Mooclive

Identification of online courses Users behavioural patterns




CONTEXT

  • This R&D project was funded by the ANR (ANR, fr: National Agency for Research) with the ambition to give a significant boost to higher eduction online programs.
  • Finding an efficient way to identify behavioural patterns of MOOC users community is a recurring issue in e-learning.

The experiments were conducted on MOOCs published in the framework of the research project #MOOCLive under the leadership of the Centre Virchow-Villerme for Public Health. The project aimed to substantially improve the efficiency of the MOOCs through a deeper understanding of the participants and their behaviors..

Thanks to an iterative process, users behaviors understanding was refined. Our objective is to introduce a procedure that can be applied irrespectively of the experts classication.

SOLUTION

With this project, Aldwin focuses its research to users automatic modeling using Machine Learning algorithms with the ambition to help instructors in their process of creating better educational contents.

RESULT

Within this R&D project, Aldwin managed to :

  • Develop a process of mixed research using both quantitative and qualitative methods.
  • Users modeling through the use of information from log files provided from the MOOC plateform called MOOC FUN (FUN : France Université Numérique).
  • Extraction of behavioral and performance variables.
  • Identification of the typical journey for each type of learner’s groups thanks to Process Mining.
    This approach, developed by Aldwin by ANEO, mixes qualitative elements and innovative data science methods and provides a more detailed understanding of users.



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