el 16(10): e4

Research Article

Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

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  • @ARTICLE{10.4108/eai.11-4-2016.151151,
        author={Radhika M. Pai and Sucheta V. Kolekar and Manohara Pai M. M.},
        title={Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning},
        journal={EAI Endorsed Transactions on e-Learning},
        volume={3},
        number={10},
        publisher={EAI},
        journal_a={EL},
        year={2016},
        month={4},
        keywords={Web Log Analysis, Felder-Silverman Learning Style Model, Adaptive E-learning Systems, XML, Data Pre-processing, Sequences, Adaptive User Interface etc.},
        doi={10.4108/eai.11-4-2016.151151}
    }
    
  • Radhika M. Pai
    Sucheta V. Kolekar
    Manohara Pai M. M.
    Year: 2016
    Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning
    EL
    EAI
    DOI: 10.4108/eai.11-4-2016.151151
Radhika M. Pai1, Sucheta V. Kolekar1,*, Manohara Pai M. M.1
  • 1: Department of Information and Communication Technology, Manipal Institute of Technology, Manipal University, Manipal, Karnataka, India
*Contact email: kolekar.sucheta@gmail.com

Abstract

Adaptive E-learning Systems (AESs) enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM). This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.