Interdisciplinary CS1 Course for Non-Majors: The Case of Graduate Psychology Students

The objective of interdisciplinary education is to teach multiple domains, so that the students can solve integrative problems that require knowledge and skills from these domains. Interdisciplinary programs, in which computer science is combined with other domains, range from bioinformatics to computational linguistics and data science. Data science is an interdisciplinary field that integrates computer science, mathematics, statistics, and the domain knowledge of the data involved. It is a relevant research skill for many disciplines, including social sciences. Specifically, graduate psychology students who wish to learn data science have already mastered the psychology domain knowledge and their statistics background is advanced. They lack, however, the computer science knowledge required for meaningfully handling data science. The problem is, however, that there is no CS1 course that fits graduate psychology students who study data science: On the one hand, the current CS1 courses do not suit social science students; on the other hand, CS1 courses for non-majors usually do not meet the required level of computer science needed for data science. Accordingly, to close this gap, we designed a new CS1 course – an interdisciplinary Introduction to Computer Science for Psychological Science course – that a) suits social science students and b) meets the required computer science level for data science. We evaluated the course by examining students’ learning outcomes, reflections, and responses on questionnaires. Based on the data analysis according to the self-determination motivation theory, it is concluded that the course design enabled to achieve the course’s targets.