2018 Nov 6 → …

Publication series

Name	Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018

Conference

Conference	2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018
Country/Territory	United States
City	Seattle
Period	18/11/6 → …

Bibliographical note

Funding Information:
This research, 'Geospatial Big Data Management, Analysis and Service Platform Technology Development', was supported by the MOLIT(The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA(Korea Agency for Infrastructure Technology Advancement) (18NSIP-B081011-05)

Funding Information:
This research, 㔀Geospatial Big Data Management, Analysis and Service Platform Technology Development 㔁? was supported by the MOLIT(The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA(Korea Agency for Infrastructure Technology Advancement) ?( ? ? -NBS ?IP ? ? - ? ? ? 爃眂I

Publisher Copyright:
© 2018 Association for Computing Machinery.

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1145/3283590.3283591

Cite this

Ju, S., Park, S., Lim, H., Yun, S. B., & Heo, J. (2018). Spatial-data-driven student characterization: Trajectory sequence alignment based on student smart card transactions. In A. Sudo, L. H. Chin, T. Yabe, X. Song, & Y. Sekimoto (Eds.), Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018 (pp. 1-7). (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018). Association for Computing Machinery, Inc. https://doi.org/10.1145/3283590.3283591

Ju, Sungha ; Park, Sangyoon ; Lim, Hyoungjoon et al. / Spatial-data-driven student characterization : Trajectory sequence alignment based on student smart card transactions. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018. editor / Akihito Sudo ; Lau Hoong Chin ; Takahiro Yabe ; Xuan Song ; Yoshihide Sekimoto. Association for Computing Machinery, Inc, 2018. pp. 1-7 (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018).

@inproceedings{876ba1d32705415181261f9b57a1bf2f,

title = "Spatial-data-driven student characterization: Trajectory sequence alignment based on student smart card transactions",

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.",

author = "Sungha Ju and Sangyoon Park and Hyoungjoon Lim and Yun, {Sung Bum} and Joon Heo",

note = "Funding Information: This research, 'Geospatial Big Data Management, Analysis and Service Platform Technology Development', was supported by the MOLIT(The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA(Korea Agency for Infrastructure Technology Advancement) (18NSIP-B081011-05) Funding Information: This research, 㔀Geospatial Big Data Management, Analysis and Service Platform Technology Development 㔁? was supported by the MOLIT(The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA(Korea Agency for Infrastructure Technology Advancement) ?( ? ? -NBS ?IP ? ? - ? ? ? 爃眂I Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018 ; Conference date: 06-11-2018",

year = "2018",

month = nov,

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doi = "10.1145/3283590.3283591",

language = "English",

series = "Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018",

publisher = "Association for Computing Machinery, Inc",

pages = "1--7",

editor = "Akihito Sudo and Chin, {Lau Hoong} and Takahiro Yabe and Xuan Song and Yoshihide Sekimoto",

booktitle = "Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018",

}

Ju, S, Park, S, Lim, H, Yun, SB & Heo, J 2018, Spatial-data-driven student characterization: Trajectory sequence alignment based on student smart card transactions. in A Sudo, LH Chin, T Yabe, X Song & Y Sekimoto (eds), Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018, Association for Computing Machinery, Inc, pp. 1-7, 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018, Seattle, United States, 18/11/6. https://doi.org/10.1145/3283590.3283591

Spatial-data-driven student characterization: Trajectory sequence alignment based on student smart card transactions. / Ju, Sungha; Park, Sangyoon; Lim, Hyoungjoon et al.
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018. ed. / Akihito Sudo; Lau Hoong Chin; Takahiro Yabe; Xuan Song; Yoshihide Sekimoto. Association for Computing Machinery, Inc, 2018. p. 1-7 (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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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Ju S, Park S, Lim H, Yun SB, Heo J. Spatial-data-driven student characterization: Trajectory sequence alignment based on student smart card transactions. In Sudo A, Chin LH, Yabe T, Song X, Sekimoto Y, editors, Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018. Association for Computing Machinery, Inc. 2018. p. 1-7. (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018). doi: 10.1145/3283590.3283591

Spatial-data-driven student characterization: Trajectory sequence alignment based on student smart card transactions

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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