Measuring the Impact of Unique Entry-Level Instructional Course Modules Designed to Inspire Computer Science Interest

Recent research regarding university student perceptions of “Computer Science” as a field of study and their motivation to pursue such studies as a career opportunity reveal student misconceptions and lack of motivation. Many students report that they regard the study of computer science as narrowly equivalent to “programming”. Moreover, many are not consistently provided the opportunity to realize the true impact of the field within their entry-level courses since these early courses tend to focus on programming and syntax skill development. It is not until they are in their upper-level courses that they gain a broader understanding and by then, many of them have already left the field. It is hypothesized that this lack of clarity of the field at an early point in students’ academic career, coupled with the perception that the curriculum is largely irrelevant to their lives, has impacted the retention rates of computer science majors in the first two years of their academic study programs. This paper will report on a preliminary stage of a comprehensive project effort that aims to improve retention rates for computer science students in their entry-level courses through the development of course modules intended for inclusion in their entry-level curriculum. The theoretical basis for these modules will be reviewed and the design framework for the development of these models is discussed. The aim of these models is to highlight the difference between Computer Science and Programming, to show the relevance of Computer Science in recent advances in various fields, and to inspire students to appreciate Computer Science and the role of algorithms in our daily lives. The modules will cover various topics about the role of CS in cyber warfare, understanding biology, electronic voting, etc. In subsequent work, these modules will be launched as part of a mixed methods study to determine their effectiveness as compared to a control group not learning through these models and the impact of those modules on the retention rates of Computer Science majors.