Evaluated December 2021
This module is a major programming assignment that focuses making determinations about which classes should be online-only or in-person so that students are less likely to contract a disease (e.g., COVID-19) via in-class contacts. It focuses on “Greedy Algorithms” and is intended to be used in a course project on Algorithms alongside the text, Algorithm Design, by Kleinberg and Tardos. The module involves algorithm design, experimental analysis of the algorithm and it requires a written report on the analysis including a “Human Impact Analysis.”
It directly covers material in Algorithms and Complexity/Fundamental Data Structures and Algorithms, Algorithms and Complexity/Advanced Data Structures Algorithms and Analysis.
An instructor adopting this module will need to engage in background work to identify key social and ethical considerations to accompany the technical aspects of the assignment. Supplementing the existing information with readings on inequities, especially those that explore the challenges associated with the ethical and social issues associated with tradeoffs in optimization/decision making or in educational disparities would be helpful. Additional readings on human impacts and the social implications will also be helpful. To enhance the module for those students seeking to look at local issues, an instructor can provide additional guidelines for students or examples that may help them frame these next steps. Students have opportunities to move beyond the basic work by looking at their own institution’s scheduling practices. Students who have already had opportunities to explore ethics and social inequalities will more quickly identify with the central aspects of this module.
Instructors who use this module will find some guidance on development assessments for basic components of the assignment but will need to design an assessment strategy that provides criteria for writing the “Human Impact Analysis.”
The evaluation of this module was led by Darakhshan Mir and Emanuelle Burton as part of the Mozilla Foundation Responsible Computer Science Challenge. Patrick Anderson, Judy Goldsmith, Colleen Greer, Jaye Nias, Evan Peck and Marty J. Wolf also made contributions. These works are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.