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Analyzing Learners’ Behavior Beyond the MOOC: An Exploratory Study

  • Mar Pérez-SanagustínEmail author
  • Kshitij Sharma
  • Ronald Pérez-Álvarez
  • Jorge Maldonado-Mahauad
  • Julien Broisin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11722)

Abstract

Most of literature on massive open online courses (MOOCs) have focused on describing and predicting learner’s behavior with course trace data. However, little is known on the external resources beyond the MOOC they use to shape their learning experience, and how these interactions relate with their success in the course. This paper presents the results of an exploratory study that analyzes data from 572 learners in 4 MOOCs to understand (1) what the learners’ activities beyond the MOOC are, and (2) how they relate with their course performance. We analyzed frequencies of the students’ individual activities in and beyond the MOOC, and the transitions between these activities. Then, we analyzed the time spent on outside the MOOC content as well as the nature of this content. Finally, we predict which transitions better predict final learners’ grades. The results show that we can predict accurately students’ grades of the course using only internal-course fine-grained data of student’s interactions with video-lectures and exams combined with trace data of interactions with content outside the MOOCs. Also, data shows that learners spent 75% of their time on the MOOC, but go frequently to other content, mainly social networking sites, mail boxes and search engines.

Keywords

MOOCs Massive Open Online Courses Learning Analytics Exploratory study 

Notes

Acknowledgments

This work was supported by FONDECYT (11150231), University of Costa Rica (UCR), CONICYT Doctorado Nacional 2017/21170467, and CONICYT Doctorado Nacional 2016/21160081, the project “Analítica del aprendizaje para el estudio de estrategias de aprendizaje autorregulado en un contexto de aprendizaje híbrido - DIUC_XVIII_2019_54” financiado por la Dirección de Investigación de la Universidad de Cuenca (DIUC), Cuenca-Ecuador, and the LALA project (grant no. 586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP). This project has been funded with support from the European Commission. This publication reflects the views only of the author, and the Commission and the Agency cannot be held responsible for any use which may be made of the information contained therein.

References

  1. 1.
    Agapito, J.B., Sosnovsky, S., Ortigosa, A.: Detecting symptoms of low performance using production rules. In: Educational Data Mining, July 2009Google Scholar
  2. 2.
    Alario-Hoyos, C., Pérez-Sanagustín, M., Delgado-Kloos, C., Munoz-Organero, M.: Delving into participants’ profiles and use of social tools in MOOCs. IEEE Trans. Learn. Technol. 7(3), 260–266 (2014)CrossRefGoogle Scholar
  3. 3.
    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. IEEE, October 2015Google Scholar
  4. 4.
    Brinton, C.G., Chiang, M.: MOOC performance prediction via clickstream data and social learning networks. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 2299–2307 (2015)Google Scholar
  5. 5.
    Chen, G., Davis, D., Lin, J., Hauff, C., Houben, G.J.: Beyond the MOOC platform: gaining insights about learners from the social web. In: Proceedings of the 8th ACM Conference on Web Science, pp. 15–24. ACM, Hannover, May 2016Google Scholar
  6. 6.
    Cruz-Benito, J., Borrás-Genè, O., García-Peñalvo, F.J., Blanco, Á.F., Therón, R.: Extending MOOC ecosystems using web services and software architectures. In: Proceedings of the XVI ACM International Conference on Human Computer Interaction, pp. 52–57, September 2015Google Scholar
  7. 7.
    Elbadrway, A., Studham, R.S., Karypis, G.: Collaborative multi-regression models for predicting students’ performance in course activities. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, pp. 103–107. ACMl, March 2015Google Scholar
  8. 8.
    Kizilcec, R.F., Brooks, C.: Diverse big data and randomized field experiments in MOOCs. In: Lang, C., Siemens, G., Wise, A., Gašević, D. (eds.) Handbook of Learning Analytics, pp. 211–222. Society for Learning Analytics Research (2017)Google Scholar
  9. 9.
    Kizilcec, R.F., Pérez-Sanagustín, M., Maldonado, J.J.: Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Comput. Educ. 104, 18–33 (2017)CrossRefGoogle Scholar
  10. 10.
    Littlejohn, A., Hood, N., Milligan, C., Mustain, P.: Learning in MOOCs: motivations and self-regulated learning in MOOCs. Internet High. Educ. 29, 40–48 (2016)CrossRefGoogle Scholar
  11. 11.
    Maldonado-Mahauad, J., Pérez-Sanagustín, M., Moreno-Marcos, P.M., Alario-Hoyos, C., Muñoz-Merino, P.J., Delgado-Kloos, C.: Predicting learners’ success in a self-paced MOOC through sequence patterns of self-regulated learning. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) EC-TEL 2018. LNCS, vol. 11082, pp. 355–369. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-98572-5_27CrossRefGoogle Scholar
  12. 12.
    Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R.F., Morales, N., Munoz-Gama, J.: Mining theory-based patterns from big data: identifying self-regulated learning strategies in Massive Open Online Courses. Comput. Hum. Behav. 80, 179–196 (2018)CrossRefGoogle Scholar
  13. 13.
    Michinov, N., Brunot, S., Le Bohec, O., Juhel, J., Delaval, M.: Procrastination, participation, and performance in online learning environments. Comput. Educ. 56(1), 243–252 (2011)CrossRefGoogle Scholar
  14. 14.
    Liu, M., McKelroy, E., Kang, J., Harron, J., Liu, S.: Examining the use of Facebook and Twitter as an additional social space in a MOOC. Am. J. Distance Educ. 30(1), 14–26 (2016).  https://doi.org/10.1080/08923647.2016.1120584CrossRefGoogle Scholar
  15. 15.
    Moreno-Marcos, P.M., Alario-Hoyos, C., Muñoz-Merino, P.J., Kloos, C.D.: Prediction in MOOCs: a review and future research directions. IEEE Trans. Learn. Technol. (2018)Google Scholar
  16. 16.
    Oura, H., Anzai, Y., Fushikida, W., Yamauchi, Y.: What would experts say about this?: An analysis of student interactions outside MOOC platform. In: Proceedings of the 11th International Conference on Computer Supported Collaborative Learning (CSCL 2015), Gothenburg, Sweden, vol. 2, pp. 711–712 (2015)Google Scholar
  17. 17.
    Pérez-Álvarez, R., Pérez-Sanagustín, M., Maldonado-Mahauad, J.J.: NoteMyProgress: supporting learners’ self-regulated strategies in MOOCs. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 517–520. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66610-5_53CrossRefGoogle Scholar
  18. 18.
    Pérez-Álvarez, R., Maldonado-Mahauad, J., Pérez-Sanagustín, M.: Design of a tool to support self-regulated learning strategies in MOOCs. J. Univ. Comput. Sci. (JUCS) 24(8), 1090–1109 (2018)Google Scholar
  19. 19.
    Ren, Z., Rangwala, H., Johri, A.: Predicting performance on MOOC assessments using multi-regression models. arXiv preprint arXiv:1605.02269 (2016)
  20. 20.
    Schraw, G., Wadkins, T., Olafson, L.: Doing the things we do: a grounded theory of academic procrastination. J. Educ. Psychol. 99(1), 12 (2007)CrossRefGoogle Scholar
  21. 21.
    Veletsianos, G., Collier, A., Schneider, E.: Digging deeper into learners’ experiences in MOOCs: participation in social networks outside of MOOCs, notetaking and contexts surrounding content consumption. Br. J. Educ. Technol. 46(3), 570–587 (2015)CrossRefGoogle Scholar
  22. 22.
    Van Treeck, T., Ebner, M.: How useful is twitter for learning in massive communities? An analysis of two MOOCs. In: Twitter & Society, pp. 411–424 (2013)Google Scholar
  23. 23.
    Xing, W., Du, D.: Dropout prediction in MOOCs: using deep learning for personalized intervention. J. Educ. Comput. Res. (2018).  https://doi.org/10.1177/0735633118757015CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mar Pérez-Sanagustín
    • 1
    • 3
    Email author
  • Kshitij Sharma
    • 2
  • Ronald Pérez-Álvarez
    • 3
    • 4
  • Jorge Maldonado-Mahauad
    • 3
    • 5
  • Julien Broisin
    • 1
  1. 1.Institut de Recherche en Informatique de Toulouse (IRIT)Université Paul Sabatier Toulouse IIIToulouseFrance
  2. 2.Norwegian University of Science and Technology, Department of Computer ScienceTrondheimNorway
  3. 3.Pontificia Universidad Católica de ChileSantiagoChile
  4. 4.Universidad de Costa RicaPuntarenasCosta Rica
  5. 5.Universidad de Cuenca, Department of Computer ScienceCuencaEcuador

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