Journal of Experimental Education最新文献

Somatic hypermutation generates a myriad of Ab mutants in Ag-specific B cells, from which high-affinity mutants are selected. Chickens, sheep, and rabbits use nontemplated point mutations and templated mutations via gene conversion to diversify their expressed Ig loci, whereas mice and humans rely solely on untemplated somatic point mutations. In this study, we demonstrate that, in addition to untemplated point mutations, templated mutagenesis readily occurs at the murine and human Ig loci. We provide two distinct lines of evidence that are not explained by the Neuberger model of somatic hypermutation: 1) across multiple data sets there is significant linkage disequilibrium between individual mutations, especially among close mutations, and 2) among those mutations, those <8 bp apart are significantly more likely to match microhomologous regions in the IgHV repertoire than predicted by the mutation profiles of somatic hypermutation. Together, this supports the role of templated mutagenesis during somatic diversification of Ag-activated B cells.

Personal relevance goes by many names in the motivation literature, stemming from a number of theoretical frameworks. Currently these lines of research are being conducted in parallel with little synthesis across them, perhaps because there is no unifying definition of the relevance construct within which this research can be situated. In this paper we propose a new framework to synthesize existing research on relevance and provide a common platform for researchers to communicate and collaborate. In light of this new framework we review the role of relevance in three prominent theories in the motivation literature: the four-phase model of interest development (Hidi & Renninger, 2006), expectancy-value theory (Eccles et al., 1983), and self-determination theory (Deci & Ryan, 1985). We then explore eight relevance constructs commonly used in the literature and the educational interventions that derive from them. Finally, we offer a synthesis of these constructs and suggest some directions for future research.

Mentoring, particularly same-gender and same-race mentoring, is increasingly seen as a powerful method to attract and retain more women and racial minorities into science, technology, engineering, and mathematics (STEM) education and careers. This study examines elements of a mentoring dyad relationship (i.e., demographic and perceived similarity) that influence the quality of mentorship, as well as the effect of mentorship on STEM career commitment. A national sample of African American undergraduates majoring in STEM disciplines were surveyed in their senior year. Overall, perceived similarity, rather than demographic similarity, was the most important factor associated with protégé perceptions of high quality mentorship and high quality mentoring was in turn associated with higher commitment to STEM careers. We discuss the implications for mentoring underrepresented students and broadening participation in STEM.

Using data from the Educational Longitudinal Study of 2002-2006 (ELS:02/06), this study investigated the effects of advanced math course taking on math achievement and college enrollment and how such effects varied by socioeconomic status (SES) and race/ethnicity. Results from propensity score matching and sensitivity analyses showed that advanced math course taking had positive effects on math achievement and college enrollment. Results also demonstrated that the effect of advanced math course taking on math achievement was greater for low SES students than for high SES students, but smaller for Black students than for White students. No interaction effects were found for college enrollment. Limitations, policy implications, and future research directions are discussed.

Increasing the correlation between the independent variable and the mediator (a coefficient) increases the effect size (ab) for mediation analysis; however, increasing a by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation due to increases in a at some point outweighs the increase of the effect size (ab) and results in a loss of statistical power. This phenomenon also occurs with nonparametric bootstrapping approaches because the variance of the bootstrap distribution of ab approximates the variance expected from normal theory. Both variances increase dramatically when a exceeds the b coefficient, thus explaining the power decline with increases in a. Implications for statistical analysis and applied researchers are discussed.

Evaluating the structural quality of arguments is a skill important to students' ability to comprehend the arguments of others and produce their own. The authors examined college and high school students' ability to evaluate the quality of 2-clause (claim-reason) arguments and tested a tutorial to improve this ability. These experiments indicated that college and high school students had difficulty evaluating arguments on the basis of their quality. Experiments 1 and 2 showed that a tutorial explaining skills important to overall argument evaluation increased performance but that immediate feedback during training was necessary for teaching students to evaluate the claim-reason connection. Using a Web-based version of the tutorial, Experiment 3 extended this finding to the performance of high-school students. The study suggests that teaching the structure of an argument and teaching students to pay attention to the precise message of the claim can improve argument evaluation.

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3 variance-covariance structures. The results from a full-crossed simulation design suggest that traditional repeated measures have significantly higher power than do hierarchical multivariate linear models for main effects, but they have significantly lower power for interaction effects in most situations. Significant power differences are also exhibited when power is compared across different covariance structures.