Considering temporal features in early prediction of at-risk students

Zoe Y.R. Chen; Anna Y.Q. Huang; Owen H.T. Lu; Stephen J.H. Yang

doi:10.1109/ICALT52272.2021.00112

Considering temporal features in early prediction of at-risk students

Zoe Y.R. Chen, Anna Y.Q. Huang, Owen H.T. Lu, Stephen J.H. Yang

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

Abstract

Nowadays, there are more and more researches focused on prediction of learning outcome, and most of them applied quantitate type of analysis approaches. Thus, we want to apply another type of analysis approach to do early prediction. In this research, we applied temporal features and analysis approach to predict students' learning outcomes and identify at-risk students. The result shows that using temporal features is effective on early prediction of learning outcome and there exists differences of learning behaviors between students which have different learning background.

Original language	English
Title of host publication	Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021
Editors	Maiga Chang, Nian-Shing Chen, Demetrios G Sampson, Ahmed Tlili
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	350-351
Number of pages	2
ISBN (Electronic)	9781665441063
DOIs	https://doi.org/10.1109/ICALT52272.2021.00112
State	Published - Jul 2021
Event	21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021 - Virtual, Online, Malaysia Duration: 12 Jul 2021 → 15 Jul 2021

Publication series

Name	Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021

Conference

Conference	21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021
Country/Territory	Malaysia
City	Virtual, Online
Period	12/07/21 → 15/07/21

Keywords

Early prediction
Learning analytics
Long-Short-Term-Memory

Access to Document

10.1109/ICALT52272.2021.00112

The Research of Applying Educational Big Data and Learning Analytics to Improve Self-Regulated Learning in Programming(1/3)
Yang, S. J. H. (PI)
1/08/20 → 31/07/21
Project: Research
An Empirical Study on the Cultivation and Learning Analytics of Computational Thinking Skills(2/3)
Yang, S. J. H. (PI)
1/08/20 → 31/07/21
Project: Research

Cite this

Chen, Z. Y. R., Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2021). Considering temporal features in early prediction of at-risk students. In M. Chang, N.-S. Chen, D. G. Sampson, & A. Tlili (Eds.), Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021 (pp. 350-351). (Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICALT52272.2021.00112

Chen, Zoe Y.R. ; Huang, Anna Y.Q. ; Lu, Owen H.T. et al. / Considering temporal features in early prediction of at-risk students. Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021. editor / Maiga Chang ; Nian-Shing Chen ; Demetrios G Sampson ; Ahmed Tlili. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 350-351 (Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021).

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title = "Considering temporal features in early prediction of at-risk students",

abstract = "Nowadays, there are more and more researches focused on prediction of learning outcome, and most of them applied quantitate type of analysis approaches. Thus, we want to apply another type of analysis approach to do early prediction. In this research, we applied temporal features and analysis approach to predict students' learning outcomes and identify at-risk students. The result shows that using temporal features is effective on early prediction of learning outcome and there exists differences of learning behaviors between students which have different learning background.",

keywords = "Early prediction, Learning analytics, Long-Short-Term-Memory",

author = "Chen, {Zoe Y.R.} and Huang, {Anna Y.Q.} and Lu, {Owen H.T.} and Yang, {Stephen J.H.}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021 ; Conference date: 12-07-2021 Through 15-07-2021",

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series = "Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021",

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

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booktitle = "Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021",

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Chen, ZYR, Huang, AYQ, Lu, OHT & Yang, SJH 2021, Considering temporal features in early prediction of at-risk students. in M Chang, N-S Chen, DG Sampson & A Tlili (eds), Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021. Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021, Institute of Electrical and Electronics Engineers Inc., pp. 350-351, 21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021, Virtual, Online, Malaysia, 12/07/21. https://doi.org/10.1109/ICALT52272.2021.00112

Considering temporal features in early prediction of at-risk students. / Chen, Zoe Y.R.; Huang, Anna Y.Q.; Lu, Owen H.T. et al.
Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021. ed. / Maiga Chang; Nian-Shing Chen; Demetrios G Sampson; Ahmed Tlili. Institute of Electrical and Electronics Engineers Inc., 2021. p. 350-351 (Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021).

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

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AU - Huang, Anna Y.Q.

AU - Lu, Owen H.T.

AU - Yang, Stephen J.H.

PY - 2021/7

Y1 - 2021/7

N2 - Nowadays, there are more and more researches focused on prediction of learning outcome, and most of them applied quantitate type of analysis approaches. Thus, we want to apply another type of analysis approach to do early prediction. In this research, we applied temporal features and analysis approach to predict students' learning outcomes and identify at-risk students. The result shows that using temporal features is effective on early prediction of learning outcome and there exists differences of learning behaviors between students which have different learning background.

AB - Nowadays, there are more and more researches focused on prediction of learning outcome, and most of them applied quantitate type of analysis approaches. Thus, we want to apply another type of analysis approach to do early prediction. In this research, we applied temporal features and analysis approach to predict students' learning outcomes and identify at-risk students. The result shows that using temporal features is effective on early prediction of learning outcome and there exists differences of learning behaviors between students which have different learning background.

KW - Early prediction

KW - Learning analytics

KW - Long-Short-Term-Memory

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PB - Institute of Electrical and Electronics Engineers Inc.

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Chen ZYR, Huang AYQ, Lu OHT, Yang SJH. Considering temporal features in early prediction of at-risk students. In Chang M, Chen NS, Sampson DG, Tlili A, editors, Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 350-351. (Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021). doi: 10.1109/ICALT52272.2021.00112

Considering temporal features in early prediction of at-risk students

Abstract

Publication series

Conference

Keywords

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Projects

The Research of Applying Educational Big Data and Learning Analytics to Improve Self-Regulated Learning in Programming(1/3)

An Empirical Study on the Cultivation and Learning Analytics of Computational Thinking Skills(2/3)

Cite this