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)


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.


MOOCs Massive Open Online Courses Learning Analytics Exploratory study 



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.


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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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