An overall assignment grade typically reflects a combination of numerous learning objectives. For example, a grade on an introductory economics paper might summarize the student's writing fluency, understanding of the supply and demand model, attention to data sources, and effectiveness of visual data communication. Because of this, it is very difficult to use assignment grades alone to assess the effectiveness of curricular revision. Course grades obviously reflect an even larger number of facets to student performance.
One way to solve this problem is to use a grading rubric that includes multiple dimensions. That way, you can target your assessment focus to the areas of student work that you are targeting for change. You can look at these two rubrics for inspiration (courtesy of John Bean, Seattle University):
Note that by revising the bullet-pointed sub-criteria in each section (see the left-most column) he can quickly adapt these rubrics to many new assignments.
Several instruments have been developed to assess learning in the context of student research. While student research is but one manifestation of teaching with data and these tools are explicitly linked to the natural sciences, these student surveys may nonetheless be useful:
- The Survey of Undergraduate Research Experiences (SURE) and Classroom Undergraduate Research Experience (CURE) were developed by David Lopatto (Grinnell College) and collaborators. The instruments measure student attitudes toward science, intentions to persist in the sciences, and self-perceived gains in skills directly and indirectly related to working with data.
- The slightly lengthier Undergraduate Research Student Self-Assessment (URSSA) is designed to assess student research experiences. As a result, it may not fit well in the context of courses. But it may still jumpstart your thinking on assessment. (The URSSA also allows you to add questions to the instrument.) Like the CURE and SURE, it is specific to the natural-sciences and speaks to only one manifestation of teaching with data. Like the CURE and the SURE, it assesses attitudes, potential persistence, and research aptitudes.