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1. Building Bridges into Engineering and Computer Science: Outcomes, Impacts and Lessons Learned

   Espiritu, Doris ; Todorovic, Ruzica ; O'Connell, Bridget ( January 2022 , 2022 ASEE National Conference and Exposition)

   Wright College, an urban open-access community college, independently accredited within a larger community college system, is a federally recognized Hispanic-Serving Institution (HSI) with the largest community college enrollment of Hispanic students in its state. In 2018, Wright College received an inaugural National Science Foundation-Hispanic Serving Institution (NSF:HSI) research project grant “Building Capacity: Building Bridges into Engineering and Computer Science”. The project's overall goals are to increase underrepresented students pursuing an associate degree (AES) in engineering and computer science and streamline two transitions: high school to community college and 2-year to 4-year institutions. Through the grant, Wright College created a holistic and programmatic framework that examines and correlates engineering students' self-efficacy (the belief that students will succeed as engineers) and a sense of belonging with student success. The project focuses on Near-STEM ready students (students who need up to four semesters of math remediation before moving into Calculus 1). The project assesses qualitative and quantitative outcomes through surveys and case study interviews supplemented with retention, persistence, transfer, associate and bachelor's degree completion rates, and time for degree completion. The key research approach is to correlate student success data with self-efficacy and belonging measures. Outcomes and Impacts Three years into the project, Wright College Engineering and Computer Science Program was able to: • Develop and implement the Contextualized Summer Bridge with a total of 132 Near-STEM participants. One hundred twenty-seven (127) completed; 100% who completed the Bridge eliminated up to two years of math remediation, and 54% were directly placed in Calculus 1. All successful participants were placed in different engineering pathways, and 11 students completed Associate in Engineering Science (AES) and transferred after two years from the Bridge. • Increase enrollment by 940% (25 to 235 students) • Retain 93% of first-year students (Fall to fall retention). Seventy-five percent (75%) transferred after two years from initial enrollment. • Develop a holistic and programmatic approach for transfer model, thus increasing partnerships with 4-year transfer institutions resulting in the expansion of guaranteed/dual admissions programs with scholarships, paid research experience, dual advising, and students transferring as juniors. • Increase diversity at Wright College by bridging the academic gap for Near-STEM ready students. • Increase self-efficacy and belonging among all Program participants. • Increase institutionalized collaborations responsible for Wright College's new designation as the Center of Excellence for Engineering and Computer Science. • Increase enrollment, retention, and transfer of Hispanic students instrumental for Wright College Seal of Excelencia recognition. Lessons Learned The framework established during the first year of the grant overwhelmingly increased belonging and self-efficacy correlated with robust outcomes. However, the COVID-19 pandemic provided new challenges and opportunities in the second and third years of the grant. While adaptations were made to compensate for the negative impact of the pandemic, the face-to-face interactions were critical to support students’ entry into pathways and persistence within the Program. 

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2. A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID ‐19

   https://doi.org/10.1002/nav.21905  

   Mehrotra, Sanjay ; Rahimian, Hamed ; Barah, Masoud ; Luo, Fengqiao ; Schantz, Karolina ( May 2020 , Naval Research Logistics (NRL))

   We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk‐averse condition. The model is applied to study the allocation of ventilator inventory in the COVID‐19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non‐COVID‐19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non‐COVID‐19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top‐most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst‐case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk‐aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp‐up consideration can be based on a cost‐benefit analysis.

    

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3. Who's in the Crowd Matters: Cognitive Factors and Beliefs Predict Misinformation Assessment Accuracy

   https://doi.org/10.1145/3555611  

   Kaufman, Robert A. ; Haupt, Michael Robert ; Dow, Steven P. ( November 2022 , Proceedings of the ACM on Human-Computer Interaction)

   Misinformation runs rampant on social media and has been tied to adverse health behaviors such as vaccine hesitancy. Crowdsourcing can be a means to detect and impede the spread of misinformation online. However, past studies have not deeply examined the individual characteristics - such as cognitive factors and biases - that predict crowdworker accuracy at identifying misinformation. In our study (n = 265), Amazon Mechanical Turk (MTurk) workers and university students assessed the truthfulness and sentiment of COVID-19 related tweets as well as answered several surveys on personal characteristics. Results support the viability of crowdsourcing for assessing misinformation and content stance (i.e., sentiment) related to ongoing and politically-charged topics like the COVID-19 pandemic, however, alignment with experts depends on who is in the crowd. Specifically, we find that respondents with high Cognitive Reflection Test (CRT) scores, conscientiousness, and trust in medical scientists are more aligned with experts while respondents with high Need for Cognitive Closure (NFCC) and those who lean politically conservative are less aligned with experts. We see differences between recruitment platforms as well, as our data shows university students are on average more aligned with experts than MTurk workers, most likely due to overall differences in participant characteristics on each platform. Results offer transparency into how crowd composition affects misinformation and stance assessment and have implications on future crowd recruitment and filtering practices. 

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4. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

   https://doi.org/10.7554/eLife.81916  

   Sherratt, Katharine ; Gruson, Hugo ; Grah, Rok ; Johnson, Helen ; Niehus, Rene ; Prasse, Bastian ; Sandmann, Frank ; Deuschel, Jannik ; Wolffram, Daniel ; Abbott, Sam ; et al ( April 2023 , eLife)

   Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.

   We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance.

   Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models.

   Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks.

   AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

    

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5. WIP: Incremental innovation training as a means for percolating faculty teaching culture change: A first look

   Gao, R ; Hipwell, M.C ; Seets, D. C. ; Polycarpou, A ; Watson, K. ; Srinivasa, A. R. ( July 2022 , ASEE annual conference)

   With funding from a National Science Foundation (NSF) IUSE/PFE: Revolutionizing Engineering and Computer Science Departments (IUSE/PFE: RED) grant, our vision is to focus on faculty development and culture change to reduce the effort and risk experienced by faculty in implementing pedagogical changes and to increase iterative, data-driven changes in teaching. Our project, called Teams for Creating Opportunities for Revolutionizing the Preparation of Students (TCORPS), is an adaptation of the “Additive innovation” model proposed by Arizona State University [1]. The Department of Mechanical Engineering at Texas A&M University has a long legacy of individualistic and---in many cases---a fixed mindset [2] approach to teaching with the expectation of top-down management of change. The goal of our project is to evolve the departmental culture to a bottom-up team structure where the faculty embrace an innovative mindset and extend an iterative build-test-learn method of the maker culture [3] that was formalized by the Lean Startup [4] approach. Faculty already have investigative and experimentation-driven processes in place for research and a keen understanding of data to support their hypotheses. We aim to leverage this preexisting strength and knowledge by extending it to the faculty-led, small-scale, iterative improvement of curriculum and pedagogy 

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