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Recent work has shown that student trust in their instructor is a key moderator of STEM student buy-in to evidence-based teaching practices (EBTs), enhancing positive student outcomes such as performance, engagement, and persistence. Although trust in instructor has been previously operationalized in related settings, a systematic classification of how undergraduate STEM students perceive trustworthiness in their instructors remains to be developed. Moreover, previous operationalizations impose a structure that often includes distinct domains, such as cognitive and affective trust, that have yet to be empirically tested in the undergraduate STEM context. MethodsTo address this gap, we engage in a multi-step qualitative approach to unify existing definitions of trust from the literature and analyze structured interviews with 57 students enrolled in undergraduate STEM classes who were asked to describe a trusted instructor. Through thematic analysis, we propose that characteristics of a trustworthy instructor can be classified into three domains. We then assess the validity of the three-domain model both qualitatively and quantitatively. First, we examine student responses to determine how traits from different domains are mentioned together. Second, we use a process-model approach to instrument design that leverages our qualitative interview codebook to develop a survey that measures student trust. We performed an exploratory factor analysis on survey responses to quantitatively test the construct validity of our proposed three-domain trust model. Results and discussionWe identified 28 instructor traits that students perceived as trustworthy, categorized into cognitive, affective, and relational domains. Within student responses, we found that there was a high degree of interconnectedness between traits in the cognitive and relational domains. When we assessed the construct validity of the three-factor model using survey responses, we found that a three-factor model did not adequately capture the underlying latent structure. Our findings align with recent calls to both closely examine long-held assumptions of trust dimensionality and to develop context-specific trust measurements. The work presented here can inform the development of a reliable measure of student trust within undergraduate STEM student environments and ultimately improve our understanding of how instructors can best leverage the effectiveness of EBTs for positive student learning outcomes. 

Abstract This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies. 

In response to the outbreak of COVID-19 the national landscape of higher education changed quickly and dramatically to move “online” in the Spring semester of 2020. While distressing to both faculty and students, it presents a unique opportunity to explore how students responded to this unexpected and challenging learning situation. In four undergraduate STEM courses that incorporated course-based undergraduate research experiences (CUREs)—which are often focused on discovery learning and laboratory research—we had an existing study in progress to track students' interest development at five time points over the Spring 2020 semester. Via this ongoing study we were able to investigate how students stay engaged in their college science courses when facing unexpected challenges and obstacles to their learning. Longitudinal survey data from 41 students in these CURE courses demonstrated that students' situational interest dropped significantly when their CURE courses unexpectedly shifted from hands-on, discovery-based, and laboratory-based instruction to online instruction. Although we observed a dramatic decline in student interest in general after the CURE courses moved fully online, the decline rates varied across students. Students who were able to make meaningful connections between the learning activities and their personal or career goals were more likely to maintain a higher level of interest in the course. Implications for practice are discussed. 

Abstract Objective In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Materials and Methods We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. Conclusion By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.