research-article

CLMS-Net: dropout prediction in MOOCs with deep learning

Authors:

Jun FengAuthors Info & Claims

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

Article No.: 75, Pages 1 - 6

https://doi.org/10.1145/3321408.3322848

Published: 17 May 2019 Publication History

Abstract

Massive Open Online Courses (MOOCs) have played an increasingly crucial role in education, but the high dropout rate problem is great serious. Predicting whether students will dropout has attracted considerable attention. Current methods mainly depend on handcrafted features. The process is laborious and not scalability, and even difficult to guarantee the final prediction effect. In this paper, we propose a deep neural network model, which is a combination of Convolutional Neural Network, Long Short-Term Memory network and Support Vector Machine. Our model has an effective feature extraction strategy, which automatically extract features from the raw data, and takes into account the impact of the sequential relationship of student behavior and class imbalance on dropout and, most importantly, reinforce the performance of dropout prediction. Extensive experiments on a public dataset have shown that the proposed model can achieve better results comparable to feature engineering based methods and other neural network methods.

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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
Ma HZhao WTang YHuang PZhu HTang WLi K(2024)Personalized Early Warning of Learning Performance for College Students: A Multilevel Approach via Cognitive Ability and Learning State ModelingIEEE Transactions on Learning Technologies10.1109/TLT.2024.338221717(1440-1453)Online publication date: 2024
https://doi.org/10.1109/TLT.2024.3382217
Al Amoudi SAlhothali AMirza RAssalahi HAldosemani T(2024)Click-Based Representation Learning Framework of Student Navigational Behavior in MOOCsIEEE Access10.1109/ACCESS.2024.345051412(121480-121494)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3450514
Show More Cited By

Index Terms

CLMS-Net: dropout prediction in MOOCs with deep learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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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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Returned Talented Scholars Research project in Shaanxi Province
National Natural Science Foundation of China
the Shaanxi Province Key Lab. of Satellite and Terrestrial Network Tech. R&D

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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
Ma HZhao WTang YHuang PZhu HTang WLi K(2024)Personalized Early Warning of Learning Performance for College Students: A Multilevel Approach via Cognitive Ability and Learning State ModelingIEEE Transactions on Learning Technologies10.1109/TLT.2024.338221717(1440-1453)Online publication date: 2024
https://doi.org/10.1109/TLT.2024.3382217
Al Amoudi SAlhothali AMirza RAssalahi HAldosemani T(2024)Click-Based Representation Learning Framework of Student Navigational Behavior in MOOCsIEEE Access10.1109/ACCESS.2024.345051412(121480-121494)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3450514
Vives LCabezas IVives JReyes NAquino JCóndor JAltamirano S(2024)Prediction of Students’ Academic Performance in the Programming Fundamentals Course Using Long Short-Term Memory Neural NetworksIEEE Access10.1109/ACCESS.2024.335016912(5882-5898)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3350169
Khairy DAlharbi NAmasha MAreed MAlkhalaf SAbougalala R(2024)Prediction of student exam performance using data mining classification algorithmsEducation and Information Technologies10.1007/s10639-024-12619-wOnline publication date: 3-May-2024
https://doi.org/10.1007/s10639-024-12619-w
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
Chibouti ADrissi SChefrour ABoussaha K(2024)Towards Advanced Approaches to Predict Students Dropout in MOOCSNovel and Intelligent Digital Systems: Proceedings of the 4th International Conference (NiDS 2024)10.1007/978-3-031-73344-4_22(269-275)Online publication date: 16-Oct-2024
https://doi.org/10.1007/978-3-031-73344-4_22
Gutierrez-Pachas DGarcia-Zanabria GCuadros-Vargas ECamara-Chavez GGomez-Nieto E(2023)Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American ContextEducation Sciences10.3390/educsci1302015413:2(154)Online publication date: 1-Feb-2023
https://doi.org/10.3390/educsci13020154
Chi ZZhang SShi L(2023)Analysis and Prediction of MOOC Learners’ Dropout BehaviorApplied Sciences10.3390/app1302106813:2(1068)Online publication date: 13-Jan-2023
https://doi.org/10.3390/app13021068
Li JLi SZhao YGuo LHao FRen MLi K(2023)Predicting Dropouts Before Enrollments in MOOCs: An Explainable and Self-Supervised ModelIEEE Transactions on Services Computing10.1109/TSC.2023.331162716:6(4154-4167)Online publication date: Nov-2023
https://doi.org/10.1109/TSC.2023.3311627
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