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Course-based Undergraduate Research Experiences (CUREs) have the potential to lower barriers to participation in research for a wide range of undergraduate students. In this study, we examined written reflections of first-generation college students who participated in CUREs to understand the challenges and benefits that these courses offered them.

Undergraduate science, technology, engineering, and mathematics (STEM) students' motivations have a strong influence on whether and how they will persist through challenging coursework and into STEM careers. Proper conceptualization and measurement of motivation constructs, such as students' expectancies and perceptions of value and cost (i.e., expectancy value theory [EVT]) and their goals (i.e., achievement goal theory [AGT]), are necessary to understand and enhance STEM persistence and success. Research findings suggest the importance of exploring multiple measurement models for motivation constructs, including traditional confirmatory factor analysis, exploratory structural equation models (ESEM), and bifactor models, but more research is needed to determine whether the same model fits best across time and context. As such, we measured undergraduate biology students' EVT and AGT motivations and investigated which measurement model best fit the data, and whether measurement invariance held, across three semesters. Having determined the best-fitting measurement model and type of invariance, we used scores from the best performing model to predict biology achievement. Measurement results indicated a bifactor-ESEM model had the best data-model fit for EVT and an ESEM model had the best data-model fit for AGT, with evidence of measurement invariance across semesters. Motivation factors, in particular attainment value and subjective task value, predicted small yet statistically significant amounts of variance in biology course outcomes each semester. Our findings provide support for using modern measurement models to capture students' STEM motivations and potentially refine conceptualizations of them. Such future research will enhance educators' ability to benevolently monitor and support students' motivation, and enhance STEM performance and career success.

The Southern Ocean surrounding Antarctica is a region that is key to a range of climatic and oceanographic processes with worldwide effects, and is characterised by high biological productivity and biodiversity. Since 2013, the International Bathymetric Chart of the Southern Ocean (IBCSO) has represented the most comprehensive compilation of bathymetry for the Southern Ocean south of 60°S. Recently, the IBCSO Project has combined its efforts with the Nippon Foundation - GEBCO Seabed 2030 Project supporting the goal of mapping the world's oceans by 2030. New datasets initiated a second version of IBCSO (IBCSO v2). This version extends to 50°S (covering approximately 2.4 times the area of seafloor of the previous version) including the gateways of the Antarctic Circumpolar Current and the Antarctic circumpolar frontal systems. Due to increased (multibeam) data coverage, IBCSO v2 significantly improves the overall representation of the Southern Ocean seafloor and resolves many submarine landforms in more detail. This makes IBCSO v2 the most authoritative seafloor map of the area south of 50°S.

Even highly motivated undergraduates drift off their STEM career pathways. In large introductory STEM classes, instructors struggle to identify and support these students. To address these issues, we developed co‐redesign methods in partnership with disciplinary experts to create high‐structure STEM courses that better support students and produce informative digital event data. To those data, we applied theory‐ and context‐relevant labels to reflect active and self‐regulated learning processes involving LMS‐hosted course materials, formative assessments, and help‐seeking tools. We illustrate the predictive benefits of this process across two cycles of model creation and reapplication. In cycle 1, we used theory‐relevant features from 3 weeks of data to inform a prediction model that accurately identified struggling students and sustained its accuracy when reapplied in future semesters. In cycle 2, we refit a model with temporally contextualized features that achieved superior accuracy using data from just two class meetings. This modelling approach can produce durable learning analytics solutions that afford scaled and sustained prediction and intervention opportunities that involve explainable artificial intelligence products. Those same products that inform prediction can also guide intervention approaches and inform future instructional design and delivery.Practitioner notesWhat is already known about this topicLearning analytics includes an evolving collection of methods for tracing and understanding student learning through their engagements with learning technologies.Prediction models based on demographic data can perpetuate systemic biases.Prediction models based on behavioural event data can produce accurate predictions of academic success, and validation efforts can enrich those data to reflect students' self‐regulated learning processes within learning tasks.What this paper addsLearning analytics can be successfully applied to predict performance in an authentic postsecondary STEM context, and the use of context and theory as guides for feature engineering can ensure sustained predictive accuracy upon reapplication.The consistent types of learning resources and cyclical nature of their provisioning from lesson to lesson are hallmarks of high‐structure active learning designs that are known to benefit learners. These designs also provide opportunities for observing and modelling contextually grounded, theory‐aligned and temporally positioned learning events that informed prediction models that accurately classified students upon initial and later reapplications in subsequent semesters.Co‐design relationships where researchers and instructors work together toward pedagogical implementation and course instrumentation are essential to developing unique insights for feature engineering and producing explainable artificial intelligence approaches to predictive modelling.Implications for practice and/or policyHigh‐structure course designs can scaffold student engagement with course materials to make learning more effective and products of feature engineering more explainable.Learning analytics initiatives can avoid perpetuation of systemic biases when methods prioritize theory‐informed behavioural data that reflect learning processes, sensitivity to instructional context and development of explainable predictors of success rather than relying on students' demographic characteristics as predictors.Prioritizing behaviours as predictors improves explainability in ways that can inform the redesign of courses and design of learning supports, which further informs the refinement of learning theories and their applications. [ABSTRACT FROM AUTHOR]

With the growth and increased variation of faculty roles, research-intensive campuses have varied expectations with respect to teaching, service, and research. In this piece the authors introduce a categorization of the various types of teaching practice, differentiating them from knowledge generation in the disciplines. The authors then draw distinctions among the duties for different types of faculty positions and call for concrete expectations among these positions and duties. The authors argue that all faculty at institutions of higher education should be engaged in some form of scholarly teaching, and some faculty should be hired into knowledge-generating positions of discipline-based education research (DBER) and/or scholarship of teaching and learning (SOTL), to conduct research on the nature of learning and teaching in the disciplines--providing basic research and development for scholarly teaching. Ultimately, the authors strive to provide language to support discussions around roles of teaching and scholarly work in education. Although the focus is on research-intensive institutions, this discussion is worth having across the array of institutions of higher education.

Pregnant women are uniquely susceptible to adverse effects of air pollution exposure due to vulnerabilities and health consequences during pregnancy (e.g., hypertensive disorders of pregnancy [HDP]) compared to the general population. Because the Clean Air Act (CAA) creates a duty to protect at-risk groups, the regulatory assessment of at-risk populations has both policy and scientific foundations. Previously, pregnant women have not been specially protected in establishing the margin of safety for the ozone and particulate matter (PM) standards. Due to physiological changes, pregnant women can be at greater risk of adverse effects of air pollution and should be considered an at-risk population. Women with preexisting conditions, women experiencing poverty, and groups that suffer systematic discrimination may be particularly susceptible to cardiac effects of air pollutants during pregnancy. We rigorously reviewed 11 studies of over 1.3 million pregnant women in the United States to characterize the relationship between ozone or PM exposure and HDP. Findings were generally mixed, with a few studies reporting a joint association between ozone or PM and social determinants or pre-existing chronic health conditions related to HDP. Adequate evidence associates exposure to PM with an adverse effect of HDP among pregnant women not evident among non-gravid populations.

At the college level, the effectiveness of active-learning interventions is typically measured at the broadest scales: the achievement or retention of all students in a course. Coarse-grained measures like these cannot inform instructors about an intervention's relative effectiveness for the different student populations in their classrooms or about the proximate factors responsible for the observed changes in student achievement. In this study, we disaggregate student data by racial/ethnic groups and first-generation status to identify whether a particular intervention--increased course structure--works better for particular populations of students. We also explore possible factors that may mediate the observed changes in student achievement. We found that a "moderate-structure" intervention increased course performance for all student populations, but worked disproportionately well for black students--halving the black-white achievement gap--and first-generation students--closing the achievement gap with continuing-generation students. We also found that students consistently reported completing the assigned readings more frequently, spending more time studying for class, and feeling an increased sense of community in the moderate-structure course. These changes imply that increased course structure improves student achievement at least partially through increasing student use of distributed learning and creating a more interdependent classroom community.