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Predicting learning status in MOOCs using LSTM

Authors:

Hongzhi WangAuthors Info & Claims

ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China

Article No.: 74, Pages 1 - 5

https://doi.org/10.1145/3321408.3322855

Published: 17 May 2019 Publication History

Abstract

Real-time and open online course resources of MOOCs have attracted a large number of learners in recent years. However, many new questions were emerging about the high dropout rate of learners. For MOOCs platform, predicting the learning status of MOOCs learners in real time with high accuracy is the crucial task, and it also help improve the quality of MOOCs teaching. The prediction task in this paper is inherently a time series prediction problem, and can be treated as time series classification problem, hence this paper proposed a prediction model based on RNN-LSTMs and optimization techniques which can be used to predict learners' learning status. Using datasets provided by Chinese University MOOCs as the inputs of model, the average accuracy of model's outputs was about 90%.

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Cited By

Roh DHan DKim DHan KYi MHong JPark J(2024)SIG-Net: GNN based dropout prediction in MOOCs using Student Interaction GraphProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3636002(29-37)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3636002
Lu CBulut ODemmans Epp CGierl M(2024)Impacts of engagement on academic outcomes in technology-enhanced learningDistance Education10.1080/01587919.2024.2373297(1-20)Online publication date: 30-Jul-2024
https://doi.org/10.1080/01587919.2024.2373297
El Aouifi HEl Hajji MEs-Saady Y(2024)A hybrid approach for early-identification of at-risk dropout students using LSTM-DNN networksEducation and Information Technologies10.1007/s10639-024-12588-029:14(18839-18857)Online publication date: 14-Mar-2024
https://doi.org/10.1007/s10639-024-12588-0
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ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China

May 2019

963 pages

ISBN:9781450371582

DOI:10.1145/3321408

Copyright © 2019 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

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Publication History

Published: 17 May 2019

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National Natural Science Foundation of China

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ACM TURC 2019

ACM TURC 2019: ACM Turing Celebration Conference - China

May 17 - 19, 2019

Chengdu, China

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Cited By

Roh DHan DKim DHan KYi MHong JPark J(2024)SIG-Net: GNN based dropout prediction in MOOCs using Student Interaction GraphProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3636002(29-37)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3636002
Lu CBulut ODemmans Epp CGierl M(2024)Impacts of engagement on academic outcomes in technology-enhanced learningDistance Education10.1080/01587919.2024.2373297(1-20)Online publication date: 30-Jul-2024
https://doi.org/10.1080/01587919.2024.2373297
El Aouifi HEl Hajji MEs-Saady Y(2024)A hybrid approach for early-identification of at-risk dropout students using LSTM-DNN networksEducation and Information Technologies10.1007/s10639-024-12588-029:14(18839-18857)Online publication date: 14-Mar-2024
https://doi.org/10.1007/s10639-024-12588-0
Khine M(2024)Using AI for Adaptive Learning and Adaptive AssessmentArtificial Intelligence in Education10.1007/978-981-97-9350-1_3(341-466)Online publication date: 31-Oct-2024
https://doi.org/10.1007/978-981-97-9350-1_3
Niu KZhou YLu GTai WZhang K(2023)PMCT: Parallel Multiscale Convolutional Temporal model for MOOC dropout predictionComputers and Electrical Engineering10.1016/j.compeleceng.2023.108989112(108989)Online publication date: Dec-2023
https://doi.org/10.1016/j.compeleceng.2023.108989
Zhang MLi JHuang QKan H(2023)Learning counterfactual outcomes of MOOC multiple learning behaviorsComputer Applications in Engineering Education10.1002/cae.2266631:6(1678-1689)Online publication date: 27-Jul-2023
https://doi.org/10.1002/cae.22666
Alhothali AAlbsisi MAssalahi HAldosemani T(2022)Predicting Student Outcomes in Online Courses Using Machine Learning Techniques: A ReviewSustainability10.3390/su1410619914:10(6199)Online publication date: 19-May-2022
https://doi.org/10.3390/su14106199
Chen JFang BZhang HXue X(2022)A systematic review for MOOC dropout prediction from the perspective of machine learningInteractive Learning Environments10.1080/10494820.2022.2124425(1-14)Online publication date: 23-Sep-2022
https://doi.org/10.1080/10494820.2022.2124425
Suddle MBashir M(2022)Metaheuristics based long short term memory optimization for sentiment analysisApplied Soft Computing10.1016/j.asoc.2022.109794131:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109794
Zhang HDong JLv CLin YBai J(2022)Visual analytics of potential dropout behavior patterns in online learning based on counterfactual explanationJournal of Visualization10.1007/s12650-022-00899-826:3(723-741)Online publication date: 5-Nov-2022
https://doi.org/10.1007/s12650-022-00899-8
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