How Mastery and Performance Goals Influence Learners’ Metacognitive Help-Seeking Behaviours When Using Ecolab II

  • Amanda Carr (nee Harris)
  • Rose Luckin
  • Nicola Yuill
  • Katerina Avramides
Part of the Springer International Handbooks of Education book series (SIHE, volume 28)


The Ecolab software is an interactive learning environment for 10–11-year-old learners designed to help children learn about food chains and food webs. In the current chapter, we discuss the results of our recent work on achievement goal orientation and help seeking within the Ecolab environment. We situate these results within the broader landscape of our previous studies and discuss the evolutionary approach we have adopted to develop a methodology to support the design of metacognitive learning tools. This methodology has been built up over a series of empirical studies with the Ecolab software that have demonstrated that children who achieved above average learning gains use a high level of system help. In the empirical work that we focus upon in this chapter, we investigate the relationships between young learners’ metacognition: specifically their help-seeking behaviour and their achievement goal orientations. This work draws together and extends two strands of our previous research: metacognitive software scaffolding (Luckin and Hammerton. Getting to know me: Helping learners understand their own learning needs through metacognitive scaffolding. Berlin: Springer Verlag, 2002) and the influence of goal orientation on children’s learning (Harris, Yuill, & Luckin. British Journal of Educational Psychology, 78(3):355–374, 2008). Our research with Ecolab shows how tracking metacognitive behaviours—choice and use of more or less specific help—in the light of children’s goal orientations, can be used to support learning.


Goal Orientation Performance Goal Mastery Goal Learning Gain Metacognitive Process 
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

  • Amanda Carr (nee Harris)
    • 1
  • Rose Luckin
    • 2
  • Nicola Yuill
    • 3
  • Katerina Avramides
    • 2
  1. 1.Department of Applied Social SciencesCanterbury Christ Church UniversityCanterburyUK
  2. 2.The London Knowledge LabInstitute of EducationLondonUK
  3. 3.School of PsychologyUniversity of Sussex, FalmerBrightonUK

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