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What Massive Open Online Course (MOOC) Stakeholders Can Learn from Learning Analytics?

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Learning, Design, and Technology

Abstract

Massive open online courses (MOOCs) are the road that led to a revolution and a new era of learning environments. Educational institutions have come under pressure to adopt new models that assure openness in their education distribution. Nonetheless, there is still altercation about the pedagogical approach and the absolute information delivery to the students. On the other side with the use of Learning Analytics, powerful tools become available which mainly aim to enhance learning and improve learners’ performance. In this chapter, the development phases of a Learning Analytics prototype and the experiment of integrating it into a MOOC platform, called iMooX will be presented. This chapter explores how MOOC stakeholders may benefit from Learning Analytics as well as it reports an exploratory analysis of some of the offered courses and demonstrates use cases as a typical evaluation of this prototype in order to discover hidden patterns, overture future proper decisions, and to optimize learning with applicable and convenient interventions.

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Authors and Affiliations

  1. Educational Technology, Graz University of Technology, Münzgrabenstraße 35a, 8010, Graz, Austria

    Mohammad Khalil & Martin Ebner

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  1. Mohammad Khalil
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  2. Martin Ebner
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Correspondence to Mohammad Khalil .

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Editors and Affiliations

  1. College of Info, Ste G150, Univ of North Texas, Denton, Texas, USA

    Michael J Spector

  2. School of Education, Virginia Tech, Blacksburg, Virginia, USA

    Barbara B Lockee

  3. School of Education, Baker University, Overland Park, Kansas, USA

    Marcus D. Childress

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Khalil, M., Ebner, M. (2016). What Massive Open Online Course (MOOC) Stakeholders Can Learn from Learning Analytics?. In: Spector, M., Lockee, B., Childress, M. (eds) Learning, Design, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-17727-4_3-1

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  • DOI: https://doi.org/10.1007/978-3-319-17727-4_3-1

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  • Online ISBN: 978-3-319-17727-4

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