A Systematic Review on the Impact of Hot and Cool Executive Functions on Pediatric Injury Risks: a Meta-Analytic Structural Equation Modeling Approach.

Injury is a leading cause of morbidity and mortality among children in the USA. Understanding the impact of executive functions (EFs) on the risk of injuries is crucial for developing effective interventions. However, literature has failed to examine the relationship between multiple EFs and injury domains. The present paper quantitatively synthesized research on cool and hot EFs and children's intentional and unintentional injury risks using a novel meta-analytic structural equation modeling (MASEM) approach. A systematic review was conducted in the following databases: PsycINFO, Scopus, SafetyLit, Cochrane Central, and PubMed (Medline). After screening titles, abstracts, and full texts, a total of 31 studies were eligible for the MASEM analysis. One-stage MASEM was conducted on six conceptualized path analysis models according to the complexity of exogenous and endogenous variables. The MASEM models suggested that hot and cool EFs were negatively associated with children's risk of injury or injury-related risk behaviors regardless of mean age and proportion of females. Among cool EF skills, inhibitory control, but not working memory or cognitive flexibility, was significantly associated with risks of unintentional injuries. Emotion regulation was the dominant hot EF skill examined in the literature and was found significantly associated with risks of non-suicidal self-injuries (NSSIs). EF has a significant impact on children's risk of both unintentional and intentional injuries. Future research should focus on the combined force of hot and cool EF on children's risks of injuries and injury-related risk behaviors.

Too much of a good thing: frequent retrieval can impair immediate new learning.

Interpolated testing can reduce mind-wandering and proactive interference, and improve note-taking. However, recent research using face-name-profession triads, has also shown that interpolated testing can impair new learning (Davis, Chan, & Wilford, 2017). In the current study, we further examined the impact of switching from testing to new learning, but with objectively-true materials. The study employed a 2 (Interpolated task: Test vs. Restudy) × 3 (Task-switch frequency: 0, 11, 35) between-participants design. In two experiments, participants restudied or retrieved originally-learned flag-country associations and learned new flag-capital (Experiment 1) or flag-export (Experiment 2) associations. Task-switch frequency varied such that participants switched to new learning trial(s) after every restudy/test trial (35-switches), after every three restudy/test trials (11-switches), or did not switch at all (0-switch). The results further demonstrate that retrieving previously-learned material can impair learning of new associations by replicating Davis et al. (2017) with objectively-true materials.

Low-Performing Students Confidently Overpredict Their Grade Performance throughout the Semester.

When asked to predict how they will perform on an upcoming exam, students are often poorly calibrated, typically in the direction of overpredicting their performance. Research shows that low-performing students' calibration tends to remain poor across multiple tests over the course of a semester. We tested whether these students remain confident in these erroneously high grade predictions across the semester or whether their confidence wanes, suggesting some degree of metacognitive awareness. In two studies, students made grade predictions prior to taking four in-class exams and then rated their level of confidence in their predictions. Results from both studies showed that miscalibration and confidence remained stable across tests, suggesting that low-performing students continued to believe that they would perform well on upcoming exams despite prior evidence to the contrary.

Metacognitive errors in the classroom: The role of variability of past performance on exam prediction accuracy.

Students often make incorrect predictions about their exam performance, with the lowest-performing students showing the greatest inaccuracies in their predictions. The reasons why low-performing students make inaccurate predictions are not fully understood. In two studies, we tested the hypothesis that low-performing students erroneously predict their exam performance in part because their past performance varies considerably, yielding unreliable data from which to make their predictions. In contrast, high-performing students tend to have consistently high past performance that they can rely on to make relatively accurate predictions of future test performance. Results showed that across different exams (Study 1) and different courses (Study 2), low-performing students had more variable past performance than high-performing students. Further, results from Study 2 showed that variability in past course performance (but not past exam performance) was associated with poor calibration. Results suggest that variability in past performance may be one factor that contributes to low-performing students' erroneous performance predictions.