Within and between person associations of calibration and achievement

Effects of self-scoring their math problem solutions on primary school students’ monitoring and regulation

“…Baars et al, 2014;Dunlosky & Rawson, 2012). Across studies, the average accuracy of primary school students' monitoring judgments varies enormously depending on the type of task (Boekaerts & Rozendaal, 2010;Rutherford, 2017) and students' age (accuracy is higher for older children; Destan & Roebers, 2015;Roebers et al, 2014). The present study focuses on selfregulated learning of problem-solving tasks in upper primary school, more specifically on fourth-grade students practicing computational tasks (US fourth grade is comparable to Dutch sixth grade; students of approximately 9-10 years old).…”

“…Unfortunately, primary school students' self-monitoring and self-regulation are often inaccurate, and researchers are looking for ways to help them improve these processes (e.g., Baars et al, 2014;García et al, 2016;Van Loon & Roebers, 2017). However, relatively little attention has been paid to self-monitoring, and especially self-regulation, when practicing with problem-solving tasks (van Gog et al, 2020), even though problem solving plays an important role in many primary and secondary school subjects such as mathematics and science (for exceptions, see Baars et al, 2014Baars et al, , 2018Boekaerts & Rozendaal, 2010;García et al, 2016;Rutherford, 2017). To be able to optimally support primary school students' self-monitoring and self-regulation when acquiring problem-solving skills, we need to gain a better understanding of their self-monitoring and self-regulation accuracy and what interventions are successful for improving accuracy.…”

Influence of Early Adolescents’ Mastery Goals on Inter- and Intra-Individual Relationships Between Metacognitive Monitoring and Academic Outcomes

“…Task difficulty can also affect the accuracy of individual’s metacognitive monitoring (Winne & Jamieson-Noel, 2002). Furthermore, Rutherford (2017) disentangled the within-person (i.e., cross-task variance) and between-person (i.e., prior test scores) components of metacognitive monitoring while predicting achievement outcomes. She found that both within- and between-person variance in metacognitive monitoring significantly predicted achievement for a full-year online mathematics curriculum.…”

“…Calibration has been regarded as a more objective measure than other competence beliefs because, in addition to confidently correct answers, it includes the judgment of unsure or incorrect answers (Dunlosky & Thiede, 2013; Winne & Jamieson-Noel, 2002). For this reason, calibration can be applied to specific learning contexts by capturing moment-to-moment changes in micro-level metacognitive monitoring (Rutherford, 2017). This strength tends to be maximized when measuring metacognition of younger students, who are vulnerable to positive bias when judging their cognitive competence (Hacker et al, 2008).…”

“…Despite its importance in primary school students’ learning, previous research has not paid much attention to the fact that calibration can be an outcome of the dynamic interaction between learning contexts and students’ personal motivation (Rutherford, 2017). Students’ calibration can be affected by different context- and domain-specific cues (Greene et al, 2015).…”