Making Software

Contextualizing for Motivation

What we know thus far is that beginning computing students learn far less about design and programming than we might predict, and they fail their first course at a pretty high rate. We find that changing from textual to visual modalities does not consistently result in better results, which suggests that another variable may be at play. The previous section explored the possibility that differences in how visualizations are used may be one such variable. What other variables might we manipulate in order to improve student success and learning?

In 1999, Georgia Tech decided to require introductory computing of all undergraduate students. For the first few years of this requirement, only one course satisfied this requirement. Overall, the pass rate in this course was 78%, which is quite good by Bennedsen and Caspersen’s analysis [Bennedsen and Caspersen 2007]. However, that number wasn’t so good when we start disaggregating it. The pass rate for students from the Liberal Arts, Architecture, and Management colleges was less than 50% [Tew et al. 2005a]. Women failed at nearly twice the rate of men. A course aimed at all students, but at which mostly males in technical majors pass, highlights general problems in teaching computing.

In 2003, we started an experiment to teach students in those classes a different kind of introductory course, one contextualized around manipulating media [Forte and Guzdial 2004]. Students worked on essentially the same kinds ...