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The present study analyzes Imposter Phenomenon (IP) through the lens of three different motivational frameworks. Expectancy Value Theory, Attribution Theory, and Self-Determination Theory were used to study IP among academics. With 72% of participants experiencing frequent or intense IP levels, IP was prevalent among those sampled. Females experienced higher IP than males, although race and first-generation status did not significantly impact IP levels. Post docs had higher IP scores than tenured faculty and full-time non-tenured faculty had higher IP scores than tenured faculty. Younger academics had higher IP scores. Analyses of the motivational frameworks showed significant differences by IP level.

This study examines, using structural equation modeling, the mediating effects of ethnic identity and intercultural competence on historically marginalized identities (HMIs) and cognitive empathy. Participants received an Identity Scale score summing each identity marker for groups that have experienced systemic, historic marginalization (i.e., domestic/international student status, race/ethnicity, gender identity, income, and sexual orientation), with an individual being able to attain a score ranging from 0 (no HMI) to 5 (all HMIs present). Information about participants' ethnic identity, cognitive empathy, and intercultural competence was also collected. The sample included a diverse, representative sample of 623 participants from three higher education institutions in the United States. Our tested model showed significant direct and indirect effects among all the variables with the exception of one relationship. The paper discusses the model results and the implications of our findings for inclusive education.

The widely used Draw a Scientist Test was revised to focus on statistics and 110 Elementary Statistics students were asked to draw a statistician. In addition, to better understand students' drawings and have some relative comparison, 173 College Algebra students were asked to draw a mathematician. A detailed analysis of students' images and students' demographic information was conducted using descriptive statistics, categorical data analysis, logistic regressions, and hierarchical cluster analysis. Results showed that students tend to perceive statisticians and mathematicians as primarily White and male. However, female students were more likely than male students to draw a female statistician and mathematician. Two themed clusters emerged from the hierarchical cluster analysis for both the math and statistics students. We discuss the implications of the results for teaching and future research.

Conceptual change can be a challenging process, particularly in science education where many of the concepts are complex, controversial, or counter-intuitive. Yet, conceptual change is fundamental to science learning, which suggests science educators and science education researchers need models to effectively address and investigate conceptual change. Consideration of the current research and extant models of conceptual change reflect a need for a holistic, comprehensive, and dynamic model of conceptual change. In response, we developed the Dynamic Model of Conceptual Change (DMCC), which uses multiple lines of research that explore the variables influencing conceptual change and the dynamic interactions that take place during the conceptual change process in science teaching and learning. Unique to the DMCC is the potential for iterations, regression, enter and exit points at various stages of the conceptual change process, and the influences of message recognition, message engagement and processing, and the nature of the resulting conceptual change. The DMCC contains elements from extant models along with previously un-emphasized influential conceptual change variables such as culture, society, attitude, practices, and personal epistemology. We constructed the DMCC to provide science educators and researchers a more holistic framework for exploring conceptual change in science instruction and learning.

This study examined the relationship among cognitive and motivational variables impacting college students' willingness to take mitigative action to reduce the impacts of human-induced climate change. One hundred and forty college students were asked to read a persuasive text about human-induced climate change and were pre- and post-tested on their attitudes about climate change and their willingness to take action to mitigate its effects. Students showed statistically significant changes in their attitudes about climate change and their willingness to commit to take action. A path model demonstrated that openness to change and a willingness to think deeply about issues predicted both change in attitudes and expressed willingness to take action. This research demonstrates that a persuasive text has the potential to promote change around complex socio-scientific issues. (Contains 2 figures and 3 tables.)

The purpose of this paper is to propose a new approach to research on gender differences in science that uses the work on expertise in science as a framework for understanding gender differences. Because gender differences in achievement and participation in the sciences are largest in physics, the focus of this review is on physics. The nature of expertise is first discussed and a framework that focuses on factors that influence the emergence of expertise in physics is presented. This is used to interpret what is known about gender differences in science, particularly physics. Next, the potential contributions of the research on gender differences to our understanding of expertise are discussed. Using what is learned from these two areas of research, recommendations are made for future research examining gender differences in physics. It is suggested that such an approach be used for other areas of science, such as chemistry, where large gender differences in achievement and participation also exist.

As an example of design-based research, this study refined an assessment strategy for simultaneously enhancing inquiry-based learning and supporting achievement on conventional assessment measures. "Astronomy Village[R]: Investigating the Universe[TM]" is a software program designed to engage secondary science students in authentic and inquiry-based learning over core topics in astronomy. The software was enhanced with a 20-hour curriculum and three levels of assessment to ensure successful inquiry experiences "and" high-stakes achievement. The first year implementation of Astronomy Village[R] yielded significant gains on a curriculum-oriented exam but not a standards-oriented test, and provided useful design insights that were integrated into the second year implementations. Significant gains were obtained on the test during the second year as well. It is expected that many existing inquiry-oriented science curricula might be similarly enhanced, and is suggested that a large-scale effort to do so might have a lasting impact on science education.

This study uses an existing astronomy software program to encourage inquiry-based learning and argumentation while also ensuring gains on conventional classroom assessments and high-stakes achievement tests. To supplement the software program, a curriculum and three levels of assessment--quizzes, exam, and test--were designed and administered to 11th- and 12th-grade students during a four-week implementation. Students worked in groups for the quizzes and exam to engage in argumentative discourse. Because of the difficulties in engaging students in quality argumentation, the curriculum, assessments, and overall learning environment were examined for ways to further support argumentative discourse. (Contains 3 tables, 3 figures, and 1 note.)

The SRMR, RMSEA, CFI, and TLI are commonly used fit indexes reported when describing the fit of structural equation models (SEM) used in math and science education. A large number of the models tested in math and science education tend to be path models that study the interaction between various motivational, affective, contextual, and cognitive variables or latent growth curve models that examine change in students over time. The majority of these models tend to have small degrees of freedom and small sample sizes. Given the common use of these fit indexes, it is important to understand their performance when reported for relatively simple models. [ABSTRACT FROM AUTHOR]