Visual analytics of potential dropout behavior patterns in online learning based on counterfactual explanation-Reference-Cited by-同舟云学术

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Visual analytics of potential dropout behavior patterns in online learning based on counterfactual explanation

Published:2022-11-05 Issue: Volume: Page:
ISSN:1343-8875
Container-title:Journal of Visualization
language:en
Short-container-title:J Vis

Author:

Zhang Huijie,Dong Jialu,Lv Cheng,Lin Yiming,Bai Jinghan

Funder

National Natural Science Foundation of China under Grant

the Key Research and Development Project of Science and Technology Development Plan of Science and Technology Department of Jilin Province

National Key R &D Program of China

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Condensed Matter Physics

Link

https://link.springer.com/content/pdf/10.1007/s12650-022-00899-8.pdf

Reference51 articles.

1. Amnueypornsakul B, Bhat S, Chinprutthiwong P (2014) Predicting attrition along the way: the UIUC model. In: Proceedings of the EMNLP 2014 workshop on analysis of large scale social interaction in MOOCs, pp 55–59

2. Chen Q, Chen Y, Liu D, Shi C, Wu Y, Qu H (2016) Peakvizor: visual analytics of peaks in video clickstreams from massive open online courses. IEEE Trans Visual Comput Graph 22(10):2315–2330

3. Chen Q, Yue X, Plantaz X, Chen Y, Shi C, Pong T, Qu H (2020) Viseq: visual analytics of learning sequence in massive open online courses. IEEE Trans Visual Comput Graph 26(3):1622–1636

4. Chen Y,Chen Q, Zhao M, Boyer S, Veeramachaneni K, Qu H (2016) Dropoutseer: visualizing learning patterns in massive open online courses for dropout reasoning and prediction. In: 11th IEEE conference on visual analytics science and technology, IEEE VAST 2016, Baltimore, MD, USA, October 23–28, 2016. IEEE Computer Society, pp 111–120

5. Cheng F, Ming Y, Qu H (2021) DECE: decision explorer with counterfactual explanations for machine learning models. IEEE Trans Visual Comput Graph 27(2):1438–1447

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