Descriptive and predictive modeling of student achievement, satisfaction, and mental health for data-driven smart connected campus life service

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

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

1 Citation (Scopus)

Abstract

Yonsei University in Korea launched an educational innovation project entitled “Data-Driven Smart-Connected Campus Life Service”, for which student-related data have been accumulated at university level since spring of 2015, and descriptive, predictive and prescriptive modeling have been conducted to offer innovative education service to students. The dataset covers not only conventional student information, student questionnaire survey, and university administrative data, but also unconventional data sets such as student location data and learning management system (LMS) log data. Based on the datasets, with respect to 4,000+ freshman students at residential college, we conducted preliminary implementation of descriptive and predictive modeling for student achievement, satisfaction, and mental health. The results were overall promising. First, descriptive and predictive modeling of GPA for student achievement presented a list of significant predictive variables from student locations and LMS activities. Second, descriptive modeling of student satisfaction revealed influential variables such as “improvement of creativity” and “ability of cooperation”. Third, similar descriptive modeling was applied to students' mental health changes by semesters, and the study uncovered influential factors such as “difficulty with relationship” and “time spent with friends increased' as key determinants of student mental health. Although the educational innovation project is still in its early stages, we have three strategies of the future modelling efforts: They are: (1) step-by-step improvement from descriptive, predictive, to prescriptive modelling; (2) full use of recurring data acquisition; (3) different level of segmentation.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Learning Analytics and Knowledge
Subtitle of host publicationLearning Analytics to Promote Inclusion and Success, LAK 2019
PublisherAssociation for Computing Machinery
Pages531-538
Number of pages8
ISBN (Electronic)9781450362566
DOIs
Publication statusPublished - 2019 Mar 4
Event9th International Conference on Learning Analytics and Knowledge, LAK 2019 - Tempe, United States
Duration: 2019 Mar 42019 Mar 8

Publication series

NameACM International Conference Proceeding Series

Conference

Conference9th International Conference on Learning Analytics and Knowledge, LAK 2019
CountryUnited States
CityTempe
Period19/3/419/3/8

Fingerprint

Service life
Health
Students
Innovation
Data acquisition
Education

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Heo, J., Lim, H., Yun, S. B., Ju, S., Park, S., & Lee, R. (2019). Descriptive and predictive modeling of student achievement, satisfaction, and mental health for data-driven smart connected campus life service. In Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019 (pp. 531-538). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3303772.3303792
Heo, Joon ; Lim, Hyoungjoon ; Yun, Sung Bum ; Ju, Sungha ; Park, Sangyoon ; Lee, Rebekah. / Descriptive and predictive modeling of student achievement, satisfaction, and mental health for data-driven smart connected campus life service. Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019. Association for Computing Machinery, 2019. pp. 531-538 (ACM International Conference Proceeding Series).
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abstract = "Yonsei University in Korea launched an educational innovation project entitled “Data-Driven Smart-Connected Campus Life Service”, for which student-related data have been accumulated at university level since spring of 2015, and descriptive, predictive and prescriptive modeling have been conducted to offer innovative education service to students. The dataset covers not only conventional student information, student questionnaire survey, and university administrative data, but also unconventional data sets such as student location data and learning management system (LMS) log data. Based on the datasets, with respect to 4,000+ freshman students at residential college, we conducted preliminary implementation of descriptive and predictive modeling for student achievement, satisfaction, and mental health. The results were overall promising. First, descriptive and predictive modeling of GPA for student achievement presented a list of significant predictive variables from student locations and LMS activities. Second, descriptive modeling of student satisfaction revealed influential variables such as “improvement of creativity” and “ability of cooperation”. Third, similar descriptive modeling was applied to students' mental health changes by semesters, and the study uncovered influential factors such as “difficulty with relationship” and “time spent with friends increased' as key determinants of student mental health. Although the educational innovation project is still in its early stages, we have three strategies of the future modelling efforts: They are: (1) step-by-step improvement from descriptive, predictive, to prescriptive modelling; (2) full use of recurring data acquisition; (3) different level of segmentation.",
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Heo, J, Lim, H, Yun, SB, Ju, S, Park, S & Lee, R 2019, Descriptive and predictive modeling of student achievement, satisfaction, and mental health for data-driven smart connected campus life service. in Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 531-538, 9th International Conference on Learning Analytics and Knowledge, LAK 2019, Tempe, United States, 19/3/4. https://doi.org/10.1145/3303772.3303792

Descriptive and predictive modeling of student achievement, satisfaction, and mental health for data-driven smart connected campus life service. / Heo, Joon; Lim, Hyoungjoon; Yun, Sung Bum; Ju, Sungha; Park, Sangyoon; Lee, Rebekah.

Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019. Association for Computing Machinery, 2019. p. 531-538 (ACM International Conference Proceeding Series).

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

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Heo J, Lim H, Yun SB, Ju S, Park S, Lee R. Descriptive and predictive modeling of student achievement, satisfaction, and mental health for data-driven smart connected campus life service. In Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019. Association for Computing Machinery. 2019. p. 531-538. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3303772.3303792