Jamie Payton

Studying the factors that can lead to student success and persistence has been the subject of a large body of CS education research. Our earlier work on tools to engage students in Algorithms and Data Structures courses demonstrated uneven benefits, especially among underrepresented students. Using a national pool of students from 13 colleges/universities in Algorithms and Data Structures courses, we used pre and post online surveys to assess the psychometric characteristics of a revised measure of prerequisite proficiency together with 4 others (confidence in computing, precollege computing experiences, skill with computer applications and course engagement) to test for gender and ethnic differences and to investigate the factors that would be useful in predicting final course grade. We were able to confirm differences in prerequisite proficiency; however, differences were a characteristic of additional factors such as precollege experiences, self-rated skill levels and confidence in computing. Regression analysis showed these differences can be mitigated by directing efforts at multiple components, rather than just prerequisite proficiency. Measures taken in the post-survey at the end of the semester showed a reduced number of demographic differences, when compared to the pre-survey. Prerequisite proficiency and performance scores were found to be significant predictors of course grades.

Several studies have reported the positive benefits of informal Computer Science learning programs for Black girls, which include staff, mentors, and peers reflective of the girls in the program; however, we do not know enough about what motivates Black women to sign up to teach in such programs, or how representation in mentoring affects future CS learning for Black girls. Here we report the intersectional experiences of 11 Black women who served as instructors and mentors for the virtual INTech Summer Camp. The women in this study confirm that it is imperative to consider the intersectional experiences of Black women in CS as a whole, without explicating their identities. We found that Black women in CS feel that it is their responsibility to give back to Black girls to pay it forward from their own mentorship experiences. The Black women in our study describe seeking to fill a gap in mentorship that they experienced themselves, to share their knowledge and experiences to support the development of others who can relate to that intersection of identifying as Black and a girl or woman. Providing positive representation to encourage young Black girls to pursue tech careers is imperative to diversifying the tech workforce.

Multi-pronged programs that involve students in a combination of proven interventions (i.e., tutoring other students, building community, developing skills, etc.) constitute one pedagogical approach to increasing the number and diversity of computing professionals. In this manuscript, we evaluate the efficacy of one such multi-pronged program, the STARS Computing Corps, a Broadening Participation in Computing Alliance program funded by the National Science Foundation. These analyses improve upon previous efforts to assess the efficacy of STARS by examining dosage effects of the program, adding controls for students' initial intentions to pursue computing, and conducting these analyses at various points in a student's participation in STARS. We also conduct analyses to determine the efficacy of various STARS activities. Controlling for students' initial intentions to persist in computing, we find robust evidence that spending more time each week on STARS' activities positively predicts students' intentions to persist in a computing career, and that STARS has a heightened positive impact on Black and Hispanic students. We do not find evidence that the number of semesters a student spends in STARS is predictive of computing persistence, nor do we find differences in the efficacy of various STARS activities. In sum, these results suggest that STARS has a positive impact on students' intentions to persist in computing and that multi-pronged programs like STARS should focus on the intensity of participation (as opposed to the length of participation or a particular activity) to increase students' desire to persist in computing careers.

The Parallel and Distributed Computing community has been interested in integrating PDC content into early CS curriculum to prime the students for more advanced materials and build a workforce able to leverage advanced computing infrastructure. To deploy this strategy at scale, it is important to identify anchor points in early CS courses where we can insert PDC content. We present an analysis of CS courses that primarily focuses on CS1 and Data Structure courses. We collected data on course content through in-person workshops, where instructors of courses classified their course materials against standard curriculum guidelines. By using these classification, we make sense of how Computer Science is being taught. We highlight different types of CS1 and Data Structure courses. And we provide reflection on how that knowledge can be used by PDC experts to identify anchoring points for PDC content, while being sensitive to the needs of instructors.

Introducing elementary students to computer science and computational thinking (CS/CT) can enhance their problem solving skills and enhance their confidence and sense of belonging in computing. Project moveSMART aims to introduce learning activities into elementary classrooms that address computer science concepts in a way that integrates with core curriculum requirements and promotes physical activity. In this paper, we explore an extension to an initial set of Project moveSMART computer science learning activities to introduce elementary students to machine learning concepts in a way that is integrated with required learning objectives covered in a Physical Education course. Specifically, students use the BBC micro:bit and its on board sensors to capture rhythmic movement data, explore and analyze patterns in the data, and use a learned "dance move recognition" application that uses their data in order to learn about machine learning in an age appropriate way. To demonstrate feasibility of supporting dance move recognition on the resource-constrained device, we developed a prototype, which is able to detect 5 different dance moves with a 96.6% accuracy.

With limited time in the school day to meet required state-level objectives across subjects, it is challenging to meet the need to address declining physical activity levels and to incorporate additional computer science instruction in elementary schools. Project moveSMART uses a web-based platform to integrate opportunities for physical education with computer science and computational thinking (CS/CT) learning activities. Fourth grade students who underwent a single-day intervention experienced a significant improvement in their interest in coding and their perceptions of coders. This paper presents a series of three new lessons in the fifth grade moveSMART curriculum that allow students to create and improve a physical activity monitor. We employ a project-based learning approach to expose students to data analysis and machine learning in the framework of classification and pattern recognition.