Investigating Student Profiles Related to Academic Learning Achievement

Yicong Liang; Haoran Xie; Di Zou; Fu Lee Wang

doi:10.1007/978-981-99-8385-8_4

Investigating Student Profiles Related to Academic Learning Achievement

Yicong Liang, Haoran Xie, Di Zou, Fu Lee Wang

School of Science and Technology

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

Abstract

In this paper, we aim to identify the key attributes from secondary school students’ profiles that affect learning achievement. In particular, this work investigates and compares how demographics, school-related features and social-related features in student profiles are associated with academic success or failure in the maths final exam. The experiment is conducted on a real-world dataset, and we find that parents’ education and occupation background, students’ motivation and past academic records, and socializing with friends are highly associated with final learning performance. Finally, we summarize the main characteristics of students with high academic potential in secondary school.

Original language	English
Title of host publication	Advances in Web-Based Learning – ICWL 2023 - 22nd International Conference, ICWL 2023, Proceedings
Editors	Haoran Xie, Chiu-Lin Lai, Wei Chen, Guandong Xu, Elvira Popescu
Pages	39-48
Number of pages	10
DOIs	https://doi.org/10.1007/978-981-99-8385-8_4
Publication status	Published - 2023
Event	22nd International Conference on Web-based Learning, ICWL 2023 - Sydney, Australia Duration: 26 Nov 2023 → 28 Nov 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14409 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Web-based Learning, ICWL 2023
Country/Territory	Australia
City	Sydney
Period	26/11/23 → 28/11/23

Keywords

Educational Data Analysis
Learning Analytics
Student Performance
Student Profiles

Access to Document

10.1007/978-981-99-8385-8_4

Cite this

Liang, Y., Xie, H., Zou, D., & Wang, F. L. (2023). Investigating Student Profiles Related to Academic Learning Achievement. In H. Xie, C.-L. Lai, W. Chen, G. Xu, & E. Popescu (Eds.), Advances in Web-Based Learning – ICWL 2023 - 22nd International Conference, ICWL 2023, Proceedings (pp. 39-48). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14409 LNCS). https://doi.org/10.1007/978-981-99-8385-8_4

Liang, Yicong ; Xie, Haoran ; Zou, Di et al. / Investigating Student Profiles Related to Academic Learning Achievement. Advances in Web-Based Learning – ICWL 2023 - 22nd International Conference, ICWL 2023, Proceedings. editor / Haoran Xie ; Chiu-Lin Lai ; Wei Chen ; Guandong Xu ; Elvira Popescu. 2023. pp. 39-48 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{fdaeb26413414e95b5db1aeb15a18572,

title = "Investigating Student Profiles Related to Academic Learning Achievement",

abstract = "In this paper, we aim to identify the key attributes from secondary school students{\textquoteright} profiles that affect learning achievement. In particular, this work investigates and compares how demographics, school-related features and social-related features in student profiles are associated with academic success or failure in the maths final exam. The experiment is conducted on a real-world dataset, and we find that parents{\textquoteright} education and occupation background, students{\textquoteright} motivation and past academic records, and socializing with friends are highly associated with final learning performance. Finally, we summarize the main characteristics of students with high academic potential in secondary school.",

keywords = "Educational Data Analysis, Learning Analytics, Student Performance, Student Profiles",

author = "Yicong Liang and Haoran Xie and Di Zou and Wang, {Fu Lee}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 22nd International Conference on Web-based Learning, ICWL 2023 ; Conference date: 26-11-2023 Through 28-11-2023",

year = "2023",

doi = "10.1007/978-981-99-8385-8_4",

language = "English",

isbn = "9789819983841",

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

pages = "39--48",

editor = "Haoran Xie and Chiu-Lin Lai and Wei Chen and Guandong Xu and Elvira Popescu",

booktitle = "Advances in Web-Based Learning – ICWL 2023 - 22nd International Conference, ICWL 2023, Proceedings",

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Liang, Y, Xie, H, Zou, D & Wang, FL 2023, Investigating Student Profiles Related to Academic Learning Achievement. in H Xie, C-L Lai, W Chen, G Xu & E Popescu (eds), Advances in Web-Based Learning – ICWL 2023 - 22nd International Conference, ICWL 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14409 LNCS, pp. 39-48, 22nd International Conference on Web-based Learning, ICWL 2023, Sydney, Australia, 26/11/23. https://doi.org/10.1007/978-981-99-8385-8_4

Investigating Student Profiles Related to Academic Learning Achievement. / Liang, Yicong; Xie, Haoran; Zou, Di et al.
Advances in Web-Based Learning – ICWL 2023 - 22nd International Conference, ICWL 2023, Proceedings. ed. / Haoran Xie; Chiu-Lin Lai; Wei Chen; Guandong Xu; Elvira Popescu. 2023. p. 39-48 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14409 LNCS).

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

TY - GEN

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AU - Zou, Di

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N2 - In this paper, we aim to identify the key attributes from secondary school students’ profiles that affect learning achievement. In particular, this work investigates and compares how demographics, school-related features and social-related features in student profiles are associated with academic success or failure in the maths final exam. The experiment is conducted on a real-world dataset, and we find that parents’ education and occupation background, students’ motivation and past academic records, and socializing with friends are highly associated with final learning performance. Finally, we summarize the main characteristics of students with high academic potential in secondary school.

AB - In this paper, we aim to identify the key attributes from secondary school students’ profiles that affect learning achievement. In particular, this work investigates and compares how demographics, school-related features and social-related features in student profiles are associated with academic success or failure in the maths final exam. The experiment is conducted on a real-world dataset, and we find that parents’ education and occupation background, students’ motivation and past academic records, and socializing with friends are highly associated with final learning performance. Finally, we summarize the main characteristics of students with high academic potential in secondary school.

KW - Educational Data Analysis

KW - Learning Analytics

KW - Student Performance

KW - Student Profiles

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DO - 10.1007/978-981-99-8385-8_4

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

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BT - Advances in Web-Based Learning – ICWL 2023 - 22nd International Conference, ICWL 2023, Proceedings

A2 - Xie, Haoran

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A2 - Xu, Guandong

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T2 - 22nd International Conference on Web-based Learning, ICWL 2023

Y2 - 26 November 2023 through 28 November 2023

ER -

Liang Y, Xie H, Zou D, Wang FL. Investigating Student Profiles Related to Academic Learning Achievement. In Xie H, Lai CL, Chen W, Xu G, Popescu E, editors, Advances in Web-Based Learning – ICWL 2023 - 22nd International Conference, ICWL 2023, Proceedings. 2023. p. 39-48. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-8385-8_4