Early in the pandemic we gathered a group of educators to create and share at-home educational opportunities for families to design and make STEAM projects while at home. As this effort, CoBuild19, continued, we decided to extend our offerings to include basic computer programming. To accomplish this, we created an offering called the Design with Code Club (DwCC). We structured DwCC to be different from other common coding offerings in that we wanted the main focus to be on kids designing solutions to problems that might include the use of technology and coding. We were purposeful in this decision for two main reasons. First, we wanted to make our coding club more interesting to girls, where previous research demonstrates their interest in designing solutions. Second, we wanted this effort to be different from most programming instruction, where coding activities use programming as the core of instruction and application in authentic and student-selected contexts plays a secondary role. DwCC was set up so that each of the first four weeks had a different larger challenge that was COVID-19 related and sessions unfolded with alternating smaller challenges, discussion around design and coding instruction that would develop their skills and knowledge of micro:bit capabilities. We culminated DwCC with an open-ended project where the kids were given the challenge of coming up with their own problem for which they might incorporate micro:bit as part of the solution.
Because we were doing all of this online, we used the micro:bit interface through Microsoft MakeCode, which includes a functional simulator. From our experiences we realized that simulations are not as enticing as physical computing with a tangible device, so we set up an incentive where youth who participated in at least three sessions of the club would receive a physical micro:bit. We advertised DwCC through Facebook and twitter and had nearly 200 families register their kids to participate. In the end, a total of 52 micro:bits were sent to youth participants.
Based on this success, we sought to expand the effort and increase accessibility for groups that are traditionally underrepresented in STEM. In spring 2021, we offered a Girls DwCC. This was a redesigned version of the club where the focus was even more on problem-solving through design. The club was run by all women, including one from the US, an Industrial Engineer from Mexico and a computer programmer from Albania. More than 50 girls from 17 countries participated in the club! We are working on another version of GDwCC that will be offered in Spanish and focus on Latina girls in the US and Mexico.
In the most recent iteration of DwCC we are working with an educator at a school for deaf students to create a version of the club that works for their students. We are doing some modification of activities and recreating videos that involve sign language interpretation.
In this presentation we will report on the variants of DwCC, results from participant feedback surveys and plans for future versions.
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Person Detection in Collaborative Group Learning Environments Using Multiple Representations
We introduce the problem of detecting a group of students from classroom videos. The problem requires the detection of students from different angles and the separation of
the group from other groups in long videos (one to one and a half hours).
We use multiple image representations to solve the problem. We use FM components to separate each group from background groups, AM-FM components for detecting the back-of-the-head, and YOLO for face detection. We use classroom videos from four different groups to validate our approach. Our use of multiple representations is shown to be significantly more accurate than the use of YOLO alone.
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- NSF-PAR ID:
- 10310099
- Date Published:
- Journal Name:
- 2021 Asilomar Conference on Signals, Systems, and Computers
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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While engineering grows as a part of elementary education, important questions arise about the skills and practices we ask of students. Both collaboration and decision making are complex and critical to the engineering design process, but come with social and emotional work that can be difficult for elementary students to navigate. Productive engagement in collaborative teams has been seen to be highly variable; for some teams, interpersonal conflicts move the design process forward, while for others they stall the process. In this work in progress, we are investigating the research question, what is the nature of students’ disciplinary talk during scaffolded decision making? We explore this research question via a case study of one student group in a 4th-grade classroom enrolled in an outreach program run by a private university in a Northeastern city. This program sends pairs of university students into local elementary schools to facilitate engineering in the classroom for one hour per week. This is the only engineering instruction the elementary students receive and the engineering curriculum is planned by the university students. For the implementation examined in this study, the curriculum was designed by two researchers to scaffold collaborative groupwork and decision making. The instruction was provided by an undergraduate and one of the researchers, a graduate student. The scaffolds designed for this semester of outreach include a set of groupwork norms and a decision matrix. The groupwork norms were introduced on the first day of instruction; the instructors read them aloud, proposed groupwork scenarios to facilitate a whole class discussion about whether or not the norms were followed and how the students could act to follow the norms, and provided time for students to practice the norms in their engineering design groups for the first project. For the rest of the semester, an anchor chart of the norms was displayed in the classroom and referenced to encourage consensus. The researchers designed the decision matrix scaffold to encourage design decisions between multiple prototypes based on problem criteria and test results. Instructors modeled the use of this decision matrix on the third day of instruction, and students utilized the matrix in both design projects of the semester. Data sources for this descriptive study include students’ written artifacts, photos of their design constructions, and video records of whole-class and team discourse. We employ qualitative case study and microethnographic analysis techniques to explore the influence of the intentional discourse scaffolds on students’ collaborative and decision-making practices. Our analysis allowed us to characterize the linguistic resources (including the decision matrix) that the students used to complete four social acts during decision making: design evaluation, disagreeing with a teammate, arguing for a novel idea, and sympathizing with a design. This research has implications for the design of instructional scaffolds for engineering curricula at the elementary school level, whether taking place in an outreach program or in regular classroom instruction.more » « less
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Abstract Advances in visual perceptual tasks have been mainly driven by the amount, and types, of annotations of large-scale datasets. Researchers have focused on fully-supervised settings to train models using offline epoch-based schemes. Despite the evident advancements, limitations and cost of manually annotated datasets have hindered further development for event perceptual tasks, such as detection and localization of objects and events in videos. The problem is more apparent in zoological applications due to the scarcity of annotations and length of videos-most videos are at most ten minutes long. Inspired by cognitive theories, we present a self-supervised perceptual prediction framework to tackle the problem of temporal event segmentation by building a stable representation of event-related objects. The approach is simple but effective. We rely on LSTM predictions of high-level features computed by a standard deep learning backbone. For spatial segmentation, the stable representation of the object is used by an attention mechanism to filter the input features before the prediction step. The self-learned attention maps effectively localize the object as a side effect of perceptual prediction. We demonstrate our approach on long videos from continuous wildlife video monitoring, spanning multiple days at 25 FPS. We aim to facilitate automated ethogramming by detecting and localizing events without the need for labels. Our approach is trained in an online manner on streaming input and requires only a single pass through the video, with no separate training set. Given the lack of long and realistic (includes real-world challenges) datasets, we introduce a new wildlife video dataset–nest monitoring of the Kagu (a flightless bird from New Caledonia)–to benchmark our approach. Our dataset features a video from 10 days (over 23 million frames) of continuous monitoring of the Kagu in its natural habitat. We annotate every frame with bounding boxes and event labels. Additionally, each frame is annotated with time-of-day and illumination conditions. We will make the dataset, which is the first of its kind, and the code available to the research community. We find that the approach significantly outperforms other self-supervised, traditional (e.g., Optical Flow, Background Subtraction) and NN-based (e.g., PA-DPC, DINO, iBOT), baselines and performs on par with supervised boundary detection approaches (i.e., PC). At a recall rate of 80%, our best performing model detects one false positive activity every 50 min of training. On average, we at least double the performance of self-supervised approaches for spatial segmentation. Additionally, we show that our approach is robust to various environmental conditions (e.g., moving shadows). We also benchmark the framework on other datasets (i.e., Kinetics-GEBD, TAPOS) from different domains to demonstrate its generalizability. The data and code are available on our project page:
https://aix.eng.usf.edu/research_automated_ethogramming.html -
Introduction and Theoretical Frameworks Our study draws upon several theoretical foundations to investigate and explain the educational experiences of Black students majoring in ME, CpE, and EE: intersectionality, critical race theory, and community cultural wealth theory. Intersectionality explains how gender operates together with race, not independently, to produce multiple, overlapping forms of discrimination and social inequality (Crenshaw, 1989; Collins, 2013). Critical race theory recognizes the unique experiences of marginalized groups and strives to identify the micro- and macro-institutional sources of discrimination and prejudice (Delgado & Stefancic, 2001). Community cultural wealth integrates an asset-based perspective to our analysis of engineering education to assist in the identification of factors that contribute to the success of engineering students (Yosso, 2005). These three theoretical frameworks are buttressed by our use of Racial Identity Theory, which expands understanding about the significance and meaning associated with students’ sense of group membership. Sellers and colleagues (1997) introduced the Multidimensional Model of Racial Identity (MMRI), in which they indicated that racial identity refers to the “significance and meaning that African Americans place on race in defining themselves” (p. 19). The development of this model was based on the reality that individuals vary greatly in the extent to which they attach meaning to being a member of the Black racial group. Sellers et al. (1997) posited that there are four components of racial identity: 1. Racial salience: “the extent to which one’s race is a relevant part of one’s self-concept at a particular moment or in a particular situation” (p. 24). 2. Racial centrality: “the extent to which a person normatively defines himself or herself with regard to race” (p. 25). 3. Racial regard: “a person’s affective or evaluative judgment of his or her race in terms of positive-negative valence” (p. 26). This element consists of public regard and private regard. 4. Racial ideology: “composed of the individual’s beliefs, opinions and attitudes with respect to the way he or she feels that the members of the race should act” (p. 27). The resulting 56-item inventory, the Multidimensional Inventory of Black Identity (MIBI), provides a robust measure of Black identity that can be used across multiple contexts. Research Questions Our 3-year, mixed-method study of Black students in computer (CpE), electrical (EE) and mechanical engineering (ME) aims to identify institutional policies and practices that contribute to the retention and attrition of Black students in electrical, computer, and mechanical engineering. Our four study institutions include historically Black institutions as well as predominantly white institutions, all of which are in the top 15 nationally in the number of Black engineering graduates. We are using a transformative mixed-methods design to answer the following overarching research questions: 1. Why do Black men and women choose and persist in, or leave, EE, CpE, and ME? 2. What are the academic trajectories of Black men and women in EE, CpE, and ME? 3. In what way do these pathways vary by gender or institution? 4. What institutional policies and practices promote greater retention of Black engineering students? Methods This study of Black students in CpE, EE, and ME reports initial results from in-depth interviews at one HBCU and one PWI. We asked students about a variety of topics, including their sense of belonging on campus and in the major, experiences with discrimination, the impact of race on their experiences, and experiences with microaggressions. For this paper, we draw on two methodological approaches that allowed us to move beyond a traditional, linear approach to in-depth interviews, allowing for more diverse experiences and narratives to emerge. First, we used an identity circle to gain a better understanding of the relative importance to the participants of racial identity, as compared to other identities. The identity circle is a series of three concentric circles, surrounding an “inner core” representing one’s “core self.” Participants were asked to place various identities from a provided list that included demographic, family-related, and school-related identities on the identity circle to reflect the relative importance of the different identities to participants’ current engineering education experiences. Second, participants were asked to complete an 8-item survey which measured the “centrality” of racial identity as defined by Sellers et al. (1997). Following Enders’ (2018) reflection on the MMRI and Nigrescence Theory, we chose to use the measure of racial centrality as it is generally more stable across situations and best “describes the place race holds in the hierarchy of identities an individual possesses and answers the question ‘How important is race to me in my life?’” (p. 518). Participants completed the MIBI items at the end of the interview to allow us to learn more about the participants’ identification with their racial group, to avoid biasing their responses to the Identity Circle, and to avoid potentially creating a stereotype threat at the beginning of the interview. This paper focuses on the results of the MIBI survey and the identity circles to investigate whether these measures were correlated. Recognizing that Blackness (race) is not monolithic, we were interested in knowing the extent to which the participants considered their Black identity as central to their engineering education experiences. Combined with discussion about the identity circles, this approach allowed us to learn more about how other elements of identity may shape the participants’ educational experiences and outcomes and revealed possible differences in how participants may enact various points of their identity. Findings For this paper, we focus on the results for five HBCU students and 27 PWI students who completed the MIBI and identity circle. The overall MIBI average for HBCU students was 43 (out of a possible 56) and the overall MIBI scores ranged from 36-51; the overall MIBI average for the PWI students was 40; the overall MIBI scores for the PWI students ranged from 24-51. Twenty-one students placed race in the inner circle, indicating that race was central to their identity. Five placed race on the second, middle circle; three placed race on the third, outer circle. Three students did not place race on their identity circle. For our cross-case qualitative analysis, we will choose cases across the two institutions that represent low, medium and high MIBI scores and different ranges of centrality of race to identity, as expressed in the identity circles. Our final analysis will include descriptive quotes from these in-depth interviews to further elucidate the significance of race to the participants’ identities and engineering education experiences. The results will provide context for our larger study of a total of 60 Black students in engineering at our four study institutions. Theoretically, our study represents a new application of Racial Identity Theory and will provide a unique opportunity to apply the theories of intersectionality, critical race theory, and community cultural wealth theory. 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Artificial Intelligence (AI) developments in recent years have allowed several new types of applications to emerge. In particular, detecting people and objects from sequences of pictures or videos has been an exciting field of research. Even though there have been notable achievements with the emergence of sophisticated AI models, there needs to be a specialized research effort that helps people finding misplaced items from a set of video sequences. In this paper, we leverage voice recognition and Yolo (You Only Look Once) real-time object detection system to develop an AI-based solution that addresses this challenge. This solution assumes that previous recordings of the objects of interest and storing them in the dataset have already occurred. To find a misplaced object, the user delivers a voice command that is in turn fed into the Yolo model to detect where and when the searched object was seen last. The outcome of this process is a picture that is provided as evidence. We used Yolov7 for object detection thanks to its better accuracy and wider database while leveraging Google voice recognizer to translate the voice command into text. The initial results we obtained show a promising potential for the success of our approach. Our findings can be extended to be applied to various other scenarios ranging from detecting health risks for elderly people to assisting authorities in locating potential persons of interest.more » « less
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