New Directions for Computing Education ― Embedding Computing Across Disciplines

Why should every student take a computing course? What should be the content of these courses? How should they be taught, and by whom? This book addresses these questions by identifying the broader reaches of computing education, problem-solving and critical thinking as a general approach to learning.

The book discusses new approaches to computing education, and considers whether the modern ubiquity of computing requires an educational approach that is inherently interdisciplinary and distinct from the traditional computer science perspective. The alternative approach that the authors advocate derives its mission from an intent to embed itself within an interdisciplinary arts and science context.

An interdisciplinary approach to computing is compellingly valuable for students and educational institutions alike. Its goal is to support the educational and intellectual needs of students with interests in the entire range of academic disciplines. It capitalizes on students’ focus on career development and employers’ demand for technical, while also engaging a diverse student body that may not possess a pre-existing interest in computing for computing’s sake. This approach makes directly evident the applicability of computer science topics to real-world interdisciplinary problems beyond computing and recognizes that technical and computational abilities are essential within every discipline.

The book offers a valuable resource for computer science and computing education instructors who are presently re-thinking their curricula and pedagogical approaches and are actively trying new methods in the classroom. It will also benefit graduate students considering a future of teaching in the field, as well as administrators (in both higher education and high schools) interested in becoming conversant in the discourse surrounding the future of computing education.

Samuel B. Fee is a Professor and Chair of Computing & Information Studies at Washington & Jefferson College. His research interests extend into the realms of computing, education, and digital media production. His inquiries within these areas are aimed at better answering a few specific questions, including: How can we best learn and conduct research within the broader realm of computing? How does technology change the way we interact with one another? How do visuals and interfaces impact our abilities to communicate and learn? And, beyond the quantitative methodologies of current Social Science research, how do we reinvigorate the discourse concerning the Art of teaching? He previously co-edited the volume The Role of Criticism in Understanding Problem Solving. He publishes regularly on the topics of computing pedagogies and educational research as well as software development and digital media.

Amanda M. Holland-Minkley is an Associate Professor of Computing & Information Studies at Washington & Jefferson College. Her research explores novel applications of problem-based and hands-on pedagogies to computer science education, both at the course and the curricular level. These applications strive to transform computer science content into forms useful and accessible to students from a diversity of disciplines. She is also highly engaged in undergraduate research, with recent projects falling at the intersection of information security, data management, and machine learning.

Thomas E. Lombardi is an Assistant Professor of Computer Information Systems in the School of Business at the University of the Virgin Islands. His research focuses on the application of data science and interdisciplinary computing to a wide range of subjects such as digital humanities, networking and bioinformatics. He is particularly interested in developing pedagogies for teaching data-oriented concepts to a broad audience for the purpose of improving civic education. Most recently his theoretical research has addressed the potential role of computation in the analysis of iconography. Specifically, this project applies data mining and machine learning techniques to large repositories of artwork.