• DocumentCode
    240467
  • Title

    Investigating Students´ Interaction Profile in an Online Learning Environment with Clustering

  • Author

    Akcapynar, Gokhan ; Altun, Arif ; Cosgun, Erdal

  • Author_Institution
    Comput. Educ. & Instructional Technol., Hacettepe Univ., Ankara, Turkey
  • fYear
    2014
  • fDate
    7-10 July 2014
  • Firstpage
    109
  • Lastpage
    111
  • Abstract
    The aim of this study is to identify clusters of students who interact with an online learning environment in similar ways. The study included analyzing three-month interaction data from 74 undergraduates in the online learning environment using the Self Organizing Map (SOM) clustering method. The results of analysis revealed the existence of three distinct groups of students, labeled by their interaction (non-active, active, very active) and course success (low learning, medium learning, high learning). These are the preliminary results of the study and the cluster data which was obtained here is intended to be used in further studies for classifying new students or adaptation and personalization purposes.
  • Keywords
    computer aided instruction; pattern clustering; self-organising feature maps; SOM clustering method; cluster data; online learning environment; self organizing map clustering method; student interaction profile; Adaptation models; Artificial intelligence; Computational modeling; Computers; Data mining; Graphics; Navigation; clustering; interaction data; student behavior modeling; student profiling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on
  • Conference_Location
    Athens
  • Type

    conf

  • DOI
    10.1109/ICALT.2014.40
  • Filename
    6901411