Metacognition and Learning

, Volume 14, Issue 2, pp 215–228 | Cite as

Accuracy in judgments of study time predicts academic success in an engineering course

  • Justin G. Gyllen
  • Thomas F. StahovichEmail author
  • Richard E. Mayer
  • Amirali Darvishzadeh
  • Negin Entezari


The present work examines the accuracy of self-reports of study time for college students. In a 10-week Mechanical Engineering course, 99 college students accessed their textbook, homework solutions, graded work, and lecture slides via custom software that recorded objective measures of reading time. In addition, the students provided subjective judgments of the time they spent reading these materials. Comparisons between the objective and subjective measures reveal that students significantly overestimated time with the textbook, homework solutions, graded work, and lecture slides, with higher performing students overestimating to a lesser degree. The difference between objective and subjective judgments of study time correlated significantly and negatively with final course grade for the textbook (r = −.31), homework solutions (r = −.39), and lecture slides (r = −.24), but not for graded work (r = −.05). This study calls into question the utility of self-report data in studies of student study habits, and showcases the value of objective technology-based measures of such habits.


Study strategies Evaluation methodologies Interactive learning environments Learning management systems 


Funding information

This project was supported by the National Science Foundation under Award Numbers 0935239, 1432820, and 1612511.

Compliance with ethical standards

The authors listed on this manuscript declare that they have no conflicts of interest to report.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Justin G. Gyllen
    • 1
  • Thomas F. Stahovich
    • 1
    Email author
  • Richard E. Mayer
    • 2
  • Amirali Darvishzadeh
    • 3
  • Negin Entezari
    • 3
  1. 1.Department of Mechanical Engineering, Bourns College of EngineeringUniversity of CaliforniaRiversideUSA
  2. 2.Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraUSA
  3. 3.Department of Computer Science and Engineering, Bourns College of EngineeringUniversity of CaliforniaRiversideUSA

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