Preparing, Visualizing, and Using Real-world Data in Introductory Courses

Working with real-world data has increasingly become a popular context for introductory computing courses. As a valuable 21st century skill, preparing students to be able to divine meaning from data can be useful to their long-term careers. Because Data Science aligns so closely with computing, many of the topics and problems it affords as a context can support the core learning objectives in introductory computing classes. In many instances, incorporating a real-world dataset to provide concrete context for an activity or assignment can improve student engagement and understanding of the abstract educational content being presented. However, there are many problems inherent to bringing real-world data into introductory courses. How do instructors, with finite amounts of time and energy, find and prepare suitable datasets for their pedagogical needs? Once the datasets are ready, how can students conveniently interact with and draw meaning from the datasets, especially when they are used in complex projects that are typical of later introductory courses? On the other hand, how does an instructor balance the complexities of using real-world datasets in the classroom, making sure that students appreciate the meaningfulness of course activities and their connection to learning objectives? This panel brings together experts with experience in using real-world data in introductory computing courses. Each panelist provides unique perspectives and skills to the problem of preparing, interacting, visualizing, and using pedagogical datasets. This panel should be of particular interest to instructors who are considering integrating current and real-world data into their assignments and projects, and to educational developers who want to create and manage datasets for pedagogical purposes. The panel will follow a conventional format: 5 minutes of introduction, 10 minutes for each panelist to present, and then 30 minutes for audience Q&A.