Honors Analytics: Science, An Interdisciplinary Lab-Based Course on Visual Perception

Interdisciplinarity has consistently been a hallmark of honors courses, particularly in the arts, humanities, and social sciences. Such an approach has been less universal in honors courses in the natural sciences, particularly in laboratory- based courses (Ramaley). We believe that a mark of success of any such course is the degree to which it moves from multidisciplinary to interdisciplinary. Moreover, if the course fulfills a general education requirement in science, it needs to include exposure to the scope of scientific investigation, the techniques of science, and the nature of the scientific process. At Belmont University, we created and for ten years offered Honors Analytics: Science as part of an interdisciplinary, alternative general education curriculum for students in the honors program. This four-credit-hour science course, which had no math or science prerequisites, included a two-hour lab component and serves as the only science course in the curriculum. Students in all majors took the course, even science majors, typically in their junior year. While practical impediments arose after ten years that precluded our continuing to teach the course, it had been a highly successful solution to the challenge of offering interdisciplinary science courses in honors. We provide an account of the course here as a potential model for other honors programs. Honors courses at Belmont for a long time used a “professor pool” model, in which a faculty member coordinated a course and was allotted a budget to bring in faculty with expertise in other areas. In the Analytics courses, the coordinator was initially a mathematician who also had a background in physics. At a later date the coordination was assigned to a biologist who had an undergraduate major in chemistry. Later yet, a psychologist became the coordinator. As a result, we can provide disciplinary perspectives on the content and pedagogy of the course, including laboratory exercises, along with our collective views on the practical details of the model, the strengths of the approaches we employed, shortcomings we perceived, and suggestions for how the course could be improved by other institutions