Contains fulltext : 333701.pdf (Publisher’s version ) (Open Access)

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.

Students struggle with how to loop through lists in Python, often mixing and matching their strategies between looping by value and looping by index. At the heart of this issue is the concept of indexing, which can be a challenging abstraction for students. This work uses Resource Theory, a cognitive framework based on DiSessa's Knowledge in Pieces, to identify concepts and procedures that students activate when solving problems involving indexing.

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.

Data science is often heralded as a key learning goal for students in STEM classrooms. There are also myriad efforts to integrate data science into these classrooms, and many dedicated research efforts for identifying the best ways to do so. However, the problem is that there is little agreement on how to introduce data science to students, whether it be through computer science courses where students can learn programming, through STEM courses where students can learn disciplinary knowledge, or through newly designed data science centric courses. Furthermore, best practices for teaching data science require an understanding of what data science is from students' perspectives, and how they experience it. This poster explores this problem by showcasing an interview study of an undergraduate course offered at Michigan State University, which focuses on computational modeling and data analysis. Students in this course learn data science via problem-based group work and apply it to several disciplinary contexts. The interview study examines how students perceived what they learned, and how their self-efficacy developed over the course of the semester. In effect, we demonstrate a course where students are learning data science, identify the key features of the course that students perceive, and build an understanding of data science self-efficacy, which can be used to help design positive, effective experiences in data science courses.