This lightning talk presents early work surrounding undergraduate students' experiences in data science and computing classrooms that shape their identities. Identity in learning spaces has been studied extensively in STEM disciplines, but little research has focused on undergraduate students majoring in data science. Since data science sits at the intersection of several disciplines, such as mathematics, statistics, and computer science, it is unclear how data science students navigate those individual discipline identities and/or construct a unique identity that is greater than the sum of those parts. As Michigan State University's Computational Mathematics, Science, and Engineering department offers data science degrees, it is of particular interest to understand the classroom messaging and experiences that shape our students' sense of self in relation to data science and computing. The project will build on various identity frameworks understand how students' data science identities are developed or stifled through classroom experiences. From previous research, we know that prototypical identities of STEM center cisgender, heterosexual, white male students and can exclude students from different demographics and lived experiences. To address this, I will utilize a critical lens to interrogate possible inequity, bias, and inaccessibility that may emerge from students' identity shaping experiences in these classrooms. At the time of this lightning talk, a thematic analysis of the first seven interviews I have conducted will be presented. All are welcome to attend the presentation and provide feedback and insight on initial observations, synthesis, and plans for the future.

As computing becomes increasingly intertwined with other disciplines, research that centers computing education in an interdisciplinary context (and the challenges surrounding it) is increasingly relevant. In particular, writing modular code (i.e. using functions) is a fundamental part of scientific programming. However, functions have been identified as challenging for students to learn. This assignment leverages a fun, real, and approachable dataset from the 2018 Central Park Squirrel Census as well as experiences authentic to developing scientific programs to introduce the concept of functions in Python. The assignment is intended to be delivered in a "flipped classroom" format, where students are first introduced to concepts in videos and short problems prior to coming to class. Once in class, the students work collaboratively in groups of 4-6 with a hands-on programming activity. By the end of the assignment, students have not only had the opportunity to learn about functions in an authentic context, but they have also been able to make calculations and propose their own problems to learn about the data.

Few would argue that modern careers across a wide range of disciplines can be performed in the absence of computing in one form or another. As such, it is becoming increasingly important for our education system to appropriately prepare students for the modern world by integrating computing and computational thinking into how students learn disciplinary content (i.e. disciplines outside of computer science). However, how to best perform this integration is not yet known, nor is there likely to be one ''best'' method. In this session, we will facilitate a discussion of the variety of ways in which post-secondary institutions are actively using computing to support disciplinary learning by highlighting examples while also considering mechanisms not yet explored. We invite anyone who wants to learn more about current efforts, share their own experiences, and contribute ideas for future endeavors. In an effort to maximize the diversity of perspectives in this discussion, we've included discussion leaders who span a variety of roles within curriculum development and classroom instruction and encourage participants from all levels and backgrounds.