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Incorrect Weighting of Absolute Performance in Self-Assessment

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Book cover Advances in Machine Learning and Data Analysis

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 48))

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Abstract

Students spend much of their life in an attempt to assess their aptitude for numerous tasks. For example, they expend a great deal of effort to determine their academic standing given a distribution of grades. This research finds that students use their absolute performance, or percentage correct as a yardstick for their self-assessment, even when relative standing is much more informative. An experiment shows that this reliance on absolute performance for self-evaluation causes a misallocation of time and financial resources. Reasons for this inappropriate responsiveness to absolute performance are explored.

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Author information

Authors and Affiliations

  1. Department of Management and Marketing, Monmouth University, Leon Hess Business School, 400 Cedar Ave., West Long Branch, NJ, 00764-1898, US

    Scott A. Jeffrey

  2. Department of Management Sciences, University of Waterloo, 200 University Ave. West, Waterloo, Ontario, N2L 3G1, Canada

    Brian Cozzarin

Authors
  1. Scott A. Jeffrey
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  2. Brian Cozzarin
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Corresponding author

Correspondence to Scott A. Jeffrey .

Editor information

Editors and Affiliations

  1. Dept. Chemical Engineering, California State University, Long Beach, 90840, California, USA

    Mahyar A. Amouzegar

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Jeffrey, S.A., Cozzarin, B. (2010). Incorrect Weighting of Absolute Performance in Self-Assessment. In: Amouzegar, M. (eds) Advances in Machine Learning and Data Analysis. Lecture Notes in Electrical Engineering, vol 48. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3177-8_16

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  • DOI: https://doi.org/10.1007/978-90-481-3177-8_16

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-3176-1

  • Online ISBN: 978-90-481-3177-8

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