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Evaluation of Student Performance in Adaptive E-Learning Processes with Active Tutorship

Author

Listed:
  • Agostino Marengo

    (Osel Consulting S.R.L., University of Bari, Bari, Italy)

  • Alessandro Pagano

    (Osel Consulting S.R.L., University of Bari, Bari, Italy)

  • Alessio Barbone

    (Osel Consulting S.R.L., University of Bari, Bari, Italy)

Abstract

This paper describes the implementation of adaptive technology in a specific, Open Source, Learning Management System (LMS). After a preliminary study about the adaptive features already built-in and the capabilities ready to perform a suitable student modeling, the research team extended those capabilities with a specific data model, student model, and tutoring engine to perform automatic monitoring and sequencing of Learning Objects for each particular learner. Testing activities has proven the efficiency method in content and course delivery and given the opportunity to further develop a predicting tool based on data mining student modeling. This provides an efficient tool in tutorship activities. This paper describes some best practices developed during a Tempus IV Project granted by EU.

Suggested Citation

  • Agostino Marengo & Alessandro Pagano & Alessio Barbone, 2014. "Evaluation of Student Performance in Adaptive E-Learning Processes with Active Tutorship," International Journal of Technology Diffusion (IJTD), IGI Global, vol. 5(4), pages 35-49, October.
    • Handle: RePEc:igg:jtd000:v:5:y:2014:i:4:p:35-49
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