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Why is monitoring accuracy so poor in number line estimation? The importance of valid cues and systematic variability for U.S. college students

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Abstract

Metacognitive monitoring, recognizing when one is accurate or not, is important because judgments of one’s performance or knowledge often relate to control decisions, such as help seeking. Unfortunately, children and adults struggle to accurately monitor their performance during number-magnitude estimation. People’s accuracy in estimating number magnitudes is related to math achievement and health risk comprehension. Thus, poor monitoring of number-magnitude estimation performance could pose problems when completing math tasks or making health decisions. Here, we evaluated why monitoring accuracy was so poor during number-line estimation, whether it was greater in the presence of a cue that was predictive of performance or when accounting for spatial skills, and the relation between monitoring judgments and control. Monitoring accuracy was greater in a condition in which familiarity, a cue adults commonly rely on to monitor their performance in this task, was predictive of estimation accuracy, compared to a condition in which familiarity was misleading. Although indices of monitoring accuracy did not improve when accounting for spatial skills, reducing variability by dichotomizing estimation performance into accurate or not improved monitoring accuracy metrics. Accurate monitoring was important because adults were more likely to ask for help when they were less confident in their estimate. Taken together, our data on monitoring accuracy suggested that (a) using cues predictive of accuracy is important for monitoring in number-line estimation, (b) adults are poor at detecting small differences in their performance, and (c) prior estimates of monitoring accuracy in number-line estimation may underestimate people's true monitoring ability.

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Notes

  1. Note that screen resolution and CSS pixels often differ because screen resolution, which reflects screen size multiplied by pixel density, has increased in recent years. CSS pixel width and height is a more accurate reflection of screen size than resolution, or total number of pixels.

  2. If participants exceeded the time constraint, the window flashed and prompted them to: “Please respond within the time limit” but still permitted them to answer the item. Thus, there were no missing data on the number-line estimation task.

  3. Recall that continuous predictors were rescaled by dividing the within-person mean centered variables by 100. We rescaled here for interpretation.

  4. We rescaled confidence here to be on the same 100-point scale as PAE. Recall that in the primary analyses, we rescaled within-person mean centered confidence by dividing by 100.

  5. We predicted confidence judgments from condition and help seeking (yes or no) in a mixed effects model. Confidence was lower in the diagnostic than non-diagnostic cue conditions, b = -10.53, p < .001, and when participants asked for help compared to when they did not, b = -12.09, p < .001. An interaction (b = -15.41, p < .001) revealed that confidence was similar for small-component items between conditions but lower when participants asked for help in the diagnostic (M = 42.0, SE = 3.72) than non-diagnostic cue (M = 60.20, SE = 3.79) conditions. And, in the non-diagnostic cue condition, confidence was similar when they asked for or did not (M = 64.6, SE = 3.59) ask for help p = .096.

  6. Note that we multiplied gamma values by -1 because our measure of estimation accuracy was a degree of error. Thus, gamma values closer to 1 reflect more accurate monitoring with this coding.

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Acknowledgements

Support for this research was provided in part by the U.S. Department of Education, Institute of Education Sciences Grant R305A160295 and R305U200004 to Clarissa A. Thompson. We would like to thank members of the first authors’ dissertation, John Dunlosky, Nora Newcombe, Brad Morris, Chris Was, and Jeff Ciesla for their valued input on this work.

Funding

This research was funded by Institute of Education Sciences (IES) grants R305A160295 and R305U200004.

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  1. Department of Psychology, University of North Florida, 1 UNF Drive Building 51, Room 3209, Jacksonville, FL, 32224, USA

    Charles J. Fitzsimmons

  2. Department of Psychological Sciences, Kent State University, Kent, USA

    Clarissa A. Thompson

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Part of the data and writing in this study were included as the first authors’ dissertation. We report all measures included in the study and all data and analytic scripts are available on the OSF project page (https://osf.io/g7rxn/). All analyses were run in R. This study was pre-registered on OSF (https://osf.io/myqjp/).

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Fitzsimmons, C.J., Thompson, C.A. Why is monitoring accuracy so poor in number line estimation? The importance of valid cues and systematic variability for U.S. college students. Metacognition Learning 19, 21–52 (2024). https://doi.org/10.1007/s11409-023-09345-y

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