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The authors present the design and implementation of an exploratory virtual learning environment that assists children with autism (ASD) in learning science, technology, engineering, and mathematics (STEM) skills along with improving social-emotional and communication skills. The primary contribution of this exploratory research is how educational research informs technological advances in triggering a virtual AI companion (AIC) for children in need of social-emotional and communication skills development. The AIC adapts to students’ varying levels of needed support. This project began by using puppetry control (human-in-the-loop) of the AIC, assisting students with ASD in learning basic coding, practicing their social skills with the AIC, and attaining emotional recognition and regulation skills for effective communication and learning. The student is given the challenge to program a robot, Dash™, to move in a square. Based on observed behaviors, the puppeteer controls the virtual agent’s actions to support the student in coding the robot. The virtual agent’s actions that inform the development of the AIC include speech, facial expressions, gestures, respiration, and heart color changes coded to indicate emotional state. The paper provides exploratory findings of the first 2 years of this 5-year scaling-up research study. The outcomes discussed align with a common approach of research design used for students with disabilities, called single case study research. This type of design does not involve random control trial research; instead, the student acts as her or his own control subject. Students with ASD have substantial individual differences in their social skill deficits, behaviors, communications, and learning needs, which vary greatly from the norm and from other individuals identified with this disability. Therefore, findings are reported as changes within subjects instead of across subjects. While these exploratory observations serve as a basis for longer term research on a larger population, this paper focuses less on student learning and more on evolving technology in AIC and supporting students with ASD in STEM environments. 

Antifungal resistance in pathogenic fungi is a growing global health concern. Nonpathogenic laboratory strains of Saccharomyces cerevisiae are an important model for studying mechanisms of antifungal resistance that are relevant to understanding the same processes in pathogenic fungi. We have developed a series of laboratory modules in which high school students used experimental evolution to study antifungal resistance by isolating azole-resistant S. cerevisiae mutants and examining the genetic basis of resistance. We have sequenced 99 clones from these experiments and found that all possessed mutations previously shown to impact azole resistance, validating our approach. We additionally found recurrent mutations in an mRNA degradation pathway and an uncharacterized mitochondrial protein (Csf1) that have possible mechanistic connections to azole resistance. The scale of replication in this initiative allowed us to identify candidate epistatic interactions, as evidenced by pairs of mutations that occur in the same clone more frequently than expected by chance (positive epistasis) or less frequently (negative epistasis). We validated one of these pairs, a negative epistatic interaction between gain-of-function mutations in the multidrug resistance transcription factors Pdr1 and Pdr3. This high school–university collaboration can serve as a model for involving members of the broader public in the scientific process to make meaningful discoveries in biomedical research.

 

Daedalus MASE (Mission Assessment through Simulation Exercise) is an open-source package of scientific analysis tools aimed at research in the Lower Thermosphere-Ionosphere (LTI). It was created with the purpose to assess the performance and demonstrate closure of the mission objectives of Daedalus, a mission concept targeting to performin-situmeasurements in the LTI. However, through its successful usage as a mission-simulator toolset, Daedalus MASE has evolved to encompass numerous capabilities related to LTI science and modeling. Inputs are geophysical observables in the LTI, which can be obtained either throughin-situmeasurements from spacecraft and rockets, or through Global Circulation Models (GCM). These include ion, neutral and electron densities, ion and neutral composition, ion, electron and neutral temperatures, ion drifts, neutral winds, electric field, and magnetic field. In the examples presented, these geophysical observables are obtained through NCAR’s Thermosphere-Ionosphere-Electrodynamics General Circulation Model. Capabilities of Daedalus MASE include: 1) Calculations of products that are derived from the above geophysical observables, such as Joule heating, energy transfer rates between species, electrical currents, electrical conductivity, ion-neutral collision frequencies between all combinations of species, as well as height-integrations of derived products. 2) Calculation and cross-comparison of collision frequencies and estimates of the effect of using different models of collision frequencies into derived products. 3) Calculation of the uncertainties of derived products based on the uncertainties of the geophysical observables, due to instrument errors or to uncertainties in measurement techniques. 4) Routines for the along-orbit interpolation within gridded datasets of GCMs. 5) Routines for the calculation of the global coverage of anin situmission in regions of interest and for various conditions of solar and geomagnetic activity. 6) Calculations of the statistical significance of obtaining the primary and derived products throughout anin situmission’s lifetime. 7) Routines for the visualization of 3D datasets of GCMs and of measurements along orbit. Daedalus MASE code is accompanied by a set of Jupyter Notebooks, incorporating all required theory, references, codes and plotting in a user-friendly environment. Daedalus MASE is developed and maintained at the Department for Electrical and Computer Engineering of the Democritus University of Thrace, with key contributions from several partner institutions.

 

STEM graduation rates, cumulative GPAs, and final GPA distributions of years 2016 to 2019 graduates were evaluated for students who participated in Rice University’s STEM intervention (the Rice Emerging Scholars Program, or RESP, which is partly funded through an NSF SSTEM grant), which begins with a pre-freshman STEM summer bridge program. RESP participants (n=89) and a comparison category of students (n=81) were identified as being underprepared for STEM coursework. Outcomes from the rest of the graduating classes were also assessed (i.e., non-comparison, non-RESP students). Incoming high school AP and IB credits were a moderate predictor of cumulative graduation GPA. After controlling for test credits, student status predicted cumulative graduation GPA, with higher GPAs in the noncomparison, non-RESP condition. Seventy-two RESP students graduated with a STEM major (81% STEM retention) compared with 62% of comparison students and 87% of non-comparison, non-RESP students. A chi-square test found a significant difference in favor of higher STEM retention among RESP students than the comparison students. Of RESP STEM graduates, 94% graduated with at least a B- GPA, compared with 86% of the comparison students, and 97% of the non-comparison, non-RESP students. A chi-square test approached significance in favor of more B- and above GPAs among RESP students than the comparison students. Overall, we found that high school preparation predicted STEM students’ graduation GPAs. Further, although RESP participation did not predict the cumulative GPAs of STEM majors, the program may: 1) improve STEM degree persistence and 2) ensure that more of the program’s STEM graduates achieve at least a B- cumulative graduation GPA. The number of RESP and comparison students is relatively small, yet these findings nevertheless offer preliminary evidence that the intervention may be effective at improving STEM outcomes for students who would otherwise struggle the most with their coursework. As more students graduate from the university, we will be able to make stronger conclusions about the effectiveness of RESP in improving outcomes of underprepared STEM students. 

Anecdotal evidence suggests that the timing and intensity of the Central American Midsummer Drought (MSD) may be changing, while observations from limited meteorological station data and paleoclimate reconstructions show neither significant nor consistent trends in seasonal rainfall. Climate model simulations project robust future drying across the region, but internal variability is expected to dominate until the end of the century. Here we use a high-resolution gridded precipitation dataset to investigate these apparent discrepancies and to quantify the spatiotemporal complexities of the MSD. We detect spatially variable trends in MSD timing, the amount of rainy season precipitation, the number of consecutive and total dry days, and extreme wet events at the local scale. At the regional scale, we find a positive trend in the duration, but not the magnitude of the MSD, which is dominated by spatially heterogeneous trends and interannual variability linked to large-scale modes of ocean-atmosphere circulation. Although the current climate still reflects predominantly internal variability, some Central American communities are already experiencing significant changes in local characteristics of the MSD. A detailed spatiotemporal understanding of MSD trends and variability can contribute to evidence-based adaptation planning and help reduce the vulnerability of Central American communities to both natural rainfall variability and anthropogenic change.

 

To combat math underperformance among incoming STEM majors, Rice University designed a summer bridge program with National Science Foundation (NSF) S-STEM funding that included an intensive calculus course. Students invited to participate in the program were identified as being underprepared for STEM classes based on their standardized test scores, high school STEM coursework, and socioeconomic status. One of the program’s goals is to improve students’ preparation for the advanced math courses required for all STEM majors at Rice. The bridge program is designed to teach the material that has historically been most challenging for underprepared students, meaning the math content covered primarily second-semester calculus topics. We explored the impact of bridge program participation on math performance in first and second-semester math. First, we examined group differences in math preparation. Though program administrators attempt to create equivalent bridge and comparison groups, the bridge program is optional, meaning group assignment is not completely random. Bridge students were less prepared than comparison students on number of high school calculus AP (or equivalent) credits received. We analyzed group differences in final class grades from 2012-2017 among the comparison group, the bridge group, and the rest of the class (i.e. non-comparison and nonbridge), standardizing grades using Z-scores. Planned contrasts found that bridge students performed slightly better than, but not significantly different from, comparison students in first semester math. Conversely, planned contrasts found that the bridge group significantly outperformed the comparison group in second-semester math. These results suggest that bridge program exposure to calculus may improve performance relative to a comparison group, which is especially noteworthy because bridge students are the least math-prepared STEM students entering the university. Future research will analyze outcomes in more advanced math classes. We will use these findings to refine the bridge program’s approach to teaching students how to succeed at collegiate-level math classes and, ultimately, as STEM majors at Rice.