Predicting Learners’ Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning

  • Jorge Maldonado-MahauadEmail author
  • Mar Pérez-Sanagustín
  • Pedro Manuel Moreno-Marcos
  • Carlos Alario-Hoyos
  • Pedro J. Muñoz-Merino
  • Carlos Delgado-Kloos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11082)


In the past years, predictive models in Massive Open Online Courses (MOOCs) have focused on forecasting learners’ success through their grades. The prediction of these grades is useful to identify problems that might lead to dropouts. However, most models in prior work predict categorical and continuous variables using low-level data. This paper contributes to extend current predictive models in the literature by considering coarse-grained variables related to Self-Regulated Learning (SRL). That is, using learners’ self-reported SRL strategies and MOOC activity sequence patterns as predictors. Lineal and logistic regression modelling were used as a first approach of prediction with data collected from N = 2,035 learners who took a self-paced MOOC in Coursera. We identified two groups of learners: (1) Comprehensive, who follow the course path designed by the teacher; and (2) Targeting, who seek for the information required to pass assessments. For both type of learners, we found a group of variables as the most predictive: (1) the self-reported SRL strategies ‘goal setting’, ‘strategic planning’, ‘elaboration’ and ‘help seeking’; (2) the activity sequences patterns ‘only assessment’, ‘complete a video-lecture and try an assessment’, ‘explore the content’ and ‘try an assessment followed by a video-lecture’; and (3) learners’ prior experience, together with the self-reported interest in course assessments, and the number of active days and time spent in the platform. These results show how to predict with more accuracy when students reach a certain status taking in to consideration not only low-level data, but complex data such as their SRL strategies.


Self-regulated learning Prediction Massive Open Online Courses Sequence patterns Achievement Success 



This work was supported by FONDECYT (Chile) under project initiation grant No.11150231, the MOOC-Maker Project (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP), the LALA Project (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and CONICYT/DOCTORADO NACIONAL 2016/21160081, the Spanish Ministry of Education, Culture and Sport, under an FPU fellowship (FPU016/00526) and the Spanish Ministry of Economy and Competiveness (Smartlet project, grant number TIN2017-85179-C3-1-R) funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jorge Maldonado-Mahauad
    • 1
    • 3
    Email author
  • Mar Pérez-Sanagustín
    • 1
    • 4
  • Pedro Manuel Moreno-Marcos
    • 2
  • Carlos Alario-Hoyos
    • 2
  • Pedro J. Muñoz-Merino
    • 2
  • Carlos Delgado-Kloos
    • 2
  1. 1.Department of Computer SciencePontificia Universidad Católica de ChileSantiagoChile
  2. 2.Department of Telematics EngineeringUniversidad Carlos III de MadridMadridSpain
  3. 3.Department of Computer ScienceUniversity of CuencaCuencaEcuador
  4. 4.Institut de Recherche en Informatique de ToulouseUniversité Tolouse III Paul SabatierToulouseFrance

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