ABSTRACT
It has become increasingly important for K-12 students to learn how to investigate patterns, correlations, and significance in data. The Berkeley Engineering Research Experiences for Teachers plus Data (BERET+D) pairs undergraduate pre-service teachers and experienced in-service science and mathematics teachers (PSTs and ISTs) to engage in engineering and data science research, exploring and analyzing data sets drawn from a variety of STEM fields and laboratories across the UC Berkeley campus. In addition to conducting independent summer research projects with guidance from university research faculty, the program provides opportunities for: (1) PSTs to develop data science-based lessons inspired by their research and aligned to the Next Generation Science Standards (NGSS), (2) ISTs to create data science-based curricula designed to inspire middle and high school students to see STEM classes as exciting and with real-life applications, and (3) ISTs to collaborate with and mentor PSTs preparing to enter K-12 STEM classrooms. Contributing towards broader impacts, CalTeach recruits a racially and socioeconomically diverse population of PSTs, and all ISTs were recruited from local public schools, in order to educate, prepare, and encourage more minority and female K-12 students to consider higher education and careers in STEM. During the first two summers of this project (2020-2021), participants completed over forty data-science related projects, developed over thirty K-12 data-science related lesson plans in math, science, and engineering, and created six classroom-ready and publicly accessible (teachengineering.org) curricular units showcasing data science. As an example of these curricular units, and as further evidence of the project's broader impact, one IST has developed an ongoing partnership between their classroom and a research laboratory on campus allowing high school physics students to learn data science techniques by analyzing and interpreting distant satellite signals collected by radio telescopes. Preliminary evaluation of this ongoing project revealed that participants viewed data science as important and essential in K-12 curriculum, that data analysis is a critical and useful skill for youth, and that data science aligns closely with the science and engineering practices called forth by NGSS. Though constrained by work-from-home restrictions due to COVID during the first two years, participants described their experience as positive and valuable, particularly in conceiving of ways to engage young learners with data-science through remote instruction. © American Society for Engineering Education, 2022
ABSTRACT
This paper proposes to find how some specific constraints affect portfolio choice under two dynamic situations which are only considering risk and considering both risk and return respectively. Ten individual stocks from different industries and SPX 500 from 2001 to 2021 have been selected as the data. For example, the COVID-19 has brought a great negative impact on the aviation industry. By analyzing the data, I have found that the stock market has a strong resilience and ability to deal with external shocks. I have also chosen two models, namely Markowitz Model and Index Model under five constraints to examine the portfolio performance. These results indicate that some constraints have a negative impact on portfolio choice. For example, the portfolio under the benchmark has meant having no constraints being ones with the minimum risk. In other words, too many constraints make the risk higher. Additionally, the portfolio has its maximum return and risk if the broad index isn't in the portfolio. This means broad index can control the risk of the portfolio. From the results of the paper and the research direction, this paper has applications for stock investors during shocks and events which had broad impacts. © 2022 Owner/Author.
ABSTRACT
This paper uses scenario analysis to investigate the broader impact of teleworking in four scenarios including the COVID-19 pandemic, worst-, moderate-, and best-case scenarios on building-level energy use, energy consumption in transportation, and information and communication technology (ICT) usage by using the databases of the Government of Canada. The COVID-19 scenario relies on the available data for the pandemic period. The worst-case scenario is when telework has an adverse effect on energy use while the moderate- and best-case scenarios are when the minimum and maximum savings are achieved by telework. The data includes commuting distances, electricity and natural gas consumption for offices and residential buildings, and ICT usage. Then, the associated GHG emissions are calculated for transportation, residential and office buildings, and ICT and the analysis are carried out by applying a potential fraction of saving to the associated GHG emissions of each domain and scenario. This paper demonstrates the potential energy savings of teleworking significantly depends on teleworker behavior to a degree that in the worst-case scenario no potential saving is observed while the savings are significant in the best-case scenario. Therefore, the impact of telework is highly uncertain and complicated and current statistics are insufficient for accurate estimates. © 2021 Institute of Physics Publishing. All rights reserved.
ABSTRACT
Due to the closing of campuses, museums, and other public spaces during the pandemic, the typical avenues for recruitment, partnership, and dissemination are now unavailable to developmental labs. In this paper, we show how a shift in perspective has impacted our lab's ability to successfully transition to virtual work during the COVID-19 shut-down. This begins by recognizing that any lab that relies on local communities to engage in human research is itself a community organization. From this, we introduce a community-engaged lab model, and explain how it works using our own activities during the pandemic as an example. To begin, we introduce the vocabulary of mission-driven community organizations and show how we applied the key ideas of mission, vision, and culture to discussions of our own lab's identity. We contrast the community-engaged lab model with a traditional bi-directional model of recruitment from and dissemination to communities and describe how the community-engaged model can be used to reframe these and other ordinary lab activities. Our activities during the pandemic serve as a case study: we formed new community partnerships, engaged with child "citizen-scientists" in online research, and opened new avenues of virtual programming. One year later, we see modest but quantifiable impact of this approach: a return to pre-pandemic diversity in our samples, new engagement opportunities for trainees, and new sustainable partnerships. We end by discussing the promise and limitations of the community-engaged lab model for the future of developmental research.