research-article

E-Learning Performance Prediction Based on Attention Mechanism

Authors:

Yu-Ting LingAuthors Info & Claims

ICDEL '21: Proceedings of the 2021 6th International Conference on Distance Education and Learning

Pages 152 - 156

https://doi.org/10.1145/3474995.3475020

Published: 22 November 2021 Publication History

Abstract

One of the current research hotspots in educational data mining is to predict their online academic performance. The prediction results can provide personalized guidance for learners and teaching strategies. At present, performance prediction methods often ignore the fact that different behavior characteristics have different effects on performance. Therefore, this paper proposed an e-Learning performance prediction method based on attention mechanism. This method calculated the attention score of each behavior feature, then assigned the corresponding attention weight to each behavior feature. So, by this way, different behavior features have different influence on academic performance. The experimental results show that this method can predict e-Learning performance more accurately than other methods.

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Cited By

Leelaluk STang CMinematsu TTaniguchi YOkubo FYamashita TShimada A(2024)Attention-Based Artificial Neural Network for Student Performance Prediction Based on Learning ActivitiesIEEE Access10.1109/ACCESS.2024.342955412(100659-100675)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3429554
Dol SJawandhiya P(2023)Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A surveyEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106071122:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106071
Mi W(2022)English Education Tutoring Teaching System Based on MOOCComputational Intelligence and Neuroscience10.1155/2022/15633522022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1563352
Show More Cited By

Index Terms

E-Learning Performance Prediction Based on Attention Mechanism
1. Applied computing
  1. Education
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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ICDEL '21: Proceedings of the 2021 6th International Conference on Distance Education and Learning

May 2021

330 pages

ISBN:9781450390033

DOI:10.1145/3474995

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2021

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National Natural Science Foundation of China

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ICDEL 2021

ICDEL 2021: 2021 the 6th International Conference on Distance Education and Learning

May 21 - 24, 2021

Shanghai, China

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Cited By

Leelaluk STang CMinematsu TTaniguchi YOkubo FYamashita TShimada A(2024)Attention-Based Artificial Neural Network for Student Performance Prediction Based on Learning ActivitiesIEEE Access10.1109/ACCESS.2024.342955412(100659-100675)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3429554
Dol SJawandhiya P(2023)Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A surveyEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106071122:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106071
Mi W(2022)English Education Tutoring Teaching System Based on MOOCComputational Intelligence and Neuroscience10.1155/2022/15633522022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1563352
Leelaluk SMinematsu TTaniguchi YOkubo FYamashita TShimada A(2022)Scaled-Dot Product Attention for Early Detection of At-risk Students2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)10.1109/TALE54877.2022.00059(316-322)Online publication date: Dec-2022
https://doi.org/10.1109/TALE54877.2022.00059

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