Attitudes play an important role in students' academic achievement and retention, yet quality tools to measure them are not readily available in the new field of data science. Through funding from the National Science Foundation, we are developing a family of instruments that measure attitudes toward data science in the context of an introductory, college-level course. This poster will showcase preliminary results discussing pilot instruments to assess instructors' attitudes toward teaching data science and an inventory which captures classroom characteristics. These instruments, based on Expectancy-Value Theory, will enable data science education researchers to evaluate pedagogical innovations and measure instructional effectiveness relating to student attitudes. We invite instructors of data science courses to join in this discussion and to use these instruments for their own data science education research projects.

Introductory data science courses are appearing at colleges, universities, and high schools around the country and the world. What topics do we cover in these courses, and how and why are these decisions made? How do we consider the background knowledge of our students and how they hope to utilize their skills after this course (whether professionally, additional courses, or as an engaged citizen)? In addition, the course is being taught by computer scientists, statisticians, business analysts, mathematicians, journalists, etc. Each of these disciplines approaches the topics differently. What upskilling is required of instructors to prepare them to integrate material from academic disciplines in which they were not trained into the course? How much, if any, cross-disciplinary collaboration and discussion occurs or should occur in designing this course? Participants in this birds-of-a-feather will share their decision processes and choices about introductory data science courses that they teach or are designing. This includes choices made about the content as well as whether and how upskilling occurs. They will review and refine a list of current data science topics created based on national surveys of data science instructors as well as a review of curriculum guidelines. Close attention will be paid to differing language between data science instructors from different academic backgrounds. We welcome new and experienced data science instructors, educators planning on or interested in teaching such a course.

Data Science is one of the fastest growing fields with unmet demand from employers. Many academic institutions have taken on the task of creating programs to meet both current and future needs and demands. Data science, as a field, integrates aspects of computer science, statistics, and subject matter expertise which encourages cross-disciplinary conversations and collaboration. In this talk, we present results from a broad survey of instructors of introductory college-level data science courses for undergraduates. In addition, we explore the alignment of these findings with the recommendations of various professional organizations.

Attitudes play an important role in students' academic achievement and retention, yet quality tools to measure them are not readily available in the new field of data science. Through funding from the National Science Foundation, we are developing instruments that measure attitudes toward data science in the context of an introductory, college-level course. This talk focuses on preliminary work to develop the Student Survey of Motivational Attitudes toward Data Science (S-SOMADS), which includes construct development, item creation involving content experts from numerous disciplines, pilot testing, and item refinement. Along with the student instrument, we intend to develop an instructor attitude instrument as well as an environment inventory to capture instructor attitudes and course characteristics that may be associated with attitudes. This family of instruments, based on Expectancy-Value Theory, will enable data science education researchers to evaluate pedagogical innovations, create course assessments, and measure instructional effectiveness relating to student attitudes. We invite instructors of data science courses to participate in this project to discuss data science topics, serve as subject matter experts, or to collect pilot data from themselves and their students.

Data scientists - practitioners, researchers, and educators - often disagree on the definition of data science. Data science courses are taught by faculty in departments of computer science, statistics, and business analytics as well allied fields. Some data scientists create scalable dashboards, others employ and (hopefully) explain machine learning models, most (but not all) demand data wrangling and visualization skills, and some require theoretical knowledge to develop novel algorithmic and/or analytic techniques. How, then, do we determine what content should be covered in an introductory college-level data science course? Some serve as prerequisites while others are terminal experiences for non-majors (who we secretly hope to entice into the field). Some courses require prior knowledge of statistical methods, databases, or programming, uniting heterogeneous populations of students and demanding flexibility regarding programming language(s) utilized or allowed. Realistically, we often make expeditious choices because competing demands leave us hard-pressed to keep up-to-date with current skills and practices. BoF participants will share their decision processes and choices about content for an introductory data science course. Information will be shared, from participants and disseminated material, on resources and curriculum guides from various professional organizations. We welcome new and experienced data science instructors, educators planning on or interested in teaching such a course, and industrial practitioners experienced in working with or hiring undergraduates.