Initial Publication Date: October 22, 2013

Assessment of the effectiveness of teaching with data starts with its goals. The why teach with data section of this module lays out six goals including better content retention, more developed problem-solving skill, more sophisticated quantitative reasoning, deeper understanding of models, a firmer grasp on research ethics, and a more positive attitude toward science and research. The goals you have for your assignment(s) and course may differ from these. Whatever they are, use your goals to focus your assessment.

Assignment Assessment/Grading

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.

Click here to learn about rubric design in detail.

Course Assessment

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.