A pretest-training-posttest design assessed whether training to improve spatial skills also improved mathematics performance in elementary-aged children. First grade students (mean age ϭ 7 years, n ϭ 134) and sixth grade students (mean age ϭ 12 years, n ϭ 124) completed training in 1 of 2 spatial skills-spatial visualization or form perception/VSWM-or in a nonspatial control condition that featured language arts training. Spatial training led to better overall mathematics performance in both grades, and the gains were significantly greater than for language arts training. The same effects were found regardless of spatial training type, or the type of mathematics tested. Educational Impact and Implications StatementWe tested whether children's mathematics scores could be improved with training in spatial skills (like, imagining objects rotating or remembering locations), based on previous research showing a connection between math and spatial skill. We found that spatial training improved math scores for children in first and sixth grade, and it did not seem to matter what kind of spatial practice we used or what specific math problems we tested. Our findings suggest that when children solve mathematics problems, they may recruit spatial thinking to help them, so teachers might use spatial tasks as a warm-up before mathematics instruction, or provide extra spatial practice to students who show weak spatial skills.

The Child Behavior Checklist 1.5–5 (CBCL 1.5–5) is applied to identify emotional and behavioral problems on children with developmental disabilities (e.g., autism spectrum disorder [ASD] and developmental delays [DD]). To understand whether there are variations between these two groups on CBCL DSM-oriented scales, we took two invariance analyses on 443 children (228 children with ASD). The first analysis used measurement invariance and multiple-group factor analysis on the test structure. The second analysis used item-level analysis, i.e., differential item functioning (DIF), to discover whether group memberships responded differently on some items even though underlying trait levels were the same. It was discovered that, on the test structure, the Anxiety Problems scale did not achieve metric invariance. The other scales achieved metric invariance; DIF analyses further revealed that there were items that functioned differently across subscales. These DIF items were mostly about children’s reactions to the surrounding environment. Our findings provide implications for clinicians to use CBCL DSM-oriented scales on differentiating children with ASD and children with DD. In addition, researchers need to be mindful about how items were responded differently, even though there were no mean differences on the surface.

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Several risk factors are related to glycemic control in patients with type 2 diabetes mellitus (T2DM), including demographics, medical conditions, negative emotions, lipid profiles, and heart rate variability (HRV; to present cardiac autonomic activity). The interactions between these risk factors remain unclear. This study aimed to use machine learning methods of artificial intelligence to explore the relationships between various risk factors and glycemic control in T2DM patients. The study utilized a database from Lin et al. (2022) that included 647 T2DM patients. Regression tree analysis was conducted to identify the interactions among risk factors that contribute to glycated hemoglobin (HbA1c) values, and various machine learning methods were compared for their accuracy in classifying T2DM patients. The results of the regression tree analysis revealed that high depression scores may be a risk factor in one subgroup but not in others. When comparing different machine learning classification methods, the random forest algorithm emerged as the best-performing method with a small set of features. Specifically, the random forest algorithm achieved 84% accuracy, 95% area under the curve (AUC), 77% sensitivity, and 91% specificity. Using machine learning methods can provide significant value in accurately classifying patients with T2DM when considering depression as a risk factor.