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

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

摘要

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.

原文	???core.languages.en_GB???
主出版物標題	Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021
編輯	Maiga Chang, Nian-Shing Chen, Demetrios G Sampson, Ahmed Tlili
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	350-351
頁數	2
ISBN（電子）	9781665441063
DOIs	https://doi.org/10.1109/ICALT52272.2021.00112
出版狀態	已出版 - 7月 2021
事件	21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021 - Virtual, Online, Malaysia 持續時間: 12 7月 2021 → 15 7月 2021

出版系列

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021
國家/地區	Malaysia
城市	Virtual, Online
期間	12/07/21 → 15/07/21

存取文件

10.1109/ICALT52272.2021.00112

應用教育大數據與學習分析於改善程式設計自我調節學習之研究(1/3)
Yang, S. J. H. (PI)
1/08/20 → 31/07/21
研究計畫: Research
運算思維能力培養與學習分析之實證研究(2/3)
Yang, S. J. H. (PI)
1/08/20 → 31/07/21
研究計畫: Research

引用此

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. 於 M. Chang, N.-S. Chen, D. G. Sampson, & A. Tlili (編輯), Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021 (頁 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. 等. / Considering temporal features in early prediction of at-risk students. Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021. 編輯 / Maiga Chang ; Nian-Shing Chen ; Demetrios G Sampson ; Ahmed Tlili. Institute of Electrical and Electronics Engineers Inc., 2021. 頁 350-351 (Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021).

@inproceedings{bba0aaf3934440678ce205862e67e98d,

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",

year = "2021",

month = jul,

doi = "10.1109/ICALT52272.2021.00112",

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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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Chen, ZYR, Huang, AYQ, Lu, OHT & Yang, SJH 2021, Considering temporal features in early prediction of at-risk students. 於 M Chang, N-S Chen, DG Sampson & A Tlili (編輯), 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., 頁 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. 等.
Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021. 編輯 / 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).

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

TY - GEN

T1 - Considering temporal features in early prediction of at-risk students

AU - Chen, Zoe Y.R.

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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U2 - 10.1109/ICALT52272.2021.00112

DO - 10.1109/ICALT52272.2021.00112

M3 - 會議論文篇章

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T3 - Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021

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

A2 - Chang, Maiga

A2 - Chen, Nian-Shing

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021

Y2 - 12 July 2021 through 15 July 2021

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Chen ZYR, Huang AYQ, Lu OHT, Yang SJH. Considering temporal features in early prediction of at-risk students. 於 Chang M, Chen NS, Sampson DG, Tlili A, 編輯, 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

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專案

應用教育大數據與學習分析於改善程式設計自我調節學習之研究(1/3)

運算思維能力培養與學習分析之實證研究(2/3)

引用此