Marking Scheme

This course will be graded unconventionally, motivated by an instructional philosophy called Mastery Learning. The basic idea is that you will be assessed based on whether you learn a topic and the depth with which you learn it. This is a flavor of specifications grading that is adapted for a system, like McMaster, where I am required to give you a letter grade, and not merely a pass/fail mark. The basic intention is that your mark will reflect how far you have managed to ascend Bloom’s Taxonomy of Learning as it relates to Quantum Chemistry.

Assignments

Your course mark will consist entirely of assignments. There will be a final three (maybe just two) assignments that will be due, and defended, during the final exam period. Assignments will be submitted, and marked for correctness (often automatically) using GitHub Classroom. Assignments will be marked for mastery via in-person (or one-on-one virtual) interviews (~20 minutes). The interview will also assess knowledge/understanding/masteryof the course material.

By default, all assignments are due at 11:59pm on Sunday.

The marking scheme for assignments is below. It is intended that the “default” grade (corresponding to B-level work) is an S.

U : Unsuccessful. The assignment was either not submitted or it did not fulfill the objectives of the assignment, typically because it did not pass the (automatic) tests. Alternatively, the student was unable to explain the thought processes they used to complete the assignment. Alternatively, the student failed to demonstrate knowledge of the relevant course material.

S- : Marginal. The assignment was submitted on time and fulfilled the objectives. The student fully understands the thought processes required to complete the assignment, but seems to have been working near the edge of their ability. The student demonstrates knowledge of the relevant course material, but does not demonstrate knowledge of its larger context, importance/significance, or the ability to apply it to new problems. (Lose one extra point.)

S : Successful. The assignment was submitted on time and fulfilled the objectives. The quality of the assignment was high (beyond merely marginal). The student not only understands the thought processes required to complete the assignment but demonstrates appropriate engagement with the assignment. The student not only demonstrates knowledge of the course material, but understands its larger context and importance, and how it could be applied/extended.

S+ : Excellent. The assignment was submitted on time and exceeded the objectives. The quality of the assignment was exemplary, to the extent that it could be used as a model for others. The student demonstrates mastery of the thought processes required to complete the assignment, and understands how the assignment could be improved or extended. The student demonstrates knowledge of the course material, understands its larger context and importance, and can apply the material. Moreover, the student has engaged with the topic independently, and evinces a nuanced understanding of the material. (Gain one extra point.)

Extra Points

  • You lose extra points by receiving an S- on an assignment (minus one point), asking for a one-week extension on an assignment (minus one point), or redoing an assignment (minus two points). To accommodate your busy schedules, when you make your GitHub account and send me your username by a private message in MSTeams, you will get 4 extra points.

  • You gain extra points by receiving an S+ on an assignment (plus one point). You can also gain extra points by working example problems. These example problems should be written beautifully, at an exemplary S+ level, as Jupyter Notebooks or as Markdown or Markedly Structure Text documents, and submitted as pull requests to the GitHub repository for course content. That way you can help future students with the course material. For each page (~50 lines, but it differs based on difficulty and relevance), you will receive 0.05 extra points. In addition, when you notice typographical errors in the course material, you can correct them and submit a pull request, and will usually receive some extra marks for this. Please consult with me about what sorts of extra credit problems/assignments/examples are most helpful. In general, any contribution you make that improves the course for future generations of students will be rewarded commensurate with the size, and excellence, of the contribution.

Final Mark

The final mark is assigned based on the fraction of the assignments you completed successfully and based on the number of “extra points” you have earned. [To receive an A or a B, you must complete all assignments successfully]{.underline}. To receive an A, you must accumulate at least half of the available “extra points”. Because S+ grades are intended to be rare rewards for exceptional performance, earning an A (let alone an A+) will usually require doing some extra credit work.

Final Mark

\(\frac{\# \text{of unsuccessful assignments}}{\text{total number of assignments}}\)

\(\frac{\# \text{ of extra points}}{\text{total number of assignments}}\)

A+

0

Greater than 1.0

A

0

Between 0.75 and 1.0

A-

0

Between 0.5 and 0.75

B+

0

Between 0.25 and 0.5

B

0

Between -0.25 and 0.25

B-

0

Less than -0.25

C+

Between 0.0 and 0.15

Greater than 0.0

C

Between 0.0 and 0.15

Between -0.5 and 0.0

C-

Between 0.0 and 0.15

Less than -0.5

D+

Between 0.15 and 0.3

Greater than 0.0

D

Between 0.15 and 0.3

Between -0.5 and 0.0

D-

Between 0.15 and 0.3

Less than -0.5

F

Greater than 0.30