Skip to main content
SpringerLink
Log in

Predicting Student Examinee Rate in Massive Open Online Courses

  • Conference paper
  • First Online:
Book cover Database Systems for Advanced Applications (DASFAA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10179))

Included in the following conference series:

Abstract

Over the past few years, massive open online courses (a.b.a MOOCs) has rapidly emerged and popularized as a new style of education paradigm. Despite various features and benefits offered by MOOCs, however, unlike traditional classroom-style education, students enrolled in MOOCs often show a wide variety of motivations, and only quite a small percentage of them participate in the final examinations. To figure out the underlying reasons, in this paper, we make two key contributions. First, we find that being an examinee for a learner is almost a necessary condition of earning a certificate and hence investigation of the examinee rate prediction is of great importance. Second, after conducting extensive investigation of participants’ operation behaviours, we carefully select a set of features that are closely reflect participants’ learning behaviours. We apply existing commonly used classifiers over three online courses, generously provided by China University MOOC platform, to evaluate the effectiveness of the used features. Based on our experiments, we find there does not exist a single classifier that is able to dominate others in all cases, and in many cases, SVN performs the best.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.hep.edu.cn/.

  2. 2.

    http://www.icourse163.org/course/RUC-488001#/info.

  3. 3.

    http://scikit-learn.org/stable/.

  4. 4.

    The performance of SVM is fairly close to that of decision tree in Course B and Course C.

References

  1. Academic & university news — times higher education (the). https://www.timeshighereducation.com/news/mooc-completion-rates-below-7/2003710.article/

  2. China university MOOC. http://www.icourse163.org/

  3. Coursera. https://www.coursera.org

  4. Data driven education workshop. https://nips.cc/Conferences/2013/

  5. edX. https://www.edx.org/

  6. Udacity. https://cn.udacity.com/

  7. XuetangX. http://www.xuetangx.com/

  8. KDD Cup 2015, MOOC dropout prediction. https://biendata.com/competition/kddcup2015/

  9. Amnueypornsakul, B., Bhat, S., Chinprutthiwong, P.: Predicting attrition along the way: the UIUC model. In: Empirical Methods in Natural Language Processing Workshop on Modeling Large Scale Social Interaction in Massively Open Online Courses (2014)

    Google Scholar 

  10. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2010)

    MATH  Google Scholar 

  11. The Free Encyclopedia, Massive open online course. https://en.wikipedia.org/w/index.php?title=Massive_open_online_course&oldid=694372484/

  12. Fei, M., Yeung, D.: Temporal models for predicting student dropout in massive open online courses. In: IEEE International Conference on Data Mining Workshop, pp. 256–263 (2015)

    Google Scholar 

  13. Freedman, D. (ed.): Statistical Models Theory and Practice. Cambridge University Press, Cambridge (2009)

    MATH  Google Scholar 

  14. Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N.: Predicting MOOC dropout over weeks using machine learning methods. In: Empirical Methods in Natural Language Processing Workshop on Modeling Large Scale Social Interaction in Massively Open Online Courses (2014)

    Google Scholar 

  15. Luaces, O., Díez, J., Alonso-Betanzos, A., Lora, A.T., Bahamonde, A.: A factorization approach to evaluate open-response assignments in moocs using preference learning on peer assessments. Knowl.-Based Syst. 85, 322–328 (2015)

    Article  Google Scholar 

  16. Manhães, L.M.B., da Cruz, S.M.S., Zimbrão, G.: WAVE: an architecture for predicting dropout in undergraduate courses using EDM. In: Symposium on Applied Computing, pp. 243–247 (2014)

    Google Scholar 

  17. Nesterko, S.O., Seaton, D.T., Reich, J., McIntyre, J., Han, Q., Chuang, I.L., Ho, A.D.: Due dates in MOOCs: does stricter mean better? In: First (2014) ACM Conference on Learning @ Scale, L@S 2014, Atlanta, GA, USA, 4–5 March 2014, pp. 193–194 (2014)

    Google Scholar 

  18. Qiu, J., Tang, J., Liu, T.X., Gong, J., Zhang, C., Zhang, Q., Xue, Y.: Modeling and predicting learning behavior in MOOCs. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 93–102 (2016)

    Google Scholar 

  19. Ramesh, A., Goldwasser, D., Huang, B., Daumé III., H., Getoor, L.: Learning latent engagement patterns of students in online courses. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 27–31 July 2014, Québec City, Québec, Canada, pp. 1272–1278 (2014)

    Google Scholar 

  20. Shah, N.B., Bradley, J., Parekh, A., Wainwright, M.J., Ramchandran, K.: A case for ordinal peer-evaluation in MOOCs. In: Neural Information Processing Systems (NIPS): Workshop on Data Driven Education (2013)

    Google Scholar 

  21. Sharkey, M., Sanders, R.: A process for predicting MOOC attrition (2014)

    Google Scholar 

  22. She, J., Tong, Y., Chen, L.: Utility-aware social event-participant planning. In: SIGMOD 2015, pp. 1629–1643 (2015)

    Google Scholar 

  23. She, J., Tong, Y., Chen, L., Cao, C.C.: Conflict-aware event-participant arrangement and its variant for online setting. IEEE Trans. Knowl. Data Eng. 28(9), 2281–2295 (2016)

    Article  Google Scholar 

  24. Taylor, C., Veeramachaneni, K., O’Reilly, U.: Likely to stop? Predicting stopout in massive open online courses. CoRR, abs/1408.3382 (2014)

    Google Scholar 

  25. Tong, Y., She, J., Ding, B., Chen, L., Wo, T., Xu, K.: Online minimum matching in real-time spatial data: experiments and analysis. PVLDB 9(12), 1053–1064 (2016)

    Google Scholar 

  26. Tong, Y., She, J., Ding, B., Wang, L., Chen, L.: Online mobile micro-task allocation in spatial crowdsourcing. In: ICDE 2016, pp. 49–60 (2016)

    Google Scholar 

  27. Tong, Y., She, J., Meng, R.: Bottleneck-aware arrangement over event-based social networks: the max-min approach. World Wide Web 19(6), 1151–1177 (2016)

    Article  Google Scholar 

  28. Zhuoxuan, J., Yan, Z., Xiaoming, L.: Learning behavior analysis and prediction based on MOOC data. J. Comput. Res. Dev. 52(3), 614–628 (2015)

    Google Scholar 

Download references

Acknowledgements

Hong Chen is the corresponding author of this paper. The work in this paper was in part supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China under No. 297615121721, No. 297616331721, No. 2015-ms007, and No. 15XNLF09. Our experimental environment is in part supported by the National Virtual Experimental Teaching Center on Big Data Aided Comprehensive Training for Liberal Arts and Social Science, Renmin University of China.

Author information

Authors and Affiliations

  1. Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Renmin University of China, Beijing, China

    Wei Lu, Tongtong Wang, Min Jiao, Xiaoying Zhang, Shan Wang, Xiaoyong Du & Hong Chen

  2. School of Information, Renmin University of China, Beijing, China

    Wei Lu, Tongtong Wang, Min Jiao, Xiaoying Zhang, Shan Wang, Xiaoyong Du & Hong Chen

Authors
  1. Wei Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tongtong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaoyong Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Chen .

Editor information

Editors and Affiliations

  1. Royal Melbourne Institute of Technology , Melbourne, Australia

    Zhifeng Bao

  2. Northwestern University , Evanston, Illinois, USA

    Goce Trajcevski

  3. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  4. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Lu, W. et al. (2017). Predicting Student Examinee Rate in Massive Open Online Courses. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10179. Springer, Cham. https://doi.org/10.1007/978-3-319-55705-2_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55705-2_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55704-5

  • Online ISBN: 978-3-319-55705-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation