Student network analysis: A novel way to predict delayed graduation in higher education

Nasheen Nur; Noseong Park; Mohsen Dorodchi; Wenwen Dou; Mohammad Javad Mahzoon; Xi Niu; Mary Lou Maher

doi:10.1007/978-3-030-23204-7_31

Student network analysis: A novel way to predict delayed graduation in higher education

Nasheen Nur, Noseong Park, Mohsen Dorodchi, Wenwen Dou, Mohammad Javad Mahzoon, Xi Niu, Mary Lou Maher

Graduate School

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

6 Citations (Scopus)

Abstract

We present a prediction model to detect delayed graduation cases based on student network analysis. In the U.S. only 60% of undergraduate students finish their bachelors’ degrees in 6 yearsÂ [1]. We present many features based on student networks and activity records. To our knowledge, our feature design, which includes conventional academic performance features, student network features, and fix-point features, is one of the most comprehensive ones. We achieved the F-1 score of 0.85 and AUCROC of 0.86.

Original language	English
Title of host publication	Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings
Editors	Seiji Isotani, Eva Millán, Amy Ogan, Bruce McLaren, Peter Hastings, Rose Luckin
Publisher	Springer Verlag
Pages	370-382
Number of pages	13
ISBN (Print)	9783030232030
DOIs	https://doi.org/10.1007/978-3-030-23204-7_31
Publication status	Published - 2019
Event	20th International Conference on Artificial Intelligence in Education, AIED 2019 - Chicago, United States Duration: 2019 Jun 25 → 2019 Jun 29

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11625 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Artificial Intelligence in Education, AIED 2019
Country/Territory	United States
City	Chicago
Period	19/6/25 → 19/6/29

Bibliographical note

Funding Information:
Acknowledgements. This work is supported by the National Science Foundation under Grant No. 1820862. Noseong Park and Mohsen Dorodchi are the co-corresponding authors.

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-23204-7_31

Cite this

Nur, N., Park, N., Dorodchi, M., Dou, W., Mahzoon, M. J., Niu, X., & Maher, M. L. (2019). Student network analysis: A novel way to predict delayed graduation in higher education. In S. Isotani, E. Millán, A. Ogan, B. McLaren, P. Hastings, & R. Luckin (Eds.), Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings (pp. 370-382). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11625 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-23204-7_31

Nur, Nasheen ; Park, Noseong ; Dorodchi, Mohsen et al. / Student network analysis : A novel way to predict delayed graduation in higher education. Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings. editor / Seiji Isotani ; Eva Millán ; Amy Ogan ; Bruce McLaren ; Peter Hastings ; Rose Luckin. Springer Verlag, 2019. pp. 370-382 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We present a prediction model to detect delayed graduation cases based on student network analysis. In the U.S. only 60% of undergraduate students finish their bachelors{\textquoteright} degrees in 6 years{\^A} [1]. We present many features based on student networks and activity records. To our knowledge, our feature design, which includes conventional academic performance features, student network features, and fix-point features, is one of the most comprehensive ones. We achieved the F-1 score of 0.85 and AUCROC of 0.86.",

author = "Nasheen Nur and Noseong Park and Mohsen Dorodchi and Wenwen Dou and Mahzoon, {Mohammad Javad} and Xi Niu and Maher, {Mary Lou}",

note = "Funding Information: Acknowledgements. This work is supported by the National Science Foundation under Grant No. 1820862. Noseong Park and Mohsen Dorodchi are the co-corresponding authors. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 20th International Conference on Artificial Intelligence in Education, AIED 2019 ; Conference date: 25-06-2019 Through 29-06-2019",

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doi = "10.1007/978-3-030-23204-7_31",

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Nur, N, Park, N, Dorodchi, M, Dou, W, Mahzoon, MJ, Niu, X & Maher, ML 2019, Student network analysis: A novel way to predict delayed graduation in higher education. in S Isotani, E Millán, A Ogan, B McLaren, P Hastings & R Luckin (eds), Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11625 LNAI, Springer Verlag, pp. 370-382, 20th International Conference on Artificial Intelligence in Education, AIED 2019, Chicago, United States, 19/6/25. https://doi.org/10.1007/978-3-030-23204-7_31

Student network analysis: A novel way to predict delayed graduation in higher education. / Nur, Nasheen; Park, Noseong; Dorodchi, Mohsen et al.
Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings. ed. / Seiji Isotani; Eva Millán; Amy Ogan; Bruce McLaren; Peter Hastings; Rose Luckin. Springer Verlag, 2019. p. 370-382 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11625 LNAI).

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

TY - GEN

T1 - Student network analysis

T2 - 20th International Conference on Artificial Intelligence in Education, AIED 2019

AU - Nur, Nasheen

AU - Park, Noseong

AU - Dorodchi, Mohsen

AU - Dou, Wenwen

AU - Mahzoon, Mohammad Javad

AU - Niu, Xi

AU - Maher, Mary Lou

N1 - Funding Information: Acknowledgements. This work is supported by the National Science Foundation under Grant No. 1820862. Noseong Park and Mohsen Dorodchi are the co-corresponding authors. Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - We present a prediction model to detect delayed graduation cases based on student network analysis. In the U.S. only 60% of undergraduate students finish their bachelors’ degrees in 6 yearsÂ [1]. We present many features based on student networks and activity records. To our knowledge, our feature design, which includes conventional academic performance features, student network features, and fix-point features, is one of the most comprehensive ones. We achieved the F-1 score of 0.85 and AUCROC of 0.86.

AB - We present a prediction model to detect delayed graduation cases based on student network analysis. In the U.S. only 60% of undergraduate students finish their bachelors’ degrees in 6 yearsÂ [1]. We present many features based on student networks and activity records. To our knowledge, our feature design, which includes conventional academic performance features, student network features, and fix-point features, is one of the most comprehensive ones. We achieved the F-1 score of 0.85 and AUCROC of 0.86.

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SN - 9783030232030

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 370

EP - 382

BT - Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings

A2 - Isotani, Seiji

A2 - Millán, Eva

A2 - Ogan, Amy

A2 - McLaren, Bruce

A2 - Hastings, Peter

A2 - Luckin, Rose

PB - Springer Verlag

Y2 - 25 June 2019 through 29 June 2019

ER -

Nur N, Park N, Dorodchi M, Dou W, Mahzoon MJ, Niu X et al. Student network analysis: A novel way to predict delayed graduation in higher education. In Isotani S, Millán E, Ogan A, McLaren B, Hastings P, Luckin R, editors, Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings. Springer Verlag. 2019. p. 370-382. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-23204-7_31

Student network analysis: A novel way to predict delayed graduation in higher education

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Bibliographical note

All Science Journal Classification (ASJC) codes

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