Making the Grade
All students know that different professors grade differently. We’d be willing to bet that your experiences in this area mirror ours, and that you’d agree that the differences go much deeper than we could have imagined. It’s not just a question of one professor being a tough grader while the other is easy, or one grading on established intervals (93–100 = A, 90–92 = A–, 87–89 = B–, 83– 86 = B, etc.) while the other grades “on the curve.” It’s more a question of style: how much feedback students receive on their work and in what form, how the various components in any individual assignment are evaluated to make up an overall grade for that assignment, and how final grades are computed, especially if the computing involves grades of differing weights. There also appear to be quite different “cultures” concerning grades in the sciences and the arts. While students in all disciplines are understandably concerned about their grades—if for no other reason than to maintain the grade point average (GPA) required to keep their financial aid or to avoid a clash with parents—those in the sciences seem to desire more precise accounting than those in the arts. Add to these factors that students in an interdisciplinary course are, by definition, constantly being put into situations outside their comfort zone, and it’s easy to see that the wave of students complaining about grading can be a tsunami waiting to happen. One can discuss grading philosophies forever, but we begin from the premise that if students put reasonable amounts of effort into the coursework, they should get “paid” with a decent grade, which we consider to be B– or better. In fact, one of us believes that all students walk in with an A and it’s the student’s responsibility to maintain that grade. This is especially true in an experimental course such as our Sound Thinking, where we know that some of the assignments and our expectations of students’ work may come across as a bit “fuzzy.”
Keywords: Visual Basic macros, blog posts/reflective journal, chunking and connecting chunks, key structure (tonic), patterns, reflective journal, blog posts, risk taking, schedules, subjective evaluation, variables
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