Effects of Day-Care on Cognitive and Socioemotional Competence of Thirteen-Year-Old Swedish Schoolchildren

This is a follow-up study of an earlier one in which positive effects of early day-care experience were found on children's cognitive and socioemotional competence at age 8. 128 children were followed from their first year of life. At 8 and 13 years of age, 92% and 89% of the children, respectively, remained in the study. Most children could be classified according to age at first entry into day-care. Cognitive and socioemotional competence was rated by the children's classroom teachers. Hierarchical regression and path analyses were used in the statistical treatment of the data. It was possible to trace independent positive effects of age of entry into day-care as far as age 13. Children entering center care or family day-care before age 1 generally performed better in school when 8 and 13 years old and received more positive ratings from their teachers on several socioemotional variables. The path analyses indicated the following causal model: family characteristics, such as type of family, family's socioemotional status, and mother's educational level, influence the time of first entry into day-care. This variable, in turn, has consequences for children's competence at 8 and/or 13 years of age even after controlling for home background, child gender, and intelligence, which, of course, have their own effects. The effect of socioeconomic status was often mediated through age of entry into day-care.     

Do you want your child to have a certified teacher?

This study examines how the teaching staff composition with respect to certification affects student achievement in compulsory Swedish schools. We apply an instrumental variable to estimate the effect of the share of non-certified teachers on student achievement (measured by grade point average, GPA). We find statistically significant negative effects on the GPA. The effect is stronger for students with highly educated parents. A one percentage point increase in the share of non-certified teachers is expected to decrease student's GPA by, on average, 1.8 standard deviations per year. This is a substantial effect considering the large differences in the shares of non-certified teachers across schools and municipalities.     

Mixed-methods exploration of students' motivation in using augmented reality in neuroanatomy education with prosected specimens

The use of augmented reality (AR) in teaching and studying neuroanatomy has been well researched. Previous research showed that AR-based learning of neuroanatomy has both alleviated cognitive load and was attractive to young learners. However, how the attractiveness of AR effects student motivation has not been discovered. Therefore, the motivational effects of AR were investigated in this research by the use of quantitative and qualitative methods. Motivation elicited by the GreyMapp-AR, an AR application, was investigated in medical and biomedical sciences students (n = 222; mean age: 19.7 ± 1.4 years) using the instructional measure of motivation survey (IMMS). Additional components (i.e., attention, relevance, confidence, and satisfaction) were also evaluated with motivation as measured by IMMS. Additionally, 19 students underwent audio-recorded individual interviews which were transcribed for qualitative analysis. Males regarded the relevance of AR significantly higher than females (P < 0.024). Appreciation of the GreyMapp-AR program was found to be significantly higher in students studying biomedical sciences as compared to students studying medicine (P < 0.011). Other components and scores did not show significant differences between student groups. Students expressed that AR was beneficial in increasing their motivation to study subcortical structures, and that AR could be helpful and motivating for preparing an anatomy examination. This study suggests that students are motivated to study neuroanatomy by the use of AR, although the components that make up their individual motivation can differ significantly between groups of students.     

Improving the Bandwidth Selection in Kernel Equating

We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners for being too complex and that it does not offer sufficient smoothing in certain cases. In addition, the bandwidth parameters have been treated as constants in the derivation of the standard error of equating even when they were selected by considering the observed data. Here, the bandwidth selection is simplified, and modified standard errors of equating (SEEs) that reflect the bandwidth selection method are derived. The method is illustrated with real data examples and simulated data.