Computer science and software engineering students are typically taught to evaluate resource use in terms of time complexity. Developers use asymptotic analysis to compare algorithms by calculating how time grows as a function of input size. However, two factors have limited traditional models of complexity as pedagogical tools. First, modern systems are so fast that even relatively inefficient algorithms can quickly process large sets of data. Second, analysis is not universally engaging; only some students care about efficiency for the sake of efficiency. Our project proposes using measurements of energy consumption and concomitant environmental impact to better engage students with efficiency and its implications.

Since current students have a strong level of concern about environmental consequences, we believe energy usage data will be more concrete and motivating than differences in time and will deepen the appreciation students have for computational complexity by using real-world measurements and by tying energy usage to climate change. We also believe that these future software engineers will be better equipped to contribute to the field by understanding the broader impact of software choices and resource usage. This approach is meant to augment and give meaning to traditional measurements of computational complexity, rather than supplant them.