Making Use of Data for Assessments: Harnessing Analytics and Data Science

  • Dirk IfenthalerEmail author
  • Samuel GreiffEmail author
  • David GibsonEmail author
Reference work entry
Part of the Springer International Handbooks of Education book series (SIHE)


The increased availability of vast and highly varied amounts of data from learners, teachers, learning environments, and administrative systems within educational settings is overwhelming. The focus of this chapter is on how data with a large number of records, of widely differing datatypes, and arriving rapidly from multiple sources can be harnessed for meaningful assessments and supporting learners in a wide variety of learning situations. Distinct features of analytics-driven assessments may include self-assessments, peer assessments, and semantic rich and personalized feedback as well as adaptive prompts for reflection. The chapter concludes with future directions in the broad area of analytics-driven assessments for teachers and educational researchers.


Assessment analytics Learning analytics Formative assessment Large-Scale assessment Data analytics 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Learning, Design and TechnologyUniversity of MannheimMannheimGermany
  2. 2.University of LuxembourgLuxembourg CityLuxembourg
  3. 3.Curtin UniversityBentleyAustralia
  4. 4.Curtin UniversityPerthAustralia

Section editors and affiliations

  • Mary Webb
    • 1
  • Dirk Ifenthaler
    • 2
  1. 1.King's College LondonLondonUK
  2. 2.University of MannheimMannheimGermany

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