Intelligent tutoring systems are known for providing customized learning opportunities for thousands of users. One feature of many systems is differentiating the amount of practice users receive. To do this, some systems rely on a threshold of consecutive correct responses. For instance, Khan Academy used to use ten correct in a row and now uses five correct in a row as the mastery threshold. The present research uses a series of randomized control trials, conducted in an online learning platform (eg., ASSISTments.org), to explore the effects of different thresholds of consecutive correct responses on learning. Results indicate that despite spending significantly more time practicing there is no significant difference on learning between two, three, four, or five consecutive correct responses. This suggests that systems, and MOOCS, can employ the simple rule of two or three consecutive correct responses when determining the amount of practice provided to users.

Studying 5.6 million biomedical science articles published over three decades, we reconcile conflicts in a longstanding interdisciplinary literature on scientists' life-cycle productivity by controlling for selective attrition and distinguishing between research quantity and quality. While research quality declines monotonically over the career, this decline is easily overlooked because higher "ability" authors have longer publishing careers. Our results have implications for broader questions of human capital accumulation over the career and federal research policies that shift funding to early-career researchers - while funding researchers at their most creative, these policies must be undertaken carefully because young researchers are less "able" on average.

Mental models—internal, dynamic, incomplete representations of the external world that people use to guide cognitive processes such as reasoning, decision making, and language comprehension—have practical implications for predicting attitudes and behaviors across various domains. This study examines how mental models of the human–nature relationship predict pro-environmental behavioral intentions directly and indirectly as mediated through anthropocentric and biocentric environmental attitudes. To address these aims, participants were asked about mental model components of the human–nature relationship (human exceptionalism, beliefs about human impact on nature, and beliefs about nature's impact on humans), pro-environmental attitudes (biocentric and anthropocentric), and their pro-environmental behavioral intentions (protection and investment). We found that protection intentions were (1) directly predicted by human exceptionalism beliefs (negatively) and perceived human impact on nature (positively) and (2) indirectly predicted by mental model components via biocentric attitudes. Investment intentions were directly predicted by nature's perceived impact on humans, and were similarly indirectly predicted by mental model components via biocentric attitudes. The results suggest that mental models of the human–nature relationship provide a cognitive foundation for environmental behavioral intentions both directly and through their association with environmental attitudes. These findings have implications for pro-environmental interventions that deal with conceptual and attitudinal change.

Dilated cardiomyopathy (DCM) is the third most common cause of heart failure and the primary reason for heart transplantation; upward of 70% of DCM cases are considered idiopathic. Our in-vitro experiments showed that reduced hybrid/complex N-glycosylation in mouse cardiomyocytes is linked with DCM. Further, we observed direct effects of reduced N-glycosylation on K v gating. However, it is difficult to rigorously determine the effects of glycosylation on K v activity, because there are multiple K v isoforms in cardiomyocytes contributing to the cardiac excitation. Due to complex functions of K v isoforms, only the sum of K + currents (I Ksum ) can be recorded experimentally and decomposed later using exponential fitting to estimate component currents, such as I Kto , I Kslow , and I Kss . However, such estimation cannot adequately describe glycosylation effects and K v mechanisms. Here, we propose a framework of simulation modeling of K v kinetics in mouse ventricular myocytes and model calibration using the in-vitro data under normal and reduced glycosylation conditions through ablation of the Mgat1 gene (i.e., Mgat1KO). Calibrated models facilitate the prediction of K v characteristics at different voltages that are not directly observed in the in-vitro experiments. A model calibration procedure is developed based on the genetic algorithm. Experimental results show that, in the Mgat1KO group, both I Kto and I Kslow densities are shown to be significantly reduced and the rate of I Kslow inactivation is much slower. The proposed approach has strong potential to couple simulation models with experimental data for gaining a better understanding of glycosylation effects on K v kinetics.

We draw on a recombinant view of innovation, where being in a new location and/or multiple locations leads to exposure to novel combinations of ideas that increase the creativity of top scientists.Using a rich, unique dataset we helped assemble, we estimate the empirical relationship between being in a new location and/or multiple locations and the expected interval before an eventual Nobel laureate (ENL) commences their prize-winning work.We find that being in a new location and in multiple locations are substantially and significantly associated with a shorter expected interval before ENLs commence their prize-winning work.

has worked in P-20 education for over 15 years as an instructor, coordinator, and director at the local, state, and national levels, with focus on underserved and low-SES student populations.Throughout his time in education, Ryan has been an advocate and implementor of equitable instructional practices that incorporate culturally responsive and student focused learning, including socio-emotional learning, project-based learning, and stakeholder involvement in curriculum and programing.Ryan's research interests include the implementation and effectiveness of student-centered

The pandemic of coronavirus disease 2019 (COVID-19) has severely affected the higher education system since Spring 2020. During the periods of school closures and limited in-person activities, engineering students and faculty experienced challenges in remote learning and mentoring activities. Funded by the National Science Foundation (NSF), this study surveyed 668 faculty and 3,385 undergraduate/graduate students in engineering from 94 institutions in 36 states across the country. Our findings indicated that several faculty and student subgroups were disproportionately negatively affected by the ongoing COVID-19 pandemic. Specifically, women engineering faculty and students encountered more challenges in transitioning to remote teaching and learning. Students with disabilities and those whose households experienced a loss of income during the COVID-19 pandemic faced more remote learning challenges, perceived less instrumental mentoring support, and were more likely to delay their graduate dates. Our structural equation modeling results showed that mentoring support could mitigate remote learning challenges and academic disruption for engineering students. The study underscores the importance of mentoring support during the crisis of a pandemic.

Studying 5.6 million biomedical science articles published over three decades, we reconcile conflicts in a long-standing interdisciplinary literature on scientists' life-cycle productivity by controlling for selective attrition and distinguishing between research quantity and quality. While research quality declines monotonically over the career, this decline is easily overlooked because higher ability authors have longer publishing careers. Our results have implications for broader questions of human capital accumulation over the career and federal research policies that shift funding to early-career researchers-while funding researchers at their most creative, these policies must be undertaken carefully because young researchers are less able on average.

Using unique, new, matched UMETRICS data on people employed on research projects and Author-ity data on biomedical publications, this paper shows that National Institutes of Health funding stimulates research by supporting the teams that conduct it. While faculty-both principal investigators (PIs) and other faculty-and their productivity are heavily affected by funding, so are trainees and staff. The largest effects of funding on research output are ripple effects on publications that do not include PIs. While funders focus on research output from projects, they would be well advised to consider how funding ripples through the wide range of people, including trainees and staff, employed on projects.

We study the effects of peer gender composition in STEM doctoral programs on persistence and degree completion. Leveraging unique new data and quasi-random variation in gender composition across cohorts within programs, we show that women entering cohorts with no female peers are 11.7 percentage points less likely to graduate within 6 years than their male counterparts. A 1 standard deviation increase in the percentage of female students differentially increases women’s probability of on-time graduation by 4.4 percentage points. These gender peer effects function primarily through changes in the probability of dropping out in a PhD program’s first year. © 2022 The University of Chicago. All rights reserved.