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Analysing Event Transitions to Discover Student Roles and Predict Grades in MOOCs

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Artificial Neural Networks and Machine Learning – ICANN 2017 (ICANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10614))

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Abstract

When interacting with a MOOC, students can perform different kinds of actions such as watching videos, answering exercises, participating in the course forum, submitting a project or reviewing a document. These actions represent the dynamism of student learning paths, and their preferences when learning in an autonomous mode. In this paper we propose to analyse these learning paths with two goals in mind. The first one is to try to discover the different roles that students may adopt when interacting with an online course. By applying k-means, six of these roles are discovered and we give a qualitative interpretation of them based on student information associated to each cluster. The other goal is to predict academic performance. In this sense, we present the results obtained with Random Forest and Neural Networks that allow us to predict the final grade with around 10% of mean absolute error.

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References

  1. Ashenafi, M.M., Riccardi, G., Ronchetti, M.: Predicting students’ final exam scores from their course activities. In: 2015 IEEE Frontiers in Education Conference (FIE), pp. 1–9 (2015)

    Google Scholar 

  2. Baker, R.S., Inventado, P.S.: Educational data mining and learning analytics. In: Larusson, J.A., White, B. (eds.) Learning Analytics, pp. 61–75. Springer, New York (2014). doi:10.1007/978-1-4614-3305-7_4

    Google Scholar 

  3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  4. Ducange, P., Pecori, R., Sarti, L., Vecchio, M.: Educational big data mining: how to enhance virtual learning environments. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds.) ICEUTE/SOCO/CISIS -2016. AISC, vol. 527, pp. 681–690. Springer, Cham (2017). doi:10.1007/978-3-319-47364-2_66

    Chapter  Google Scholar 

  5. Elias, T.: Learning Analytics: The Definitions, the Processes, and the Potential (2011)

    Google Scholar 

  6. Ezen-Can, A., Boyer, K.E., Kellogg, S., Booth, S.: Unsupervised modeling for understanding MOOC discussion forums: a learning analytics approach. In: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, LAK 2015, pp. 146–150. ACM (2015)

    Google Scholar 

  7. Ferguson, R., Clow, D.: Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs). In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, LAK 2015, pp. 51–58. ACM (2015)

    Google Scholar 

  8. Jiang, S., Williams, A., Schenke, K., Warschauer, M., O’dowd, D.: Predicting MOOC performance with week 1 behavior. In: Educational Data Mining 2014 (2014)

    Google Scholar 

  9. Lefevre, M., Guin, N., Marty, J.C., Clerc, F.: Personalization of MOOCs (2016)

    Google Scholar 

  10. Revelle, M., Domeniconi, C., Johri, A.: Persistent roles in online social networks. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS, vol. 9852, pp. 47–62. Springer, Cham (2016). doi:10.1007/978-3-319-46227-1_4

    Chapter  Google Scholar 

  11. Tseng, S.F., Tsao, Y.W., Yu, L.C., Chan, C.L., Lai, K.R.: Who will pass? Analyzing learner behaviors in MOOCs. Res. Pract. Technol. Enhanced Learn. 11(1), 8 (2016)

    Article  Google Scholar 

  12. Xu, B., Yang, D.: Motivation classification and grade prediction for MOOCs learners. In: Computational Intelligence and Neuroscience 2016 (2016)

    Google Scholar 

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Acknowledgments

The authors acknowledge financial support from the Spanish Ministerio de Economa y Competitividad (TIN2016-76406-P and from the UAM/IBM Chair).

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Authors and Affiliations

  1. Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain

    Ángel Pérez-Lemonche, Gonzalo Martínez-Muñoz & Estrella Pulido-Cañabate

Authors
  1. Ángel Pérez-Lemonche
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  2. Gonzalo Martínez-Muñoz
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  3. Estrella Pulido-Cañabate
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Corresponding author

Correspondence to Ángel Pérez-Lemonche .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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Pérez-Lemonche, Á., Martínez-Muñoz, G., Pulido-Cañabate, E. (2017). Analysing Event Transitions to Discover Student Roles and Predict Grades in MOOCs. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-68612-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68611-0

  • Online ISBN: 978-3-319-68612-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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