Hypermedia and Self-Regulation: An Interplay in Both Directions

  • Maria Opfermann
  • Katharina Scheiter
  • Peter Gerjets
  • Annett Schmeck
Part of the Springer International Handbooks of Education book series (SIHE, volume 28)


Rapid technological developments and growing interest in learning approaches other than traditional ones such as ex cathedra teaching have made hypermedia environments an increasingly popular learning device. Such environments have several advantages, but place demands on learners as well, such as requiring substantially more metacognitive and self-regulatory skills compared to structured and guided learning environments. For instance, learners should be able to check whether they learn with an appropriate combination of representations and whether their pace of information retrieval or navigation speed is appropriate. On the other hand, hypermedia environments can also support metacognitive and self-regulatory abilities and skills precisely because of their demands. When learners are not only passive recipients of information that is presented to them in bite-sized pieces but have to take decisions regarding their own learning process, active and constructive learning can be enhanced.

This chapter will first give an introduction on hypermedia, including its benefits and requirements. In a next step, we will go into more detail regarding theories and models of self-regulated learning that served as a framework for our own research on the effectiveness of hypermedia learning environment. This will finally be followed by sections discussing the “interplay in both directions,” that is, (a) what importance self-regulatory skills have for hypermedia learning and (b) how hypermedia environments could be designed and used to support self-regulated learning.


Cognitive Load Epistemological Belief Metacognitive Skill Cognitive Load Theory Metacognitive Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Maria Opfermann
    • 1
  • Katharina Scheiter
    • 2
  • Peter Gerjets
    • 2
  • Annett Schmeck
    • 1
  1. 1.Department of Instructional PsychologyDuisburg-Essen UniversityEssenGermany
  2. 2.Knowledge Media Research CenterTuebingenGermany

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