research-article

Research on the evaluation of learning behavior on MOOCs based on cluster analysis

Authors:

Dezhi SunAuthors Info & Claims

EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

Pages 1055 - 1059

https://doi.org/10.1145/3443467.3443903

Published: 01 February 2021 Publication History

Abstract

With the advancement of Internet technology, especially the development of 5G, massive open online courses(MOOCs) are more and more widely used. The exploration of issues related to MOOCs has become research hotspots in recent years. Through the analysis of the dataset of MOOCs, we extract the temporal characteristics of learning behavior of MOOCs users, calculate the similarity of the temporal characteristics, and perform cluster analysis and comparison on the temporal similarity of users to obtain user based imlicit communities divided by temporal characteristics. We apply K-means, SpectrClustering, and AgglomerativeCluster clustering algorithm to analysis the experimental data, and compare the experimental results on different numbers of clusters and different data size. We use silhouette coefficient to evaluate the effectiveness of clustering algorithm. The experimental results show that the analysis of the learning behavior of MOOCs can effectively realize the division of communities and contribute to the enhancement of MOOCs teaching.

References

[1]

Feng W, Tang J, Liu T X. Understanding dropouts in MOOCs[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 517--524.

Digital Library

[2]

Qiu J, Tang J, Liu T X, et al. Modeling and predicting learning behavior in MOOCs[C]. Proceedings of the ninth ACM international conference on web search and data mining. 2016: 93--102.

[3]

Zheng S, Rosson M B, Shih P C, et al. Understanding student motivation, behaviors and perceptions in MOOCs[C]. Proceedings of the 18th ACM conference on computer supported cooperative work & social computing. 2015: 1882--1895.

[4]

Jain A K. Data clustering: 50 years beyond K-means[J]. Pattern recognition letters, 2010, 31(8): 651--666.

[5]

Alsabti K, Ranka S, Singh V. An efficient k-means clustering algorithm[J]. 1997

[6]

Ng A Y, Jordan M I, Weiss Y. On spectral clustering: Analysis and an algorithm[C]. Advances in neural information processing systems. 2002: 849--856.

[7]

Verma D, Meila M. A comparison of spectral clustering algorithms[J]. University of Washington Tech Rep UWCSE030501, 2003, 1: 1--18.

[8]

Day W H E, Edelsbrunner H. Efficient algorithms for agglomerative hierarchical clustering methods[J]. Journal of classification, 1984, 1(1): 7--24.

[9]

Papadopoulos S, Kompatsiaris Y, Vakali A, et al. Community detection in social media[J]. Data Mining and Knowledge Discovery, 2012, 24(3): 515--554.

Digital Library

[10]

Newman M E J. Finding community structure in networks using the eigenvectors of matrices[J]. Physical review E, 2006, 74(3): 036104.

[11]

https://www.jianshu.com

[12]

http://www.kdd.org/kdd-cup

[13]

https://baike.baidu.com/item

[14]

Zhu Lianjiang, Ma Bingxian, Zhao Xuequan. Clustering validity analysis based on contour coefficient[J]. Computer Applications, 2010, 30(S2): 139--141+198.

[15]

Rousseeuw P J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis[J]. Journal of computational and applied mathematics, 1987, 20: 53--65.

Cited By

ye cChen Y(2024)Analysis and prediction of online learning behavior based on data mining technologyThird International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)10.1117/12.3031167(170)Online publication date: 19-Jul-2024
https://doi.org/10.1117/12.3031167
Cao L(2024)Data Mining Analysis of New Energy Vehicles Based on Cluster Analysis TechnologyProceedings of Innovative Computing 2024, Vol. 410.1007/978-981-97-4182-3_7(52-61)Online publication date: 24-Jun-2024
https://doi.org/10.1007/978-981-97-4182-3_7
Guo HZhang RFang X(2022)Analysis of Online Course Learning Data Based on Density Peak Clustering and Research on Teaching ModeProceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022)10.2991/978-94-6463-024-4_48(457-465)Online publication date: 29-Dec-2022
https://doi.org/10.2991/978-94-6463-024-4_48

Index Terms

Research on the evaluation of learning behavior on MOOCs based on cluster analysis
1. Applied computing
  1. Education
    1. E-learning

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EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

November 2020

1202 pages

ISBN:9781450387811

DOI:10.1145/3443467

Copyright © 2020 ACM.

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Published: 01 February 2021

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EITCE 2020

EITCE 2020: 2020 4th International Conference on Electronic Information Technology and Computer Engineering

November 6 - 8, 2020

Xiamen, China

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EITCE '20 Paper Acceptance Rate 214 of 441 submissions, 49%;

Overall Acceptance Rate 508 of 972 submissions, 52%

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Cited By

ye cChen Y(2024)Analysis and prediction of online learning behavior based on data mining technologyThird International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)10.1117/12.3031167(170)Online publication date: 19-Jul-2024
https://doi.org/10.1117/12.3031167
Cao L(2024)Data Mining Analysis of New Energy Vehicles Based on Cluster Analysis TechnologyProceedings of Innovative Computing 2024, Vol. 410.1007/978-981-97-4182-3_7(52-61)Online publication date: 24-Jun-2024
https://doi.org/10.1007/978-981-97-4182-3_7
Guo HZhang RFang X(2022)Analysis of Online Course Learning Data Based on Density Peak Clustering and Research on Teaching ModeProceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022)10.2991/978-94-6463-024-4_48(457-465)Online publication date: 29-Dec-2022
https://doi.org/10.2991/978-94-6463-024-4_48

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