Abstract:
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In her book Specifications Grading: Restoring Rigor, Motivating Students, and Saving Faculty Time, Linda Nilson makes a case that the current grading systems used throughout academia are flawed and actually motivate students to produce unsatisfactory work. Her proposed solution is a system she calls specifications grading. The system ties all assessments to the learning objectives and grades them pass/fail. Nilson promises the approach will reduce faculty workloads and improve student mastery of topics, but there are details in the execution that are not fully resolved in the text. In this roundtable, we will discuss the practical challenges of implementing specifications grading in a statistics course. In particular, which objectives should be mandatory for students exiting the introductory-level course? Which objectives would we consider to be "higher-order thinking"? Is removing partial credit in the best interest of our students? How do we evaluate conceptual vs. computational questions? We also will share any success or failure we have had in our own implementations.
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