Given the underrepresentation of Black individuals in computer science education and research, this Birds-of-a-Feather (BOF) session at SIGCSE aims to foster a supportive and inclusive community for members of the Black computer science education community. By bringing together members of the community, students, faculty, and allies, we seek to create a space to celebrate achievements as well as discuss issues faced by Black individuals in the field that impact diversity, equity, and inclusion. Our goals include: 1) Creating a supportive community: Foster a sense of belonging and camaraderie among Black CS educators through witnessing a larger representation of Black computer scientists; 2) Address systemic challenges: Discuss and explore strategies to address systemic barriers and biases in computer science education; 3) Share best practices: Exchange ideas and experiences related to teaching, research, and mentorship; 4) Inspire the next generation: Acknowledge present Black CS Ed trailblazers and encourage students to pursue graduate education and careers in computer science academia. 5) Gain inspiration: Learn from the personal journeys and experiences of senior students and faculty, gaining valuable insights for perseverance in computer science; 6) Promote joining supportive Black CS communities: e.g. IAAMCS, AAPHDCSR.

This BoF session invites all that are interested in discussing the relationship between mentoring and professional socialization. We will identify common barriers to accessing mentorship and the specific challenges racial and ethnic minoritized (REM) students encounter, and contrast liberatory mentoring with "traditional" forms of mentorship. Students from REM groups often seek out same-race mentors for support; however, given the limited number of REM faculty, students from marginalized groups may encounter unique challenges in obtaining mentoring support, Attention needs to be paid on mentorship practices that create inclusive program environments that allow for successful matriculation into their programs and retention in the profession. Based on the relevant scholarship, the facilitators will afford discussions for providing liberatory mentoring practices to promote students' inclusion and retention.

The intersection of pervasive technology and verbal communication has resulted in the creation of Automatic Speech Recognition Systems (ASRs), which automate the conversion of spontaneous speech into texts. ASR enables human-computer interactions through speech and is rapidly integrated into our daily lives. However, the research studies on current ASR technologies have reported unfulfilled social inclusivity and accentuated biases and stereotypes towards minorities. In this work, we provide a review of examples and evidence to demonstrate preexisting sexist behavior in ASR systems through a systematic review of research literature over the past five years. For each article, we also provide the ASR technology used, highlight specific instances of reported bias, discuss the impact of this bias on the female community, and suggest possible methods of mitigation. We believe this paper will provide insights into the harm that unchecked AI-powered technologies can have on a community by contributing to the growing body of research on this topic and underscoring the need for technological inclusivity for all demographics, especially women.

Lack of diversity and high dropout rates among underrepresented students plague the CS discipline. We developed, administered, and validated survey scales measuring social factors that impact the retention and graduation of under-represented CS undergrads at two institutions. Results revealed significant differences between students who identify as men vs. women in terms of computing identity and confidence, and between black and non-black students in terms of familiarity with future opportunities.

We present results in automating the analysis of student verbalizations in online learning environments, using an existing online tool designed to teach students to reason analytically about code as an example. The new extension captures "think-aloud'' data as students work through code reasoning activities. The data is recorded and transcribed automatically and used as input to a natural language processing / machine learning system designed to identify specific student attitudes (e.g., uncertain), behaviors (e.g., guessing), and difficulties (e.g., concept misunderstandings). We present the design and implementation of the tool, an analysis of its transcription accuracy, and an evaluation of its utility in identifying characteristics of student learning.