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^*

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › 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
Publisher	Springer Singapore
Chapter	4
Pages	39-48
Number of pages	10
ISBN (Electronic)	9789819983858
ISBN (Print)	9789819983841
DOIs	https://doi.org/10.1007/978-981-99-8385-8_4
Publication status	Published - 24 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

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Funding

The research has been supported by IICA Project entitled “Developing language teachers’ technological pedagogical content knowledge and enhancing students’ language learning in virtual learning environments” (102707), the Direct Grant (DR23B2), and the Faculty Research Grants (DB23A3 and DB23B2) of Lingnan University, Hong Kong.

Keywords

Educational Data Analysis
Learning Analytics
Student Performance
Student Profiles

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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

https://link.springer.com/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). Springer Singapore. 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. Springer Singapore, 2023. pp. 39-48 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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}",

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year = "2023",

month = nov,

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

language = "English",

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

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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, Springer Singapore, pp. 39-48. 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. Springer Singapore, 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: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

TY - GEN

T1 - Investigating Student Profiles Related to Academic Learning Achievement

AU - LIANG, Yicong

AU - XIE, Haoran

AU - ZOU, Di

AU - WANG, Fu Lee

PY - 2023/11/24

Y1 - 2023/11/24

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

M3 - Conference paper (refereed)

SN - 9789819983841

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

SP - 39

EP - 48

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

A2 - XIE, Haoran

A2 - LAI, Chiu-Lin

A2 - CHEN, Wei

A2 - XU, Guandong

A2 - POPESCU, Elvira

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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. Springer Singapore. 2023. p. 39-48. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2023 Nov 24. doi: 10.1007/978-981-99-8385-8_4

Investigating Student Profiles Related to Academic Learning Achievement

Abstract

Publication series

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

Other files and links

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Contrastive Sentence Representation Learning with Adaptive False Negative Cancellation

Integrating Novel Dropout Mechanism into Label Extension for Emotion Classification

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Investigating Student Profiles Related to Academic Learning Achievement

Abstract

Publication series

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

Contrastive Sentence Representation Learning with Adaptive False Negative Cancellation

Integrating Novel Dropout Mechanism into Label Extension for Emotion Classification

Cite this