Explaining calibration accuracy in classroom contexts: the effects of incentives, reflection, and explanatory style

Hacker, Douglas J.; Bol, Linda; Bahbahani, Kamilla

doi:10.1007/s11409-008-9021-5

Cited by 174 publications

(199 citation statements)

References 31 publications

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“…For example, adults overestimate their skills and performance on tasks related to reasoning, humor, and grammar (Kruger & Dunning, 1999); children overestimate their ability to remember pictures (Lipko, Dunlosky, & Merriman, 2009) and to perform physical tasks (Schneider, 1998); and consumers overestimate how easy it will be to learn to use a new product (Billeter, Kalra, & Loewenstein, 2011). Most relevant to the present research, students often overestimate how well they will perform on an upcoming test of their learning (e.g., Hacker, Bol, & Bahbahani, 2008;Miller & Geraci, 2011a, b; but see Griffin, Jee, & Wiley, 2009;Shanks & Serra, 2014), which can impair their study behaviors (Metcalfe & Finn, 2008). The reasons for such overconfidence, however, are not yet well understood.…”

Section: Introductionmentioning

confidence: 79%

Unskilled and unaware in the classroom: College students’ desired grades predict their biased grade predictions

Serra

DeMarree

2016

Mem Cogn

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People tend to be overconfident when predicting their performance on a variety of physical and mental tasks (i.e., they predict they will perform better than they actually do). Such a pattern is commonly found in educational settings, in which many students greatly overestimate how well they will perform on exams. In particular, the lowest-performing students tend to show the greatest overconfidence (i.e., the Bunskilled-and-unaware^effect). Such overconfidence can have deleterious effects on the efficacy of students' shortterm study behaviors (i.e., underpreparing for exams) and long-term academic decisions (i.e., changing one's academic major to an Beasier^topic or dropping out of school completely). To help understand why students' grade predictions are often overconfident, we examined the hypothesis that students' grade predictions are biased by their desired levels of performance, which are often much higher than their actual levels of performance. Across three studies in which actual students made predictions about their exam performance in their courses, we demonstrated that students' grade predictions are highly biased by their desired grades on those exams. We obtained this result when students predicted their exam grades over a week before the exam (Study 1), immediately after taking the exam (Study 2), and across the four course exams in a single semester (Study 3). These results are informative for understanding why the Bunskilled-and-unawarep attern of performance predictions occurs, and why people in general tend to be overconfident when making both physical and mental performance predictions.

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Section: Introductionmentioning

confidence: 79%

Unskilled and unaware in the classroom: College students’ desired grades predict their biased grade predictions

Serra

DeMarree

2016

Mem Cogn

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“…Calibration is the relationship between performance and monitoring judgment on an item-by-item basis (Dunlosky & Thiede, 2013;Hacker, Bol, & Bahbahani, 2008;Nietfeld et al, 2006;Schraw, 2009). Therefore, calibration feedback provides information about the correctness of task performance as well as the accuracy of the metacognitive judgment regarding it.…”

Section: Interventions Fostering Metacognitionmentioning

confidence: 99%

Influence of Fluid Intelligence on Accuracy of Metacognitive Monitoring in Preschool Children Fades with the Calibration Feedback

Urban¹,

Urban²

2018

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Higher fluid intelligence leads to better accuracy in metacognitive monitoring, but in school age this influence is moderated by the child's development and education. The goal of the study is to examine the interaction between fluid intelligence and performance feedback or calibration feedback on monitoring accuracy in 88 preschool children. The children in the group that received performance (PF) or calibration feedback (CF) were significantly more accurate at monitoring than the children without feedback (NF). Fluid intelligence correlated with monitoring accuracy for the whole dataset and explained 49% of variance in monitoring accuracy in the NF group; 26% in the PF group (feedback alone explained 20%) and only 12% in the CF group, not reaching significance (however, feedback alone explained 26%). Results indicate that calibration feedback could potentially fulfil the role of later education and development in improving monitoring accuracy and moderate the effect of fluid intelligence already in preschoolers.

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“…For each of the two memory tasks, the recorded measures were (a) the number of correct responses for the cued-recall test (memory score); (b) the number of items predicted during the judgment phase (judgment score); and (c) the accuracy of children's predictions compared with their actual memory performance (calibration score). The latter score was computed using a formula adapted from the one developed by Hacker, Bol, and Bahbahani (2008). Using this equation, a score of 0 indicates perfect accuracy, a negative score indicates that children underestimated their memory performance, and a positive score indicates that children overestimated their memory performance.…”

Section: < Figure 1 About Here >mentioning

confidence: 99%

The effect of feedback on children’s metacognitive judgments: a heuristic account

Geurten

Meulemans

2016

Journal of Cognitive Psychology

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In three experiments, we investigated whether the feedback effect on the accuracy of children's metacognitive judgments results from an improvement in monitoring processes or the use of the Anchoring-and-Adjustment heuristic. Experiment 1 revealed that adding feedback increased the accuracy of young children's (aged 4, 6, and 8 years) memory prediction. In Experiment 2, the influence of an external anchor on children's metacognitive judgment was established. Finally, in Experiment 3, two memory tasks that differed in terms of difficulty were administered. Participants were randomly assigned to an anchoring (high/low/no anchor) and a feedback (feedback/no feedback) condition. Results demonstrated that children in the feedback condition adjusted their predictions toward the feedback, regardless of the task's difficulty. These findings are consistent with the hypothesis that external information provided by feedback is used as an anchor for judgment. This interpretation is strengthened by the correlation found between the two scores computed to assess participants' susceptibility to anchoring and feedback effects, which indicates that children who are more sensitive to the anchoring effect are also more sensitive to the feedback effect.Keywords: Metacognition, Judgment, Feedback, Anchoring, Children Running head: Feedback effect in children 3The Effect of Feedback on Children's Metacognitive Judgments: A Heuristic AccountDecades of intensive studies on metacognition have established that how accurately people assess their memory determines how efficiently they regulate their learning (Dunlosky & Hertzog, 1998; Kornell & Bjork, 2007; Kornell & Metcalfe, 2006;T. O. Nelson & Narens, 1994;Son, 2010;Thiede, 1996). Usually, the ability to assess internal cognitive processes (i.e., metacognitive monitoring) is appraised by asking participants to judge the quality of their performance before (prospective judgment) or after (retrospective judgment) carrying out a cognitive task. These metacognitive judgments can be made on an item-by-item basis (local level) or for the task as a whole (global level). The accuracy of judgments can be assessed with calibration scores (Van Overschelde & Nelson, 2006). This type of score shows the degree to which the judgment differs from the actual level of performance.Children's metacognitive judgments are far from accurate. Many developmental studies have demonstrated that children regularly overestimate their memory performance when making metacognitive judgments (e.g., Kvavilashvili & Ford, 2014; Lipko, Dunlosky, Lipowski, & Merriman, 2012). Interestingly, this effect seems to be particularly pronounced when the accuracy of global prospective judgments is examined (Lipko, Dunlosky, & Merriman, 2009;Shin, Bjorklund, & Beck, 2007;Yussen & Levy, 1975). For instance, Shin et al. (2007) presented three groups of children (aged 6, 7, and 9 years old) with a set of pictures, then asked them to estimate how many items they thought they would be able to remember on a subsequent test (glob...

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Explaining calibration accuracy in classroom contexts: the effects of incentives, reflection, and explanatory style

Cited by 174 publications

References 31 publications

Unskilled and unaware in the classroom: College students’ desired grades predict their biased grade predictions

Unskilled and unaware in the classroom: College students’ desired grades predict their biased grade predictions

Influence of Fluid Intelligence on Accuracy of Metacognitive Monitoring in Preschool Children Fades with the Calibration Feedback

The effect of feedback on children’s metacognitive judgments: a heuristic account

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