Content, Connection and Careers: Kit-Based Learning and Virtual University Connections (Evaluation)

Science kits have been a staple of learning for some time, but in the era of COVID-19 at-home science kits took specific prominence in educational initiatives. In this paper, we delineate how kit-based education can be paired with virtual connection technology to enhance postsecondary and career exploration. The “Content, Connection and Careers” kit-based program has been developed to enable youth to explore electrical engineering principles while connecting virtually with university students to discuss engineering courses and careers. When assembled and wired up, the kit components become linear motors that use a magnetic force to pull a bolt into a pipe when youth press a button. This follows the same working principles as a doorbell or solenoid. These kits are supported by virtual learning sessions where youth connect with university students and faculty to fully understand the educational content, connect to peers and caring adults to share their learning, and explore careers that use electrical engineering skills. To investigate the effectiveness of the program, surveys were distributed to participants to understand whether the kits were simple enough for independent learning but robust enough to encourage additional self-exploration of more difficult topics with the aid of expert scientists and other adult role models. Additionally, youth were asked if the connections made with university faculty and students was beneficial in their thinking of postsecondary options and college engagement. Over 60 elementary and middle-school aged youth participated in the project. Over 80 percent of survey respondents self-reported improved knowledge of how an electromagnetic field works and how to build a simple electromagnet. Other results showed an increased understanding of engineering careers and courses required to study electric engineering in college. Before their experience in the project, very few of the young people had ever talked to university faculty or university students about their areas of research or their journey into the fields of science, technology, engineering, and math (STEM). This connection was described in the surveys as what the youth liked best about the project. © American Society for Engineering Education, 2022.

Storycircling the Virtual Creating Space in a Pandemic with Storyscope

Stories offer time and space for connection. This has been particularly true during social distancing amid the COVID-19 pandemic. In this collective autoethnographic story, we explore how generative energies of storytelling and storylistening emerge within communities via virtual storytelling. With COVID-19 being a catalyst for change, we share our adaptation of the Storyscope Project story circles to facilitate connection through a virtual space. Our stories within this work reflect our experience of virtual Storyscope in the roles of host, facilitator, participant, and educator. Additionally, the collaborative process of our writing mirrors the unfolding of virtual Storyscope story circles. In other words, the practices of virtual storytelling and storylistening guided our inquiry and evolving discussion of a similarly evolving practice. © 2022 by Wayne State University Press.

My Mind is a Prison: A Boosted Deep Learning approach to detect the rise in depression since COVID-19 using a stacked bi-LSTM CatBoost model

The COVID-19 pandemic has significantly altered our way of life. Physical, social interactions are being steadily replaced with virtual connections and remote interactions. Social media platforms such as Facebook, Twitter, and Instagram have become the primary medium of communication. However, being relegated to a solely online presence has had a major impact on the mental health of users since the onset of the pandemic. The present study aims to identify depressed Twitter users by analyzing their tweets. We propose a deep learning model which stacks a bidirectional LSTM layer along with a CatBoost Algorithm layer to classify tweets and detect depression. The results show that the proposed model outperforms standard machine learning approaches to classification and that there was a definite rise in depression since the beginning of the pandemic. The study's primary contribution is the novel deep learning model and its ability to detect depression. © 2021 IEEE.