With the rapid growth of science, technology, engineering, and mathematics (STEM) jobs in the United States, stakeholders are investing more resources to expand participation in these fields in terms of overall numbers as well as representation across demographics. Although there has been a steady stream of federal and corporate investments in STEM programs, reaching and engaging youth from underrepresented communities remains a challenge. Attempting to disrupt this trend, the National Society of Black Engineers (NSBE) is leveraging over ten years of experience to further develop and expand

Concern over the underrepresentation of women and certain minority populations in engineering has been a concern for the last few decades. Government agencies, non-profit organizations, and private companies have invested significant money and resources to address this concern (Committee on Equal Opportunities in Science and Engineering (CEOSE), 2017; Gibbin & Davis, 2002); however, the numbers of women and underrepresented minorities participating in engineering remains dismal (National Science Foundation & National Center for Science and Engineering Statistics, 2017). Much of this money has

One significant barrier to broadening participation in engineering and recruiting future engineers is the pervasive lack of understanding or even misunderstanding of what engineering is and what engineers do. The challenges to broadening participation in engineering are further complicated as underrepresented groups often report constructs, such as cultural milieu and outcome expectations, as more important than interest in influencing career choices. Addressing such issues is difficult and single exposure interventions are unlikely to make engineering careers seem more relevant or attainable

Less than 30% of students enrolled in the U.S. are proficient in science or mathematics. The landscape becomes more troubling among students who traditionally are underrepresented in STEM. For instance, in 2015, fourth grade Black students scored on average 24 points lower than their White counterparts, and 35 points lower than their Asian American counterparts. When data are disaggregated further by sex, underrepresented males, Black males in particular, underperform Black girls on fourth grade mathematics assessment. Additionally, underrepresented males who graduate from high school complete

Our NSF-funded ITEST project focuses on the collaborative design, implementation, and study of recurrent hands-on engineering activities with middle school youth in three rural communities in or near Appalachia. To achieve this aim, our team of faculty and graduate students partner with school educators and industry experts embedded in students’ local communities to collectively develop curriculum to aim at teacher-identified science standard and facilitate regular in-class interventions throughout the academic year. Leveraging local expertise is especially critical in this project because

The paper introduces the problem of robust head detection in collaborative learning environments. In such environments, the camera remains fixed while the students are allowed to sit at different parts of a table. Example challenges include the fact that students may be facing away from the camera or exposing different parts of their face to the camera. To address these issues, the paper proposes the development of two new methods based on Amplitude Modulation-Frequency Modulation (AM-FM) models. First, a combined approach based on color and FM texture is developed for robust face detection

Human activity classification remains challenging due to the strong need to eliminate structural noise, the multitude of possible activities, and the strong variations in video acquisition. The current paper explores the study of human activity classification in a collaborative learning environment.This paper explores the use of color based object detection in conjunction with contextualization of object interaction to isolate motion vectors specific to each human activity. The basic approach is to make use of separate classifiers for each activity. Here, we consider the detection of typing

In this paper, we summarize some of the lessons learned from the Advancing Out-of-School Learning in Mathematics and Engineering (AOLME) project. The AOLME project uses an integrated curriculum that relies on the use of basic concepts from middle-school mathematics to teach the foundations of image and video representations. The middle-school students, mostly from underrepresented groups, learn how to program their own video representations using Python libraries running on the Raspberry Pi. Overall, we have found that the students enjoy participating in the project.

Pre-post data from 764 treatment and 553 comparison middle school students from nine US states were analyzed to examine STEM-related attitudes and dispositions aligning with climate change beliefs and intentions, and acquisition of energy and environmental science content knowledge. Findings were that at least seven non-cognitive variables contribute between eight and fifteen percent of the beliefs, intentions, and knowledge acquisitions of interest in this study, in linear combinations of subsets of the variables. Implications of these findings for promoting interest in STEM are discussed

This research report presents the results of a STEM summer program on robotics and game design. The program was part of a three-year study funded by the National Science Foundation. Children in grades four through six participated in a two-week summer camp in 2015 to learn STEM by engaging in LEGO® EV3 robotics and computer-based games using Scalable Game Design. Twenty-eight students participated in the study that took place in a small urban community in the Rocky Mountain West. This paper reports on the results of this part of the study, specifically, how children’s computational thinking