Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School

Yicong LIANG; Haoran XIE; Di ZOU; Fu Lee WANG; Xinyi HUANG

doi:10.1007/978-981-99-8255-4_30

Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School

Yicong LIANG, Haoran XIE, Di ZOU, Fu Lee WANG^*, Xinyi HUANG

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

Abstract

Emerging technologies have allowed researchers to easily access educational data, conduct data analysis, and predict students’ learning performance. However, the factors that are essential for the predictive model have not been identified. In the present research, based on the information entropy framework, we firstly identify the factors that influence students’ academic learning performance. Then, we adopt the explainable machine learning frameworks, which are based on logistic regression and support vector machines, to predict student learning achievements. The experiment was conducted on the real-world dataset from the secondary school within two subjects. The results reveal that the feature of the failure records from students’ past performance is a significant factor related to learning achievements. The predictive model based on student profiles achieves up to 86% accuracy for the prediction of learning outcome related to the final grade.

Original language	English
Title of host publication	Technology in Education. Innovative Practices for the New Normal : 6th International Conference on Technology in Education, ICTE 2023, Hong Kong, China, December 19–21, 2023, Proceedings
Editors	Simon K. S. CHEUNG, Fu Lee WANG, Naraphorn PAOPRASERT, Peerayuth CHARNSETHIKUL, Kam Cheong LI, Kongkiti PHUSAVAT
Publisher	Springer Singapore
Chapter	30
Pages	349-360
Number of pages	12
ISBN (Electronic)	9789819982554
ISBN (Print)	9789819982547
DOIs	https://doi.org/10.1007/978-981-99-8255-4_30
Publication status	Published - 9 Nov 2023

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	1974
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Funding

This research has been supported by the 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

Student Learning Performance
Data Analysis in Education
Student Profiles
Learning Achievement Predictive Model

UN SDGs

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

Access to Document

10.1007/978-981-99-8255-4_30

https://link.springer.com/10.1007/978-981-99-8255-4_30

Cite this

LIANG, Y., XIE, H., ZOU, D., WANG, F. L., & HUANG, X. (2023). Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School. In S. K. S. CHEUNG, F. L. WANG, N. PAOPRASERT, P. CHARNSETHIKUL, K. C. LI, & K. PHUSAVAT (Eds.), Technology in Education. Innovative Practices for the New Normal : 6th International Conference on Technology in Education, ICTE 2023, Hong Kong, China, December 19–21, 2023, Proceedings (pp. 349-360). (Communications in Computer and Information Science; Vol. 1974). Springer Singapore. https://doi.org/10.1007/978-981-99-8255-4_30

LIANG, Yicong ; XIE, Haoran ; ZOU, Di et al. / Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School. Technology in Education. Innovative Practices for the New Normal : 6th International Conference on Technology in Education, ICTE 2023, Hong Kong, China, December 19–21, 2023, Proceedings. editor / Simon K. S. CHEUNG ; Fu Lee WANG ; Naraphorn PAOPRASERT ; Peerayuth CHARNSETHIKUL ; Kam Cheong LI ; Kongkiti PHUSAVAT. Springer Singapore, 2023. pp. 349-360 (Communications in Computer and Information Science).

@inproceedings{f95c5a3bb7b54bd3bb9969d3844fef07,

title = "Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School",

abstract = "Emerging technologies have allowed researchers to easily access educational data, conduct data analysis, and predict students{\textquoteright} learning performance. However, the factors that are essential for the predictive model have not been identified. In the present research, based on the information entropy framework, we firstly identify the factors that influence students{\textquoteright} academic learning performance. Then, we adopt the explainable machine learning frameworks, which are based on logistic regression and support vector machines, to predict student learning achievements. The experiment was conducted on the real-world dataset from the secondary school within two subjects. The results reveal that the feature of the failure records from students{\textquoteright} past performance is a significant factor related to learning achievements. The predictive model based on student profiles achieves up to 86% accuracy for the prediction of learning outcome related to the final grade.",

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author = "Yicong LIANG and Haoran XIE and Di ZOU and WANG, {Fu Lee} and Xinyi HUANG",

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LIANG, Y, XIE, H , ZOU, D, WANG, FL & HUANG, X 2023, Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School. in SKS CHEUNG, FL WANG, N PAOPRASERT, P CHARNSETHIKUL, KC LI & K PHUSAVAT (eds), Technology in Education. Innovative Practices for the New Normal : 6th International Conference on Technology in Education, ICTE 2023, Hong Kong, China, December 19–21, 2023, Proceedings. Communications in Computer and Information Science, vol. 1974, Springer Singapore, pp. 349-360. https://doi.org/10.1007/978-981-99-8255-4_30

Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School. / LIANG, Yicong; XIE, Haoran ; ZOU, Di et al.
Technology in Education. Innovative Practices for the New Normal : 6th International Conference on Technology in Education, ICTE 2023, Hong Kong, China, December 19–21, 2023, Proceedings. ed. / Simon K. S. CHEUNG; Fu Lee WANG; Naraphorn PAOPRASERT; Peerayuth CHARNSETHIKUL; Kam Cheong LI; Kongkiti PHUSAVAT. Springer Singapore, 2023. p. 349-360 (Communications in Computer and Information Science; Vol. 1974).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School

AU - LIANG, Yicong

AU - XIE, Haoran

AU - ZOU, Di

AU - WANG, Fu Lee

AU - HUANG, Xinyi

PY - 2023/11/9

Y1 - 2023/11/9

N2 - Emerging technologies have allowed researchers to easily access educational data, conduct data analysis, and predict students’ learning performance. However, the factors that are essential for the predictive model have not been identified. In the present research, based on the information entropy framework, we firstly identify the factors that influence students’ academic learning performance. Then, we adopt the explainable machine learning frameworks, which are based on logistic regression and support vector machines, to predict student learning achievements. The experiment was conducted on the real-world dataset from the secondary school within two subjects. The results reveal that the feature of the failure records from students’ past performance is a significant factor related to learning achievements. The predictive model based on student profiles achieves up to 86% accuracy for the prediction of learning outcome related to the final grade.

AB - Emerging technologies have allowed researchers to easily access educational data, conduct data analysis, and predict students’ learning performance. However, the factors that are essential for the predictive model have not been identified. In the present research, based on the information entropy framework, we firstly identify the factors that influence students’ academic learning performance. Then, we adopt the explainable machine learning frameworks, which are based on logistic regression and support vector machines, to predict student learning achievements. The experiment was conducted on the real-world dataset from the secondary school within two subjects. The results reveal that the feature of the failure records from students’ past performance is a significant factor related to learning achievements. The predictive model based on student profiles achieves up to 86% accuracy for the prediction of learning outcome related to the final grade.

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A2 - CHEUNG, Simon K. S.

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A2 - PHUSAVAT, Kongkiti

PB - Springer Singapore

ER -

LIANG Y, XIE H , ZOU D, WANG FL, HUANG X. Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School. In CHEUNG SKS, WANG FL, PAOPRASERT N, CHARNSETHIKUL P, LI KC, PHUSAVAT K, editors, Technology in Education. Innovative Practices for the New Normal : 6th International Conference on Technology in Education, ICTE 2023, Hong Kong, China, December 19–21, 2023, Proceedings. Springer Singapore. 2023. p. 349-360. (Communications in Computer and Information Science). Epub 2023 Nov 9. doi: 10.1007/978-981-99-8255-4_30

Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School

Abstract

Publication series

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Integrating Novel Dropout Mechanism into Label Extension for Emotion Classification

A Preliminary Investigation and Evaluation on Sentence Representation Models based on Contrastive Learning

Cite this

Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School

Abstract

Publication series

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

Integrating Novel Dropout Mechanism into Label Extension for Emotion Classification

A Preliminary Investigation and Evaluation on Sentence Representation Models based on Contrastive Learning

Cite this