Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment

Zongxi LI; Haoran XIE; Fu Lee WANG; Weiming WANG; Man-Kong CHOW

doi:10.1109/BESC57393.2022.9995635

Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment

Zongxi LI
, Haoran XIE
, Fu Lee WANG^*
, Weiming WANG
, Man-Kong CHOW

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Students' success is the ultimate goal of any institution around the world. Early detection of at-risk students can facilitate the instructor or tutor to provide timely support to those at risk of failing the course. In a traditional face-to-face classroom, students can monitor learning patterns in routine interactions. However, teachers in the online classroom have limited information, compared with the face-to-face classroom, to detect students in trouble due to the lack of instance interactions between teachers and students. Particularly, such a problem has become worse than ever since 2020, as online teaching and learning are ubiquitous in the Post-COVID19 Era. In this work, we aim to predict if the student obtains a low course grade based on their behavioral patterns in continuous assessments, which are easy-to-retrieve attributes and available in most e-learning systems. We leverage the ratio of assessment grade to the time spent on the assessment as a useful feature in the machine-learning prediction framework. Experiments on real-world datasets indicate that such a ratio can improve the accuracy of detecting at-risk students.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350398144
DOIs	https://doi.org/10.1109/BESC57393.2022.9995635
Publication status	Published - 28 Dec 2022
Event	9th IEEE International Conference on Behavioural and Social Computing, BESC 2022 - Matsuyama, Japan Duration: 29 Oct 2022 → 31 Oct 2022

Publication series

Name	Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022

Conference

Conference	9th IEEE International Conference on Behavioural and Social Computing, BESC 2022
Country/Territory	Japan
City	Matsuyama
Period	29/10/22 → 31/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Funding

The research has been supported by Lam Woo Research Fund (LWP20019) and the Faculty Research Grants (DB22B4) of Lingnan University, Hong Kong.

UN SDGs

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

SDG 4 Quality Education

Keywords

academic performance prediction
at-risk students detection
e-learning
education data mining
machine learning

Access to Document

10.1109/BESC57393.2022.9995635

Cite this

LI, Z., XIE, H., WANG, F. L., WANG, W., & CHOW, M.-K. (2022). Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment. In Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022 (Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BESC57393.2022.9995635

LI, Zongxi ; XIE, Haoran ; WANG, Fu Lee et al. / Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment. Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022).

@inproceedings{8b5fbacea8c54a7282d0a754a5c9d4ea,

title = "Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment",

abstract = "Students' success is the ultimate goal of any institution around the world. Early detection of at-risk students can facilitate the instructor or tutor to provide timely support to those at risk of failing the course. In a traditional face-to-face classroom, students can monitor learning patterns in routine interactions. However, teachers in the online classroom have limited information, compared with the face-to-face classroom, to detect students in trouble due to the lack of instance interactions between teachers and students. Particularly, such a problem has become worse than ever since 2020, as online teaching and learning are ubiquitous in the Post-COVID19 Era. In this work, we aim to predict if the student obtains a low course grade based on their behavioral patterns in continuous assessments, which are easy-to-retrieve attributes and available in most e-learning systems. We leverage the ratio of assessment grade to the time spent on the assessment as a useful feature in the machine-learning prediction framework. Experiments on real-world datasets indicate that such a ratio can improve the accuracy of detecting at-risk students.",

keywords = "academic performance prediction, at-risk students detection, e-learning, education data mining, machine learning",

author = "Zongxi LI and Haoran XIE and WANG, \{Fu Lee\} and Weiming WANG and Man-Kong CHOW",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 9th IEEE International Conference on Behavioural and Social Computing, BESC 2022 ; Conference date: 29-10-2022 Through 31-10-2022",

year = "2022",

month = dec,

day = "28",

doi = "10.1109/BESC57393.2022.9995635",

language = "English",

series = "Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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address = "United States",

}

LI, Z , XIE, H, WANG, FL, WANG, W & CHOW, M-K 2022, Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment. in Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022. Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022, Institute of Electrical and Electronics Engineers Inc., 9th IEEE International Conference on Behavioural and Social Computing, BESC 2022, Matsuyama, Japan, 29/10/22. https://doi.org/10.1109/BESC57393.2022.9995635

Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment. / LI, Zongxi ; XIE, Haoran; WANG, Fu Lee et al.
Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022).

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

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AU - LI, Zongxi

AU - XIE, Haoran

AU - WANG, Fu Lee

AU - WANG, Weiming

AU - CHOW, Man-Kong

PY - 2022/12/28

Y1 - 2022/12/28

N2 - Students' success is the ultimate goal of any institution around the world. Early detection of at-risk students can facilitate the instructor or tutor to provide timely support to those at risk of failing the course. In a traditional face-to-face classroom, students can monitor learning patterns in routine interactions. However, teachers in the online classroom have limited information, compared with the face-to-face classroom, to detect students in trouble due to the lack of instance interactions between teachers and students. Particularly, such a problem has become worse than ever since 2020, as online teaching and learning are ubiquitous in the Post-COVID19 Era. In this work, we aim to predict if the student obtains a low course grade based on their behavioral patterns in continuous assessments, which are easy-to-retrieve attributes and available in most e-learning systems. We leverage the ratio of assessment grade to the time spent on the assessment as a useful feature in the machine-learning prediction framework. Experiments on real-world datasets indicate that such a ratio can improve the accuracy of detecting at-risk students.

AB - Students' success is the ultimate goal of any institution around the world. Early detection of at-risk students can facilitate the instructor or tutor to provide timely support to those at risk of failing the course. In a traditional face-to-face classroom, students can monitor learning patterns in routine interactions. However, teachers in the online classroom have limited information, compared with the face-to-face classroom, to detect students in trouble due to the lack of instance interactions between teachers and students. Particularly, such a problem has become worse than ever since 2020, as online teaching and learning are ubiquitous in the Post-COVID19 Era. In this work, we aim to predict if the student obtains a low course grade based on their behavioral patterns in continuous assessments, which are easy-to-retrieve attributes and available in most e-learning systems. We leverage the ratio of assessment grade to the time spent on the assessment as a useful feature in the machine-learning prediction framework. Experiments on real-world datasets indicate that such a ratio can improve the accuracy of detecting at-risk students.

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KW - education data mining

KW - machine learning

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 29 October 2022 through 31 October 2022

ER -

LI Z , XIE H, WANG FL, WANG W, CHOW MK. Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment. In Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022). doi: 10.1109/BESC57393.2022.9995635

Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Data Augmentation Techniques for Contrastive Sentence Representation Learning

Cluster-level Social Emotion Classification Across Domains

Cite this

Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Data Augmentation Techniques for Contrastive Sentence Representation Learning

Cluster-level Social Emotion Classification Across Domains

Cite this