The purpose of this repository is to give you feedback on your overall grade in Computer Science 202, Algorithm Analysis. There are no files inside of this repository and you do not need to add any files to this repository.
Alternatively, the course instructor will add overall course assessment reports to either the issue tracker or the pull requests tracker in this repository. Students who have questions about their overall assessments are encouraged to schedule a meeting with the course instructor during office hours.
As explained in the course syllabus on the course web site, the grade that a student receives in this class will be based on the following categories. All of these percentages are approximate and, if the need to do so presents itself, the course instructor may, for instance, change the assigned percentages during the academic semester.
| Category | Percentage |
|---|---|
| Class Participation | 10% |
| Midterm Examination | 15% |
| Final Examination | 15% |
| Algorithm All-Hands | 10% |
| Algorithm Engineering | 50% |
These assessment categories have the following definitions:
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Class Participation: Students are expected to regularly attend and actively participate in all of the class and laboratory sessions, as outlined on the course schedule. After either an unexcused absence or a late attendance to either a class or a laboratory session, a student's weekly class participation grade will be reduced. Students who need to miss class or attend class late for an excused reason should communicate their situation to the course instructor in a timely fashion. A student's weekly class participation grade will be reduced if they are frequently observed, during either class or laboratory sessions, undertaking non-course-related activities like viewing email, social media, or other content not about algorithm analysis.
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Midterm Examination: The midterm examination is an online, executable, and cumulative assessment covering all prior material from the course sessions, as outlined on the course schedule. Unless prior arrangements are made with the instructor, all students should use their computer to take these tests on the scheduled date and to complete it in the stated location while taking no more than the required amount of time. The midterm is an executable examination that students will complete through the use of GitHub, VS Code, and the Python programming tools installed on their laptops. Following the completion of the midterm examination during a laboratory session, students will, during algorithm all-hands sessions, collaboratively complete and present an analytical and empirical evaluation of the performance of one component of their examination. Students may use external sources, including artificial intelligence coding assistants, during the completion of the midterm examination provided that they cite these sources and explain how they used them to complete the examination.
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Final Examination: The final examination is an online, executable, cumulative assessment covering all of the material during all of the course sessions, as outlined on the course schedule. Unless prior arrangements are made with the instructor, all students should use their computer to take the final examination on the scheduled date and to complete it in the stated location while taking no more than the required amount of time. The cumulative final is an executable examination that a student will complete through the use of GitHub, VS Code, and the Python programming tools installed on their laptops. Students may use external sources, including artificial intelligence coding assistants, during the completion of the final examination provided that they cite these sources and explain how they used them to complete the examination.
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Algorithm All-Hands: These projects enable students to explore both the scientific and engineering aspects of algorithm analysis, as outlined in the course schedule. During the completion of a scientific study in the field of algorithm analysis, students will complete and present a team-based project in which they propose and then answer a research question about the performance of a Python program. When completing an engineering effort in the field of algorithm analysis, students will build and present a team-based project in which they design, implement, document, release, and maintain software tools that support the rigorous evaluation of the performance (e.g., time or space overhead) of a Python program. Students may use external sources, including artificial intelligence coding assistants, during the completion of an algorithm all-hands project provided that they cite these sources and explain how they used them to complete their part(s) of an algorithm all-hands project.
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Algorithm Engineering Projects: These projects enable students to explore both the scientific and engineering aspects of algorithm analysis, as outlined in the course schedule. During the completion of this two-week project, students will design and implement a software system suitable for conducting experiments and then use it answer research questions that connect to previously discussed scientific content, as outlined in the course schedule. After implementing the benchmarking system, students will propose and formally state research questions and then discuss them with the course instructor during a laboratory session. Finally, students will conduct experiments to answer these research questions, collect data, analyze the results, and write a report that explains the results in the context of the implemented system. Students may use external sources, including artificial intelligence coding assistants, during the completion of an algorithm all-hands project provided that they cite these sources and explain how they used them to complete the various components of an algorithm engineering project.
Please note that any overall score reported to this GitHub repository is neither a final grade nor a commitment that a student will earn the reported grade as their final grade. It is especially important to note that any reported grade may be an over-approximation of a final grade since it could include a perfect score for assessments that will be completed later in the semester but for which the course instructor has not yet reported a grade.