Skip to main content
SpringerLink
Log in

Weekly Predicting the At-Risk MOOC Learners Using Dominance-Based Rough Set Approach

  • Conference paper
  • First Online:
Book cover Digital Education: Out to the World and Back to the Campus (EMOOCs 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10254))

Included in the following conference series:

Abstract

This paper proposes a method based on the Dominance-based Rough Set Approach (DRSA) to predict the learners who are likely to drop out the course during the next week of the MOOC (Massive Open Online Course) based on their demographic and dynamic data of the previous week. These are called “At-risk Learners”. This method consists in two phases: the first aims at inferring a preference model while the second consists of classifying each learner either in the decision class Cl1 of “At-risk Learners” or in the decision class Cl2 of “Active Learners” based on the previously inferred preference model. The first phase is made of three steps: the first is to identify assignment examples of learners, the second is to construct a coherent criteria family for the learners’ profiles characterization and the third is to infer a preference model resulting in a set of decision rules. The two phases should be weekly implemented throughout the MOOC broadcast. This method has been validated on real data of a French MOOC proposed by a Business School in France.

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.onlinecoursereport.com/state-of-the-mooc-2016-a-year-of-massive-landscape-change-formassive-open-online-courses/.

  2. 2.

    http://idss.cs.put.poznan.pl.

References

  1. Balakrishnan, G.: Predicting student retention in massive open online courses using hidden markov models (Technical report No. UCB/EECS-2013-109). University of California at Berkeley, May 2013. http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-109.html

  2. Bouzayane, S., Saad, I.: A preference ordered classification to leader learners identification in a MOOC. J. Dec. Syst. 26(2), 189–202 (2016). http://dx.doi.org/10.1080/12460125.2017.1252233

  3. Bouzayane, S., Saad, I.: Prediction method based DRSA to improve the individual knowledge appropriation in a collaborative learning environment: case of MOOCs. In: Proceedings of the 50th Hawaii International Conference on System Sciences, pp. 124–133, January 2017

    Google Scholar 

  4. Bouzayane, S., Saad, I.: Multicriteria decision aid approach to “referent learners” identification within a MOOC. In: EMOOCs-2016, Research Track, pp. 237–250 (2016)

    Google Scholar 

  5. Chaplot, D., Rhim, E., Kim, J.: Predicting student attrition in MOOCs using sentiment analysis and neural networks. In: Proceedings of AIED 2015 Fourth Workshop on Intelligent Support for Learning in Groups (2015)

    Google Scholar 

  6. Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129, 1–47 (2001)

    Article  MATH  Google Scholar 

  7. Hone, K.S., El Said, G.R.: Exploring the factors affecting MOOC retention: a survey study. Comput. Educ. 98, 157–168 (2016). doi:10.1016/j.compedu.2016.03.016

    Article  Google Scholar 

  8. Moon, S., Potdar, S., Martin, L.: Identifying student leaders from MOOC discussion forums thorugh language influence. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 15–20 (2014)

    Google Scholar 

  9. North, S.M., Ronny, R., Max, M.: To adapt MOOCs, or not? That is no longer the question. Univ. J. Educ. Res. 2(1), 69–72 (2014)

    Google Scholar 

  10. Pawlak, Z.: Rough sets theory for multicriteria decision analysis. Int. J. Comput. Sci. 11, 341–356 (1992)

    Google Scholar 

  11. Roy, B., Mousseau, V.: A theoretical framework for analyzing the notion of relative importance of criteria. J. Multi-Criteria Dec. Anal. 5, 145–159 (1996)

    Article  MATH  Google Scholar 

  12. Xing, W., Chen, X., Stein, J., Marcinkowski, M.: Temporal predication of dropouts in MOOCs: reaching the low hanging fruit through stacking generalization. Comput. Hum. Behav. 58, 119–129 (2016)

    Article  Google Scholar 

  13. Yang, D., Sinha, T., Adamson, D., Rose, C.P.: Turn on, tune in, drop out: anticipating student dropouts in massive open online courses. In: NIPS Workshop on Data Driven Education (2013)

    Google Scholar 

  14. Yuan, L., Powell, S.: MOOCs and disruptive innovation: implications for higher education. eLearn. Pap. in-depth 33, 1–7 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MIS Laboratory, University of Picardie Jules Verne, Amiens, France

    Sarra Bouzayane & Inès Saad

  2. Amiens Business School, Amiens, France

    Inès Saad

  3. MIRACL Laboratory, Higher Institute of Computer Science and Multimedia, Sfax, Tunisia

    Sarra Bouzayane

Authors
  1. Sarra Bouzayane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Inès Saad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sarra Bouzayane .

Editor information

Editors and Affiliations

  1. Universidad Carlos III de Madrid, Leganés (Madrid), Spain

    Carlos Delgado Kloos

  2. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Patrick Jermann

  3. Pontifical Catholic University of Chile, San Joaquín, Santiago de Chile, Chile

    Mar Pérez-Sanagustín

  4. Harvard University, Cambridge, Massachusetts, USA

    Daniel T. Seaton

  5. University of Southampton, Southampton, United Kingdom

    Su White

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Bouzayane, S., Saad, I. (2017). Weekly Predicting the At-Risk MOOC Learners Using Dominance-Based Rough Set Approach. In: Delgado Kloos, C., Jermann, P., Pérez-Sanagustín, M., Seaton, D., White, S. (eds) Digital Education: Out to the World and Back to the Campus. EMOOCs 2017. Lecture Notes in Computer Science(), vol 10254. Springer, Cham. https://doi.org/10.1007/978-3-319-59044-8_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59044-8_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59043-1

  • Online ISBN: 978-3-319-59044-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation