Test prediction and performance in a classroom context.

By Douglas J. Hacker, Linda Bol, Dianne D. Horgan, E. Rakow. 2000

496 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fd77035fdf5ae8d576afa2086eaa596c6d1b84bc

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Citations in the corpus

A Theoretical Review of Winne and Hadwin’s Model of Self-Regulated Learning: New Perspectives and Directions (2007, background): “that strong students monitor and calibrate their learning effectively, whereas lowachieving students do not (Garavalia & Ray, 2003; Hacker et al., 2000; Pintrich & De Groot, 1990; VanZile-Tamsen & Livingston, 1999). In addition, there is some evidence that the relations between conditions, operation”
Metacognitive Knowledge Monitoring and Self-Regulated Learning: Academic Success and Reflections on Learning. (2006, background): “Hacker et al. (2000) found significant differences between low performing students and high performing students in relation to their ability to predict (before taking a test) and postdict (after taking a test but before receiving their grade) their test results.”
A Comparison of the Effects of Practice Tests and Traditional Review on Performance and Calibration (2001, background): “Even though it was not the intent of the Hacker et al. (2000) study to determine the effects of practice tests on calibration, it did raise the question of whether gains in predictive accuracy might have resulted”
A Critical Review of Research on Student Self-Assessment (2019, background): “One suggested that performance and judgments of performance had little influence on subsequent test preparation behavior (Hacker et al., 2000), and the other showed that study participants followed their predictions of performance to the same degree, regardless of monitoring accuracy (van Loon et al”
Enhancing students’ self-regulation and mathematics performance: the influence of feedback and self-evaluative standards (2010, background): “Bol and Hacker 2001; Bol et al. 2005; Chen 2002; Ewers and Wood 1993; Hacker et al. 2000; Klassen 2006; Kruger and Dunning 1999; Pajares and Graham 1999; Pajares and Kranzler 1995; Winne and Jamieson-Noel 2002).”
Using strategy instruction and confidence judgments to improve metacognitive monitoring (2009, background): “At the college level the use of repeated test performance judgments in this response-oriented approach has revealed improvements for students for monitoring accuracy, and most notably for high achieving students ( Hacker et al. 2000 ).”
Training metacognition in the classroom: the influence of incentives and feedback on exam predictions (2011, background): “For example, in one study, students made predictions and postdictions on each of three different exams (Hacker et al. 2000).”
The effect of distributed monitoring exercises and feedback on performance, monitoring accuracy, and self-efficacy (2006, methodology): “Second, little research has tracked such effects over extended periods of time in an actual classroom setting (for an exception see Hacker et al., 2000).”
Miscalibration of self-efficacy and academic performance: Self-efficacy ≠ self-fulfilling prophecy (2019, background): “calibration more generally (Hacker et al., 2000; Lin & Zabrucky, 1998) Calibration plots and tests of differences between self-efficacy and performance at different performance levels illustrated a consistent tendency for weaker performers to be over-efficacious, and for stronger performers to be un”
Why the Unskilled Are Unaware: Further Explorations of (Absent) Self-Insight Among the Incompetent. (2008, unknown): “Hacker, Bol, Horgan, and Rakow (2000) provided direct evidence for this failure to learn from feedback when they tracked students during a semester-long class.”
The Influence of Overt Practice, Achievement Level, and Explanatory Style on Calibration Accuracy and Performance (2005, background): “Because calibration accuracy may be influenced by a more global self-concept of ability rather than actual performance data, this may be one reason why calibration is so resistant to change (Dembo & Jakubowski, 2003; Hacker et al., 2000; Roeser, Eccles, & Strobel, 1998).”