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WPSS: dropout prediction for MOOCs using course progress normalization and subset selection

Authors:

Yuqian Chai,

Chi-Un Lei,

Xiao Hu,

Yu-Kwong KwokAuthors Info & Claims

L@S '18: Proceedings of the Fifth Annual ACM Conference on Learning at Scale

Article No.: 29, Pages 1 - 2

https://doi.org/10.1145/3231644.3231687

Published: 26 June 2018 Publication History

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Abstract

There are existing multi-MOOC level dropout prediction research in which many MOOCs' data are involved. This generated good results, but there are two potential problems. On one hand, it is inappropriate to use which week students are in to select training data because courses are with different durations. On the other hand, using all other existing data can be computationally expensive and inapplicable in practice.

To solve these problems, we propose a model called WPSS (WPercent and Subset Selection) which combines the course progress normalization parameter wpercent and subset selection. 10 MOOCs offered by The University of Hong Kong are involved and experiments are in the multi-MOOC level. The best performance of WPSS is obtained in neural network when 50% of training data is selected (average AUC of 0.9334). Average AUC is 0.8833 for traditional model without wpercent and subset selection in the same dataset.

References

[1]

Juan Miguel L Andres, Ryan S Baker, Dragan Gašević, George Siemens, Scott A Crossley, and Srećko Joksimović. 2018. Studying MOOC completion at scale using the MOOC replication framework. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge. ACM, 71--78.

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[2]

Sebastien Boyer and Kalyan Veeramachaneni. 2015. Transfer learning for predictive models in massive open online courses. In International Conference on Artificial Intelligence in Education. Springer, 54--63.

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[3]

Sherif Halawa, Daniel Greene, and John Mitchell. 2014. Dropout prediction in MOOCs using learner activity features. Experiences and best practices in and around MOOCs 7 (2014), 3--12.

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Jacob Whitehill, Kiran Mohan, Daniel Seaton, Yigal Rosen, and Dustin Tingley. 2017. Delving Deeper into MOOC Student Dropout Prediction. arXiv preprint arXiv:1702.06404 (2017).

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Cited By

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Koh EHu X(2023)Learning Analytics for Learning: Emerging International Trends and Case Studies from the Asia-PacificInternational Handbook on Education Development in the Asia-Pacific10.1007/978-981-19-6887-7_54(1367-1393)Online publication date: 16-Nov-2023
https://doi.org/10.1007/978-981-19-6887-7_54
Koh EHu X(2023)Learning Analytics for Learning: Emerging International Trends and Case Studies from the Asia-PacificInternational Handbook on Education Development in Asia-Pacific10.1007/978-981-16-2327-1_54-1(1-27)Online publication date: 23-May-2023
https://doi.org/10.1007/978-981-16-2327-1_54-1
Adnan MAlSaeed DAl-Baity HRehman A(2021)Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning ModelComplexity10.1155/2021/55197692021(1-21)Online publication date: 13-Jul-2021
https://doi.org/10.1155/2021/5519769
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Information

Published In

L@S '18: Proceedings of the Fifth Annual ACM Conference on Learning at Scale

June 2018

391 pages

ISBN:9781450358866

DOI:10.1145/3231644

Conference Chair:
Rose Luckin
UCL Institute of Education, UK
,
Program Chairs:
Scott Klemmer
University of California at San Diego
,
Kenneth Koedinger
Carnegie Mellon University

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: 26 June 2018

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University Research Committee, University of Hong Kong

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L@S '18

L@S '18: Fifth (2018) ACM Conference on Learning @ Scale

June 26 - 28, 2018

London, United Kingdom

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L@S '18 Paper Acceptance Rate 24 of 58 submissions, 41%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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Cited By

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Koh EHu X(2023)Learning Analytics for Learning: Emerging International Trends and Case Studies from the Asia-PacificInternational Handbook on Education Development in the Asia-Pacific10.1007/978-981-19-6887-7_54(1367-1393)Online publication date: 16-Nov-2023
https://doi.org/10.1007/978-981-19-6887-7_54
Koh EHu X(2023)Learning Analytics for Learning: Emerging International Trends and Case Studies from the Asia-PacificInternational Handbook on Education Development in Asia-Pacific10.1007/978-981-16-2327-1_54-1(1-27)Online publication date: 23-May-2023
https://doi.org/10.1007/978-981-16-2327-1_54-1
Adnan MAlSaeed DAl-Baity HRehman A(2021)Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning ModelComplexity10.1155/2021/55197692021(1-21)Online publication date: 13-Jul-2021
https://doi.org/10.1155/2021/5519769
Jin C(2021)Dropout prediction model in MOOC based on clickstream data and student sample weightSoft Computing10.1007/s00500-021-05795-1Online publication date: 17-Apr-2021
https://doi.org/10.1007/s00500-021-05795-1
Jin C(2020)MOOC student dropout prediction model based on learning behavior features and parameter optimizationInteractive Learning Environments10.1080/10494820.2020.180230031:2(714-732)Online publication date: 12-Aug-2020
https://doi.org/10.1080/10494820.2020.1802300
Qiu LLiu YLiu Y(2018)An Integrated Framework With Feature Selection for Dropout Prediction in Massive Open Online CoursesIEEE Access10.1109/ACCESS.2018.28812756(71474-71484)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2881275

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