Background
Many engineering education researchers acknowledge that their positionality impacts their research. Practices for reporting positionality vary widely and rarely incorporate a nuanced discussion of the impact of demographic identities on research. Researchers holding marginalized or relatively hidden identities must navigate additional layers regarding transparency of their positionality.
Purpose
We identify ways in which positionality impacts research, with a particular emphasis on demographic identity dimensions. We note that whether identities are relatively marginalized, privileged, hidden, or apparent in a research context creates complexities for conceptualizing, practicing, and disclosing one's positionality.
Method
In a collaborative inquiry informed by autoethnography, we assemble positionality reflections of current engineering education researchers to demonstrate the primary ways in which positionality impacts research.
Results
We find that positionality impacts six fundamental aspects of research: research topic, epistemology, ontology, methodology, relation to participants, and communication. These aspects of research delve deeper than conceptions of positionality as a methodological limitation, a measure to prevent bias, or a requirement for research quality.
Conclusion
The impact of positionality on research is complex, particularly when researchers occupy minoritized identities and for research topics that interrogate power relations between identity groups. By demonstrating the practices of interrogating and representing positionality, we hope to encourage more researchers to represent positionality transparently, thus making researchers' transparency safer for all. We argue that positionality is an important tool for reflecting on and dislocating privilege, particularly when working on equity research.
Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedure to compare different experiments is no longer necessary.
Engineering programs, professional associations, and industry stakeholders emphasize the importance of preparing graduates for an increasingly global, rapidly changing environment. Although there has been increased attention to prepare undergraduates for a global engineering profession, there are challenges associated with measuring how cultural programs and experiences contribute to positive changes in students' abilities to work and thrive in diverse environments. Global competency can be defined broadly as "having an open mind while actively seeking to understand cultural norms and expectations of others, leveraging this gained knowledge to interact, communicate and work effectively outside one's environment" 1 . Measuring global competency levels before and after participation in cultural programs may therefore be a potentially effective method for measuring changes in students' ability to work in a global environment. Currently, studies on engineering students' baseline global competency levels are few at the undergraduate level. This research fills this gap, proposing a conceptual model of the factors that influence global competency levels, and also identifies the baseline levels of global competency for benchmarking. The resulting conceptual model and global competency measures will be useful toward larger scale inquiries to evaluate how participation in study abroad programs, international experiences, culturally-relevant curricula, and other related activities can contribute to changes in students' ability to work in diverse environments.The Miville-Guzman Universality-Diversity Scale short form (MGUDS-S) measures the "universe-diverse orientation" construct, which "reflects an attitude of awareness of both the similarities and differences that exist among people" 2 . Higher MGUDS-S scores have been associated with a relative positive attitude toward others and the "simultaneous appreciation of both the similarities and differences that exist between oneself and others." Therefore, MGUDS-S is used here as a proxy for students' global competency levels in our conceptual model. Based on ordinary least squares regression models using data from 1,461 engineering freshmen, significant differences between MGUDS-S scores were identified. Female students scored higher than their male counterparts, while international students scored higher than domestic students. Among domestic engineering students, gender and ethnicity are associated with differences in MGUDS-S scores. These findings are consistent with results from previous studies, and suggest that women and underrepresented minority students, as well as international students, may receive higher scores since they may be more likely to interact with others from different backgrounds. These findings contribute to a burgeoning line of scientific inquiry lending support to programs that promote student travel abroad experiences and increased interactions between diverse groups of students. This research also has broad implications for providing informa...
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