Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection

Yue Yun; Huan Dai; Ruoqi Cao; Yupei Zhang; Xuequn Shang

doi:10.1007/978-3-030-78270-2_74

Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection

Yue Yun, Huan Dai, Ruoqi Cao, Yupei Zhang, Xuequn Shang

School of Computer Science

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

4 Scopus citations

Abstract

Student learning performance prediction (SLPP) is a crucial step in high school education. However, traditional methods fail to consider abnormal students. In this study, we organized every student’s learning data as a graph to use the schema of graph memory networks (GMNs). To distinguish the students and make GMNs learn robustly, we proposed to train GMNs in an “easy-to-hard” process, leading to self-paced graph memory network (SPGMN). SPGMN chooses the lowdifficult samples as a batch to tune the model parameters in each training iteration. This approach not only improves the robustness but also rearranges the student sample from normal to abnormal. The experiment results show that SPGMN achieves a higher prediction accuracy and more robustness in comparison with traditional methods. The resulted student sequence reveals the abnormal student has a different pattern in course selection to normal students.

Original language	English
Title of host publication	Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings
Editors	Ido Roll, Danielle McNamara, Sergey Sosnovsky, Rose Luckin, Vania Dimitrova
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	417-421
Number of pages	5
ISBN (Print)	9783030782696
DOIs	https://doi.org/10.1007/978-3-030-78270-2_74
State	Published - 2021
Event	22nd International Conference on Artificial Intelligence in Education, AIED 2021 - Virtual, Online Duration: 14 Jun 2021 → 18 Jun 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12749 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Artificial Intelligence in Education, AIED 2021
City	Virtual, Online
Period	14/06/21 → 18/06/21

Keywords

Abnormal student detection
Graph memory networks
Self-paced learning
Student learning performance prediction

Access to Document

10.1007/978-3-030-78270-2_74

Cite this

Yun, Y., Dai, H., Cao, R., Zhang, Y., & Shang, X. (2021). Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings (pp. 417-421). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12749 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-78270-2_74

Yun, Yue ; Dai, Huan ; Cao, Ruoqi et al. / Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection. Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings. editor / Ido Roll ; Danielle McNamara ; Sergey Sosnovsky ; Rose Luckin ; Vania Dimitrova. Springer Science and Business Media Deutschland GmbH, 2021. pp. 417-421 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{58b60ffe8d2f4c8b8689929f4f563955,

title = "Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection",

abstract = "Student learning performance prediction (SLPP) is a crucial step in high school education. However, traditional methods fail to consider abnormal students. In this study, we organized every student{\textquoteright}s learning data as a graph to use the schema of graph memory networks (GMNs). To distinguish the students and make GMNs learn robustly, we proposed to train GMNs in an “easy-to-hard” process, leading to self-paced graph memory network (SPGMN). SPGMN chooses the lowdifficult samples as a batch to tune the model parameters in each training iteration. This approach not only improves the robustness but also rearranges the student sample from normal to abnormal. The experiment results show that SPGMN achieves a higher prediction accuracy and more robustness in comparison with traditional methods. The resulted student sequence reveals the abnormal student has a different pattern in course selection to normal students.",

keywords = "Abnormal student detection, Graph memory networks, Self-paced learning, Student learning performance prediction",

author = "Yue Yun and Huan Dai and Ruoqi Cao and Yupei Zhang and Xuequn Shang",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2021.; 22nd International Conference on Artificial Intelligence in Education, AIED 2021 ; Conference date: 14-06-2021 Through 18-06-2021",

year = "2021",

doi = "10.1007/978-3-030-78270-2_74",

language = "英语",

isbn = "9783030782696",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "417--421",

editor = "Ido Roll and Danielle McNamara and Sergey Sosnovsky and Rose Luckin and Vania Dimitrova",

booktitle = "Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings",

}

Yun, Y, Dai, H, Cao, R, Zhang, Y & Shang, X 2021, Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection. in I Roll, D McNamara, S Sosnovsky, R Luckin & V Dimitrova (eds), Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12749 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 417-421, 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Virtual, Online, 14/06/21. https://doi.org/10.1007/978-3-030-78270-2_74

Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection. / Yun, Yue; Dai, Huan; Cao, Ruoqi et al.
Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings. ed. / Ido Roll; Danielle McNamara; Sergey Sosnovsky; Rose Luckin; Vania Dimitrova. Springer Science and Business Media Deutschland GmbH, 2021. p. 417-421 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12749 LNAI).

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

TY - GEN

T1 - Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection

AU - Yun, Yue

AU - Dai, Huan

AU - Cao, Ruoqi

AU - Zhang, Yupei

AU - Shang, Xuequn

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2021.

PY - 2021

Y1 - 2021

N2 - Student learning performance prediction (SLPP) is a crucial step in high school education. However, traditional methods fail to consider abnormal students. In this study, we organized every student’s learning data as a graph to use the schema of graph memory networks (GMNs). To distinguish the students and make GMNs learn robustly, we proposed to train GMNs in an “easy-to-hard” process, leading to self-paced graph memory network (SPGMN). SPGMN chooses the lowdifficult samples as a batch to tune the model parameters in each training iteration. This approach not only improves the robustness but also rearranges the student sample from normal to abnormal. The experiment results show that SPGMN achieves a higher prediction accuracy and more robustness in comparison with traditional methods. The resulted student sequence reveals the abnormal student has a different pattern in course selection to normal students.

AB - Student learning performance prediction (SLPP) is a crucial step in high school education. However, traditional methods fail to consider abnormal students. In this study, we organized every student’s learning data as a graph to use the schema of graph memory networks (GMNs). To distinguish the students and make GMNs learn robustly, we proposed to train GMNs in an “easy-to-hard” process, leading to self-paced graph memory network (SPGMN). SPGMN chooses the lowdifficult samples as a batch to tune the model parameters in each training iteration. This approach not only improves the robustness but also rearranges the student sample from normal to abnormal. The experiment results show that SPGMN achieves a higher prediction accuracy and more robustness in comparison with traditional methods. The resulted student sequence reveals the abnormal student has a different pattern in course selection to normal students.

KW - Abnormal student detection

KW - Graph memory networks

KW - Self-paced learning

KW - Student learning performance prediction

UR - http://www.scopus.com/inward/record.url?scp=85124960219&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-78270-2_74

DO - 10.1007/978-3-030-78270-2_74

M3 - 会议稿件

AN - SCOPUS:85124960219

SN - 9783030782696

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

SP - 417

EP - 421

BT - Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings

A2 - Roll, Ido

A2 - McNamara, Danielle

A2 - Sosnovsky, Sergey

A2 - Luckin, Rose

A2 - Dimitrova, Vania

PB - Springer Science and Business Media Deutschland GmbH

T2 - 22nd International Conference on Artificial Intelligence in Education, AIED 2021

Y2 - 14 June 2021 through 18 June 2021

ER -

Yun Y, Dai H, Cao R, Zhang Y , Shang X. Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection. In Roll I, McNamara D, Sosnovsky S, Luckin R, Dimitrova V, editors, Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 417-421. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-78270-2_74

Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this